Advantages And Differences Of Azure Synapse Analytics With SQL Database

The Microsoft Azure data warehouse is growing fast. In today’s data warehouse architectures, a data warehouse is a central repository of consolidated data from one or more sources, storing current and historical data. This data can be used for reporting and analysis. In most cases, using Azure Synapse Analytics and Azure SQL Database has proven to be the right choice for a data warehouse. This article will help you find the right technology.

Azure-Synapse-Analytics

Azure Synapse Analytics is a cloud platform as a service (PaaS) that offers the Azure platform to provide complete on-demand analytics services or customized resources without servers. The main components are Synapse SQL Tank, Spark, Synapse Pipelines and Studio Apps. This article will focus on Synapse SQL Tank, which refers to the generic Azure Synapse resource for data warehouses (OLAP). Azure Synapse SQL Pool is designed as a massively parallel processing (MPP) system with a scalable architecture that distributes data processing across multiple nodes.

On the other hand, Azure SQL Database is a fully managed PaaS data engine that supports most database management functions and is particularly suited for OLTP workloads based on multiple symmetric processing. Azure SQL DB offers deployment options such as standalone databases, elastic pools and managed instances. This article describes the deployment options of Azure SQL DB compared to Azure Synapse.

What Is Azure Synapse Analytics?

Azure Synapse Analytics, formerly Azure SQL Data Warehouse, has evolved into a borderless analytics service that combines enterprise data warehouses and big data analytics. Azure Synapse combines these two worlds into a single environment that enables data collection, preparation, management and presentation for business intelligence and machine learning.

Azure Synapse vs SQL Database

Workload Types

Azure Synapse is ideal for OLAP workloads with clearly defined read and write tasks. This approach accelerates large workloads and complex queries by decoupling and parallelizing complex tasks. In this case, data is usually stored in a denormalized form using a schema.

Due to a large number of short reads and low data load, Azure SQL Database can perform these tasks more efficiently. This is also true for normalized data stored in multiple tables.

Scaling As A Function Of Demand And Cost

Azure Database PaaS allows you to scale service levels according to workload needs. With SQL Common Compute and linear scaling per storage unit, Azure Synapse provides more granular data processing for critical operations such as complex aggregations, serialization, and large amounts of data. Computation can be interrupted even when there is no query in the dataset, significantly reducing computational overhead.

Azure SQL DB consists of a service layer that ensures that data is processed correctly. With a simple data warehouse query model and low overhead, Azure SQL DB provides an easily maintainable data warehouse with an estimated cost model.

Synapse Interval, Backup And Replication

The Azure Synapse solution allows storing data in a snapshot format. Azure Synapse can be used to recover data for business continuity and disaster recovery. This is useful when you create a copy of a database for testing or deploying a built-in option for automatic and customized recovery over a specified time. An eight-hour recovery point objective (RPO) is currently supported, and snapshots of the last seven days are available in Azure Core. At this stage, geodata can be backed up daily.

Azure SQL DB also supports active geographic replication (Azure Synapse relies primarily on storage replication and does not synchronize with the core server).

Advantages-Of-Azure-Synapse-Analytics

Benefits Of Using Azure Synapse Analytics

Powerful Insight

The deep integration of Power BI and Azure Machine Learning extends the ability to discover insights from any data and apply machine learning models to any intelligent application. This significantly shortens the time to value.

Unmatched Security

Azure Synapse software offers the most advanced security and privacy features on the market. These features are built into Azure Synapse and include automatic threat detection, strong data encryption and granular access control.

Flexibility

Azure Storage is highly resilient because the computer and storage components are separate. Computing can grow on its own. Resources can be added and removed during query execution.

Integrated Power Bi Application

The Power BI workspace integrates directly into the Synapse application. Synapse Studio provides access to reports and databases and makes it easy to create new databases and reports from data processed in Synapse Azure. 

In addition, SQL Serverless looks like a traditional SQL database, making it easy to perform advanced analytical queries during import. Power BI used to be aimed at business users, but with these changes, it has been moved into the hands of data scientists. This is a logical and highly recommended move.

Explore The Data Lake

Some file formats are not easy to analyze, so additional tools are needed. For example, highly compressed Parquet files are great for archiving but are difficult to read. In Synapse, you can right-click on a file and open it using SQL.

Allocating Resources

In Azure Synapse Data Warehouse, resource allocation is measured in Data Warehouse Units (DWU). This measures the critical resources allocated to the SQL data warehouse, such as CPU, memory, and IOPS; increasing the number of DWUs improves resources and performance.

Redundant Storage

Synapse Data Warehouse stores all data in redundant volumes on the local server and Azure Premium. Multiple copies of synchronized data are stored in the local data center, allowing transparent backup in the event of a local failure. Synapse Data Warehouse also uses snapshots stored in Azure to perform regular automatic backups of active (non-offline) databases.

PolyBase

Azure Synapse Data Warehouse and PolyBase provide users with a unique ability to move data across the ecosystem and create advanced hybrid scenarios using native and non-relational data sources.

Summary

Azure SQL Database is ideal for data warehouses with small data volumes and low workloads. Azure Synapse and SQL Pool can handle large amounts of data for more complex data warehouses.

Azure SQL Data Warehouse and Azure Synapse are variants of Microsoft’s Azure PaaS platform, but their original purpose is slightly different. Azure SQL DB is designed for OLAP workloads. However, this does not mean that Azure Synapse is required for data warehouses. With Existbi’s Azure and BI Consulting, we can help you choose the right solution. 


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Reasons To Leverage Azure Synapse Analytics – The Next Generation SQL Data Warehouse

Decision support systems have a long tradition in the business world. Companies have been using analytics to get actionable data since the 1960s. The aim is to support managers in strategically managing business processes through data-driven reports, models and forecasts. Through Azure Synapse Analytics, Microsoft offers analytics services that combine the benefits of data warehouses and big data analytics.

Azure Synapse Analytics

The terms MIS (Management Information System), and DSS (Decision Support System) refer to analytical information systems that perform this function but do not distinguish between them. At the same time, BI (Business Intelligence) is a generic term that has been used since the 1990s for business applications and related product marketing.

Today, the data infrastructure for BI decision support systems is usually a central data warehouse. It provides an overview of reference architectures for information systems, the leading providers of data warehouse solutions, and free and open-source options.

Azure Synapse Analytics – The Next Generation Of SQL Data Warehouse

With Azure Synapse Analytics, Microsoft offers the successor to Azure SQL Data Warehouse. With this new service, Microsoft aims to extend its modern data warehouse strategy and enable companies to analyze large data sets more efficiently and quickly.

The new service version is designed to take data warehouse management to the next level and provide more excellent analytical capabilities.

Another benefit of Azure Synapse Analytics is its scalability. External systems can view and analyze almost unlimited amounts of data in real-time. This can be stored in external data warehouses or extensive data systems. Azure analytics can also connect local data centers.

Machine Learning And Advanced Data Protection

Machine learning models can be used in Azure Synapse Analytics. They can be integrated directly into the data warehouse for real-time data analysis, and the Spark engine is integrated into Azure Synapse Analytics.

Microsoft also added privacy features that allow you to analyze individual columns and rows with additional security and permission settings. Dynamic shutdown and persistent data encryption are also possible. Azure Synapse Analytics can also be configured to authenticate with Azure Active Directory.

In addition to data protection, data sharing is also essential. For example, Azure Data Share can be used to share data securely and efficiently between Azure services. Azure Data Share works directly with Azure Synapse Analytics. Data can be transferred from the Azure software user interface. Subscription data sharing is also possible. In this case, for example, Azure Synapse Analytics works with Office 365 and Dynamics 365. Any SaaS service that supports open data initiatives can be integrated.

You Can Also Create SQL Queries

You can send data to Azure Synapse Analytics using SQL so that you can analyze both relational and non-relational data. Microsoft claims that petabytes of data can be interpreted in seconds. Synapse Analytics also works with Power BI and Azure Machine Learning in this context. Power BI features integrate directly with Azure Synapse Analytics, including multiple data sources that can be combined with Power BI. Azure Synapse Analytics is also available with Common Data Services (CDS) and Power BI AI capabilities.

Azure Synapse Analytics supports T-SQL and other languages for analysis or interaction with external systems. For example, Python, Scala, Spark and of course .NET. Azure Synapse Analytics includes Azure Data Factory. Here you can graphically connect data sources and visualize data flows. It’s a graphical ETL tool directly within the Synapse environment.

Data Preparation And Visualization With Azure Synapse Analytics Studio

Microsoft introduced Azure Synapse Analytics Studio, an application that presents data in an engaging way for users. It is a centralized management tool that allows control of almost all known analytics functions in the Azure SQL data warehouse.

For example, you can create dashboards and workspaces to manage and prepare data for analysis directly. Workspaces allow data scientists to collect data streams and view all data without code. For example, Azure Synapse Analytics does not require a direct query to a database or data warehouse to access data. New functionality can be added on-demand or integrated directly into Spark Engine.

This workspace can be used by data scientists, business analysts, database administrators and developers who want to prepare and analyze data. You can import datasets into Power BI and prepare them for end users. You can do everything you need in the graphical user interface of Azure Synapse Analytics Studio. This allows you to quickly and easily analyze all relevant sources through a central interface.

What Can You Expect From Our Azure Data Warehouse Consulting Services?

Azure Synapse Analytics consulting and training are delivered in line with Existb’s approach for other Microsoft products. Customers who primarily use data warehouses as part of their services and have previously used Azure SQL Data Warehouse can now expect significant improvements in Azure Synapse Analytics.

Sum It Up

Medium and large enterprises increasingly use data warehouses. The business intelligence and data warehousing market offers businesses a wide range of promising open-source models and cost-effective solutions. For SMEs in particular, this reduces the financial barriers associated with the old world of big data analytics.

Medium-sized users focus on reporting when deploying BI solutions. Enterprises gain initial value by collecting data at a reasonable cost. If the assessment shows gaps in the database, the next step is to set up data collection using ETL or OLAP tools. The integration of data warehouse architecture and the proper IT infrastructure is complemented by data mining tools that can highlight emerging trends and correlations and provide essential insights for strategic decision-making through further analysis.

Medium-sized businesses considering a data warehouse should ensure that they have an appropriate business intelligence strategy in place from the outset that is compliant with data protection requirements.


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13 Types Of Industries Benefiting From Data Warehouses

A data warehouse can be defined as a set of tools and methods used in different industries to store, analyze and access information.

Organizations use different types of technologies to store information and make it available to users, such as management software, scanners and cloud based systems.

Benefits of Data Warehouse

Today, there is hardly any organization that does not use some form of data warehouse. From Google drive to cloud management software, the service is present in small, medium and large enterprises.

As we have already explained, almost every company can now benefit from data warehouse. Here are some examples. Take a look.

1. Ecommerce

Ecommerce has grown significantly in recent years, mainly due to the restrictions imposed by the Covid-19 epidemic. People are increasingly used to shopping online, in which data plays an important role.


Data warehouse is very important for those responsible for e-commerce. Information provided by customers can be used to improve marketing strategies, for example by making them more targeting and personalized.

2. Retailers

Data Warehouses are often used by retailers such as clothing stores, supermarkets, pet shops and pharmacies, among others.

In these businesses, data warehouse systems can be used to update stock levels, identify which products are selling best to avoid stock-outs, etc.

Not to mention customer data, which can be used to create personalized shopping experiences and marketing and PR strategies.

3. Industry

Industries in many different segments can use data warehouses for their business. They can be used to store customer data, track orders, ensure necessary production, etc.

In addition, prototypes and product designs can be stored according to advanced security protocols. This prevents them from falling into hands of competitors.

4. Hospitals

In the healthcare sector, data warehouses are also very useful to optimize processes. Among other things they can be used to capture patient data, making the work of doctors, nurses and other professionals easier.

As patient data is stored in the cloud, nurses can easily access medical records and see, for example, which medications doctors have prescribed for each user.

In addition, such measures make it easier for the administration to keep track of everything and for health insurers to charge the right costs.

Benefits of Data Warehouse

5. Educational Institutions

Educational institutions such as schools, colleges and universities often use data warehouse systems.

In education, teachers can use this software to stream video lesions, e-books, podcasts and other distance learning materials.

It can be used to digitally receive students homework, track class attendance, assign grades, etc.

6. Agriculture

It’s probably not farmers who think about big data first. But data warehouse is now critical to agriculture, and will become even more so as weather forecasting and soil productivity improvements become essential to feeding a growing world population.

7. Property And Land Management

Real estate companies use big data to better analysis trends and better understand their clients and markets.

Similarly, property management companies use data collected from building systems to optimize operations, identify problem areas and improve maintenance processes.

8. Insurance

In the insurance sector, data warehouses are essential for maintaining and analyzing existing customer records to identify customer trends and take further action on business.

9. Finance

The use of data warehouses in the financial sector is the same as in the banking sector. The right solution will help the financial sector analyze customer spent and develop more effective strategies to maximize profits for both parties.

10. Services

In the service sector, data warehouses are used to manage customer data, financial data and resources to analyze patterns and make decisions for positive outcomes.

11. Transportation

With powerful data warehouse solutions, transport companies can collect all their location data under one roof to anticipate market changes, analyze current passenger behavior, track demand for transport services and ultimately make effective decisions.

12. Production And Natural Resources

In the natural resources sector, data warehouse enable predictive modeling to support decision making by extracting and integrating large amounts of spatial, graphical, textual and temporal data. Applications include seismic interpretation and reservoir characterization.

Data warehouses are also used to solve continuous production problems and gain competitive advantage.

13. Banking

With a great data warehouse solution, bankers can manage all available resources more efficiently. They can better analyze customer data, government regulations, and market trends to make better decisions.

These are just a few examples of companies that benefit from data warehouses. Many companies can rely on this technology to streamline and optimize their operations.

We have to remember that we live in a world of “offices everywhere”, which means that work is done everywhere.  This is why data warehouse software is booming and businesses are using it more and more.  

How Existbi’s Data Warehouse Consulting Can Benefit Your Business?

Existbi can help you improve your brand’s presence in the digital competitive landscape. We provide your business with a dedicated data warehouse solutions that eliminates the complexity of data and delivers insights that help your business grow.

If your company struggling to manage large volumes of data, making it difficult to consolidate data, there’s no better solution then cloud data warehouse technology.

If your company falls any of the areas mentioned above, data warehouse consulting is a good investment.

With Existbi’s data warehouse training, you can bring all your data under one roof become a truly data driven business. Our Cloud, Hybrid and On-premise Data Warehouse Solutions help you collect data from multiple sources, transform it into a manageable format and upload it to your data warehouse. This will give you a clear picture of your business processes and the market in which your business operate, and help you make better decisions.


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How To Build And Automate Data Warehouse Migration Into Cloud?

The data warehouse is an essential technology for the development of business intelligence solutions. In this blog, we are going to highlight how you can build and automate the whole Data Warehouse Migration process to the cloud in a step-by-step format.

data-warehouse-migration

Data Warehouse is a unified and centralized data warehouse system that allows easy access to stored information.

DW allows for fast response and storage of large amounts of data, mainly due to its multi-dimensional architecture based modelling. However, to set up this robust data warehouse, we need to follow a few steps that will help us to set it up correctly.

Let’s look at the seven steps to creating a data warehouse:

1. Needs Analysis

First, we need to create an overview of all the information that the user wants. At this first point, we cross-check the dimensions and facts required to achieve the managers’ goals. At this first point, we are concerned with what the data warehouse will contain, not how, so we should not be concerned with the actual existence of the data but with what we want.

2. Data Mapping

In this step, we map the data by identifying the source and its path. This is where we check the feasibility of what we wanted in the first step, i.e. whether there are data that meet the desired requirements.

3. Designing The Storage Area

After mapping, we create a storage area structure, which is the data transfer area. In this area, data is replicated and separated from operational systems (OLTP (online transactional processing)) and prepared appropriately in event and dimension tables for future workloads.

4. Construction Of Dimensions

In this step, the structure of the dimensions part of DW is created. We also define the data history in the dimensions.

5. Create Events

In this step (after creating the dimensions), we design the event structures. Here we evaluate and define the details of the information to be stored in each event. We also assess the usage and storage requirements to be met.

6. Define The Overall Load Process

After completing the previous steps, we need to configure the engine so that everything can be automatically and seamlessly uploaded, updated, and processed. Therefore, the need for the general process of loading is the “brain” of Data Warehouse.

7. Create Metadata

Finally, we need to create all the metadata documentation, including the creation process and the data dictionary. Metadata is an essential support for knowledge management.

Remember that Data Mart is the division of data warehouse into subsets of information organized by specific subjects. Therefore, all these steps, except “needs assessment” (which should preferably be performed once), should be repeated with each new Data Mart created.

It is essential to follow the sequence of these steps, as they are interdependent. In other words, the next step can only start after the previous step has been completed.

A DW construction project’s success is almost guaranteed if all of these activities are given due attention. In this way, we will effectively have a data warehouse that will store the information to assist the organization in decision making.

How Can Automation Simplify Data Warehouse Migration Into Cloud?

Moving your data warehouse to the cloud is an important step for companies moving parts of their infrastructure to the cloud. It is usually complex and costly. Automation can simplify the process.

The long-term goal of many companies in their digital transformation is to use cloud technologies. However, a closer look at cloud usage data shows that many companies are still a long way from moving part of their infrastructure to the cloud.

Rather than addressing the long-term goal of the cloud, these companies often rely on temporary solutions or shortcuts. Rather than migrating important parts of the infrastructure first, they choose the easiest parts to migrate.

Should The Data Warehouse Be Migrated To The Cloud First?

When it comes to deciding which infrastructure elements are worth migrating to the cloud, the data warehouse rarely tops the list.

data warehouse migration to the cloud

However, the business case for adopting a cloud data warehouse strategy is already very compelling. Most companies can gain an advantage over their competitors by leveraging it well and extracting value from it.

In principle, this is much easier if you have a flexible and easily scalable on-premises data warehouse platform. Therefore, it would be desirable for many companies to move data storage to the cloud. However, many companies are putting this project on hold because the task itself is not straightforward.

Moving Traditional Data Warehousing To The Cloud Is Cumbersome

Traditionally, the migration of an entire data warehouse has been entrusted to an entire development team with the time and fault-tolerance to move the data warehouse from a fixed infrastructure to a cloud structure.

Such a lengthy, complex and costly process usually involves the manual migration of various parts of the data infrastructure to the cloud, which is eventually transformed into a hybrid environment.

This process is sometimes frustrating for decision-makers and creates a mental barrier to choosing cloud storage. Many companies recognize the value of moving to cloud storage, but obstacles hamper them.

Automation Can Simplify The Transition From Data Warehousing To The Cloud

The manual journey to the cloud typically consists of repetitive and time-consuming tasks.

Developers must create their solutions for each piece of infrastructure, which means long hours, lengthy implementation, and a general lack of standardization. Automation can significantly reduce these consequences by designing migration processes in a standardized way.

Automation can, therefore, “simplify” the migration process, reduce the cost of the migration project and help avoid migration errors.

All you need is a data warehouse solution that can automate data processing operations. The migration project will gradually migrate these processes with each automated process in the future.

This kills two birds with one stone: the company will now use automated processes that require almost no manual intervention, while the team will simply migrate its data warehouse to the cloud step by step.

Wrap Up

There is no single migration plan that covers all use cases. We recommend consulting your cloud data warehouse service provider, explaining what your environment is like and asking them for detailed guidance rather than trying to automate the migration process to the cloud yourself.


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7 Good Things Business Intelligence Can Do For Your Business

Many entrepreneurs have enriched themselves by following their intuition.

Intuition is the ability of an entrepreneur to associate, evaluate and process a current scenario by unconsciously recalling a similar event from the past. However, to get the most out of intuition, some prior experience is needed.

Business intelligence enables small, medium and large enterprises to harness the power of big data by analyzing data and developing trends and solutions.

In this article we look at how business intelligence contributes to business growth.

What Is Business Intelligence?

Business Intelligence (BI) is a combination of tools, technologies, applications and practices that help organizations collect, integrate, analyze and transform raw data into relevant and actionable business insights. BI consists of

  • Data Collection
  • Analytical Processing
  • Queries and Reports

What Is The Purpose Of Business Intelligence?

The main purpose of business intelligence in an organization is to help executives, managers and other business leaders make better business decisions based on data. Many companies use BI to reduce costs, identify better business opportunities and track inefficient business processes.

What Are The Benefits Of Business Intelligence In Business?

The main benefits of business intelligence stem directly from its purpose in today’s business environment. Business intelligence helps:

  • Speed up the decision-making process
  • Optimize internal business processes
  • Increase business efficiency
  • Increase turnover
  • Gain competitive advantage
  • Identify market trends
  • Identify business problems that can be solved

A major problem in today’s business environment is that entrepreneurs often confuse business intelligence with business analytics. Entrepreneurs need to understand that the essence of BI is reporting, not process management. Business intelligence has the potential to transform businesses, but it is not used because business owners are not aware of it.

  • What is BI?
  • Where do I start?
  • How long does it take to see results?

Let’s look at the components of business intelligence and the way it can help you transform your business processes for success.

1. Intelligent Decision-Making

For an entrepreneur or manager, it is important to have tight control over business data. Information is usually not the same as intelligence, especially when it is enterprise-wide.

The sole purpose of business intelligence is to organize and analyze business data. Business intelligence helps companies to make strategic decisions. A system that keeps business data in one place and up-to-date enables better business decisions and improved financial performance.

An intelligent customer relationship management (CRM) solution in sales plays a key role in bridging the gap between managers and employees. We close the gap with a system that provides a number of key business metrics, including

  • Productivity
  • Team performance
  • Customer service performance
  • Sales cycles
  • Customer behavior 
  • Loyal customers
  • Market trends

For each of these key indicators, separate data sets are collected in the CRM sales system. CRM then analyzes this data at a larger scale using the reporting function. Once analyzed, the CRM system provides data in the form of facts and figures that can be used by management to identify any discrepancies. In short, the entrepreneur does not rely on intuition, but makes decisions based on the concrete facts provided by the CRM system.

2. Better Customer Service

Better customer service is about delivering a great customer experience. Depending on the level of customer satisfaction, your business can succeed or fail.

If you leave a good impression on customers, you will encourage them to buy from you in the future. Eight percent of existing customers can generate up to 40 percent of your company’s total turnover.

Business intelligence can filter and collect data from repeat customers. Based on the data you collect, you can easily develop strategies to encourage existing customers to make repeat purchases. Business intelligence helps entrepreneurs deliver a data-driven customer experience that helps them stay competitive in the business world.

3. Better Customer Insights

Customers are less receptive to what you want to sell them. They change with market dynamics and want solutions to their problems. The journey from initial interest in a product or service to the moment of purchase has changed significantly in recent years. Simply put, customer engagement is more important than promotional activities.

The demand for integrated business intelligence tools is growing. Tools such as CRM help users understand how customers interact with them in real time. CRM software allows users to find the best way to reach customers based on accurate data.

CRM solutions are important tools for gathering the customer information needed for a business to adapt to the new era of the customer journey.

In the previous chapters we have already stressed the need to collect data from all parts of the business, including

  • Sales
  • Marketing
  • Customer service
  • Operations
  • Product development
  • Finance

In this way, business intelligence can be used to create a more comprehensive customer profile based on their interactions with the company and convert them into paying customers.

Business intelligence helps companies improve their understanding of their customers so they can take steps to improve the customer experience. Today, every business aims to achieve the highest possible levels of customer satisfaction and loyalty.

Business intelligence helps you connect all customer touch points and instantly access individual customer feedback, current service issues, purchase history and current position in the sales cycle.

With so much detailed data at your fingertips, segmenting customers by their journey is easy. Based on this data, you can develop customized customer service strategies for different customer groups. This way, business owners can best allocate resources to achieve growth targets while retaining their current customer base.

4. Increased Productivity

Business intelligence can help businesses:

  • Eliminate bottlenecks
  • Streamline business processes
  • Automate daily operations
  • More organized

By successfully implementing business intelligence, companies can provide better customer service and make more productive use of sales staff time. Business data efficiency is improved at the executive level with automated reports and intuitive dashboards. Track all contacts and transaction information with just a few clicks.

By aggregating data, customer data is available to senior management from any device via the cloud, reducing management time. Employees on the move no longer need to call the office for information. Simply enter daily updates into the app and all information will be up-to-date, without additional manual work, ensuring enterprise data integration.

5. Better ROI

One consequence of the above is an improved return on investment for the company. ROI is a priority for all businesses as they can quickly focus on achieving greater revenue and growth. CRM systems with integrated business intelligence help businesses improve their day-to-day operations.These includes

  • Sales effectiveness
  • Conversion rate of closed sales
  • Customer experience

These systems help businesses analyze large amounts of data without spending a lot of time and help them develop future growth strategies. With the insight and discipline provided by business intelligence software, companies can easily use the improved information to drive day-to-day sales and customer service. They can also avoid unnecessary assumptions and biases that typically lead businesses down the wrong path.

6. Planning A Better Future For Business

By investing in the best business intelligence software and professionals skilled in the use of these tools, businesses can improve their ability to predict market trends and customer buying habits.

By understanding consumer buying behavior, companies can develop a plan based on a comparison of purchase history and business intelligence tool prediction. Not only do they know their customers better, but they also make the best use of their resources.

7. Turn Data Into Actionable Insights

Today, big data played a big role in understanding how consumers think, search for information, buy and move. The pace of data creation will accelerate even more in the future. The main reason for this is the rise of social media channels. The number of posts published, photos and videos uploaded, tweets sent, etc. will increase the flow of data in the digital world.

The company that takes the first step and integrates its official social media channel with business intelligence software will benefit from these growing trends, stay connected with its customers and gain an edge in customer service.

Business intelligence allows you to make sense of these petabytes of data. The data collected can be easily transformed into useful insights that give businesses the competitive advantage they need.

Wrap Up

It is a myth that business intelligence is so expensive that only large companies can use it. This is not true. Today, business intelligence solution providers are working with small and medium-sized businesses to enable them to harness the power of business intelligence. BI is more accessible to SMEs today than ever before.


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How Different Are Data Science And Machine Learning In Practice?

Data science is a discipline that aims to draw meaningful conclusions from data using a scientific approach. Machine learning, in turn, is a set of techniques used by data scientists to enable computers to learn from data. In a nutshell, data science and machine learning are a way of combining science, statistics and computers together.

data-science-and-machine-learning

Machine learning is an artificial intelligence area with economic, social, ethical, and technical implications. The science of computer algorithms allows programs to improve automatically as they gain experience. One way to achieve artificial intelligence is through machine learning. Machine learning involves working with small and large datasets, analyzing and comparing data to find common patterns and explore nuances.

What Is Data Science?

By definition, data science is the process of extracting information from data collected from a variety of different sources. Today, televisions, refrigerators, cars, lighting systems, etc., can generate data and thus provide valuable information. Data science uses various techniques to analyze and interpret large amounts of data, such as predictive modeling and machine learning algorithms.

What Is Machine Learning?

Machine learning is a complex field with many different dimensions. Sometimes even technical experts find it hard to imagine the entire world of machine learning and its place in business. However, many are now interested in ML and delve deep into the subject.

For them, it is also essential to understand the structure of machine learning. As a field of artificial intelligence and computer science, machine learning uses data and algorithms to learn and evolve from experience without being directly programmed.

differences-of-machine-learning-and-data-science

What Is The Difference Between Data Science And Machine Learning?

Since data science is a broad concept covering many fields, machine learning belongs to data science. Machine learning uses different techniques such as regression and supervised clustering. However, in data science, “data” may or may not come from a machine or a mechanical process.

Data science is more advanced than machine learning. Data in data science is not necessarily the result of a mechanical process. Data can be processed manually and usually has little to do with learning.

data-science-vs-machine-learning

On the other hand, machine learning is a field of artificial intelligence, a subfield of computer science and data science.

Data science is the process of extracting valuable information from data. It is a broad discipline that encompasses skills such as statistics, mathematics, programming, computer science and business, as well as techniques and theories such as predictive analytics, data mining and visualization. 

What Is The Purpose Of Data Science?

The main goal of data science is to capture and interpret data effectively and present it in simple, non-technical language for end-users and decision-makers.

The second goal is to produce useful information and transform it into data-driven products.

What Is The Difference Between Data Science, Computer Science And Statistics?

Data science is the application of automated methods (computing) to analyze large amounts of data (statistics) and extract knowledge from it (business).

Data science is the study and analysis of all available structured and unstructured data to gain understanding and knowledge and design actions that lead to better results.

An Example Of Data Science For Problem Solving And Value Creation

It all starts with a business problem to solve. The process of using data science to solve a problem is as follows:

Business Problem –

Customers cancel their banking packages every 2-3 months after signing a contract.

Data Analysis

Data collected and analyzed concluded that customers are leaving their service packages as their debt increases.

Solutions

Based on the data, the management decided to take a proactive approach toward customers in the same group with similar characteristics.

Action

The company introduced a financial counseling program and developed applications to provide specific financial solutions to customers, which reduced over billing with increased turnover and profits.

difference-between-data-science-and-machine-learning

Where Is Machine Learning Used In Data Science?

The use of machine learning in data science can start in the data science development process or life-cycle. The different phases of the data science life cycle are:

Business Requirements

in this phase, we try to understand the requirements of the business problem to which we want to apply the system. Suppose we want to develop a recommendation system to increase sales.

Data Collection

We collect the data needed to solve the problem in this phase. We can use user ratings, reviews, purchase history, etc., for different products for the recommendation system.

Data Processing

in this phase, the raw data obtained in the previous stage is transformed into a suitable format for easy use in the following steps.

Data Mining

This phase involves understanding the patterns in the data and trying to draw valid conclusions from them.

Modeling

Modeling the data is the stage where machine learning algorithms are applied. Therefore, this phase includes the entire machine learning process. The machine learning process provides data ingestion, data cleaning, model building, model training, model testing and model performance improvement.

Application And Optimization

It is the final stage where the model is applied to the actual project and verified its performance.

How Do We Choose Between Data Science And Machine Learning?

No other choice. Data science and machine learning go hand in hand. Machines can’t learn without data, and data science is best implemented through machine learning, as explained above. Future data scientists will need to have a basic understanding of machine learning to model and interpret the vast amount of data that accumulates every day.

Conclusion

Data science, machine learning and artificial intelligence are changing the world. That’s why data science education can be an intelligent choice.

Soon, machines will replace functions performed by humans, and those who know how to work with these technologies will undoubtedly play an important role.

One of the best ways to keep up with these changes and learn how to operate machines is to become a data science expert.

Looking For A Data Science Consultancy?

Data science is an interdisciplinary field that uses computing power and big data to extract knowledge. Machine learning is currently the most popular data processing technology. Machine learning allows computers to learn on their own from the large amounts of data available.
The use of these technologies is widespread but not unlimited. Data science can be compelling, but it only works when people and data are highly specialized. To find out more, take a look at our data science courses and consultation programs.


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9 Practical Reasons To Use Microsoft Power BI Instead Of Excel

Many companies that decide to implement Power BI for data analysis are sooner or later confronted with the argument that “we don’t need Power BI because we can evaluate everything in Excel”. In reality, Excel offers a lot of possibilities to evaluate data, and in some cases, this may be sufficient. However, when it comes to comparing spreadsheets, reports or data files, Microsoft Power BI is a considerably more powerful tool than Excel. It is easier and more intuitive to use than Excel.

We compared the two tools below to help users decide whether Power BI is right for them. Have a look.

1. The Amount Of Data To Process

Power BI can process large amounts of data that cannot be opened in Excel on a standard computer. You can create analyses and reports from large files and use different data sources in one statement without splitting the files into several similar ones. Power BI also makes it easy to add new data and create relationships between files.

2. Connecting To The Cloud

Power BI provides access to local data and cloud services. The small selection of possible data sources includes Excel, Sharepoint, Azure, Salesforce, Google Analytics, GitHub, etc. Excel cannot provide such a wide range of functionality.

3. Publishing & Sharing

If you want to share a chart created in Excel, you can send it by email or save it to a network drive or SharePoint. With Power BI, you can upload a report to the cloud with a single click, and users can access the updated information.

4. Predictive Forecasting

Predictive forecasting in Power BI uses built-in forecasting models to automatically identify data phases and automatically generate forecasts for the future. It analyzes data and selects the best analyzing algorithm. This forecasting tool allows Power BI users to apply artificial intelligence to their data.

microsoft power bi vs excel

5. Intuitive Usability

Creating charts in Excel can be time-consuming, especially if they need to be customized. In Power BI, you can create and enhance reports with drag-and-drop functionality. Filtering a simple data set can be done quickly with a single click.

6. Security

Some security features can be built in Excel, but they are not as user-friendly or comprehensive as in Power BI. For example, the row-level security (RLS) feature allows users to see only the data they need. Likewise, you can publish reports to specific work spaces so that only users belonging to that workspace can access them.

7. Scheduled Data Update

Power BI (Premium) makes it easy to update your data daily or even hourly. Users benefit from faster and more reliable data updates, which reduces resource consumption.

8. Mobile View

Have you ever opened an Excel file on your smartphone and been confused by the appearance? That’s because Excel is not designed to be viewed on mobile devices. Instead, Power BI offers iOS, Android, and Windows mobile apps that provide easy access to reports and dashboards.

power bi vs excel

9. Interactive Dashboard

Another great benefit is that clicking on any part of a report or dashboard will automatically filter the entire report to include all the data and metrics for that product group. This gives you quick access to more detailed information on a specific part of the report or dashboard.

Conclusion

The comparison shows that Power BI has a significant advantage over Excel. Has Excel become unusable with the introduction of Power BI? Not at all. Excel still offers many valuable, multipurpose features and is one of the most comprehensive programs in the Office family.

Excel is an early Microsoft product, while Power BI was released a few years earlier. I think 95% of Windows users have used Excel at some time. Excel is a well-known product. Power BI is a Microsoft product for data analysis and visualization. Excel and Power BI are almost 80% identical in development time.

Power BI allows the entire data model to be transferred from an Excel report to the Power BI dashboard with a single click. Power BI and Excel have advantages and disadvantages regarding data visualization. Power BI’s benefits lie in its web and visualization features, while Microsoft Excel is for data analysis, mining, and pivot tables.

The best thing about this comparison is that you don’t have to choose one. Excel and Power BI work very well together, mainly if you use Excel for data processing and Power BI for presentations and sharing.

As an Excel user, I’m certainly a fan of all its capabilities, but I wouldn’t hesitate to use Power BI to create my reports and dashboards if needed.

So, if you need to access multiple data sources, if you need to manage large amounts of data, if you want reports to be available only to specific users, and if you are interested in attractive dashboards, don’t hesitate to get in touch with us. We will work with you to develop a concept that meets your needs.


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Step-By-Step Data Governance: It’s Role, Objectives And Importance In Business

The development of information technology has not only changed people’s daily lives, but also the organizational system of companies. They have evolved from a tool to support other sectors to an important factor in strategic business decisions. This makes all decisions in this area increasingly important. Of these, data processing has become one of the most challenging and therefore data governance plays a key role in companies of all sizes.

Data Governance refers to the management of the organization’s data and the access, use and security of information exchanged within the organization. It is the control of processes, people, objectives and their achievement. Control of information shared and communicated by the team is no less important, as everyone acts on it.

Control ensures that data entered by employees or through automated processes meets requirements and standards such as integrity, business rules and objectives, etc.

What Is Governance?

There are many definitions of governance, but the one we use here is broader. Governance  is the explicit and implicit set of decisions and commitments that an institution makes to its customers, partners and society.

In other words, it is about how an organization’s decisions and the consequences of those decisions affects the organization’s goals and the people involved. At first glance, this definition seems very broad and complex. For example, a person running a personal care business. She knows that one of her clients’ concerns is whether the company is engaging in ethical animal practices. Nor do her partners, suppliers, and investors, want to be associated with companies that engage in such practices.

Avoiding animal testing is not only ethical, it is even better when the end user and the partnership are considered. If we take all stakeholders into account in the decision-making process, we will act responsibly. 

Objectives Of Data Governance

Data governance is a decision making system based on a model that describes who acts, when, on the basis of what information, by what methods and under what conditions, and with what results.

  • Better Decision Making: When everything is specified, informed decision-making is more secure.
  • Data Security For Investors: More control leads to greater security.
  • More Efficient Processes: The standards and repeatability provided by data governance make processes simpler and easier.
  • Cost Reduction: Coordinated efforts help reduce cost.
  • Transparency: Every step is clear and transparent.

Step-By-Step Data Governance

A thorough data governance solutions includes governance, clear processes and a well-defined plan. Information is a valuable resource. Protecting corporate and customer data is a growing and increasingly complex challenge. With all employees connected to the network 24 hours a day, it is difficult to control all the information flowing through the company. So let’s look at what steps need to be taken to achieve this.

Identify Who Owns The Information

The first step is to identify who is responsible for each aspect of the data. This person will act as a custodian and may set up a committee to formulate policies and report on progress.

Understanding The Current Situation

It is important to identify where we are now before making any changes. What are the current practices? The evolution of methodology is very important here.

Develop A Strategy

Research on data governance framework suggests that management should develop a strategy for managing the company’s information in the coming years. One of the most common problems with this task is the lack of follow-up. To avoid this, management can start to identify priority areas within the company, such as marketing, to facilitate analysis and monitoring. Areas should be selected according to their ability to deliver positive results quickly and easily.

Optimal Use Of Data

The definition of data is essential for good data management. Make sure that everything is available. Data can be available in different formats, in blocks, separately, sequentially, etc, so it is important to keep it organized and accessible. 

It is also important to calculate the value of the information. You cannot protect and develop something whose value is unknown. This can be difficult because data is intangible and giver governance helps the organization to value its data over time.

Identify Risk And results

Monitoring is the most important part of projects, without it there is no way of knowing whether results have been achieved and what needs to be improved. Organizations are constantly changing and so is the data they hold. Unfortunately, most organizations are only evaluated once a year.

Objectives, Role and Importance of Data Governance

The Importance Of Data Governance In Business

One of the biggest data gaps occurred when companies started to use and rely on data for decision making. This may have become commonplace, but it has meant a fundamental change in the way companies do business.

Previously, most decisions were made on the basis of the individual or collective options of a specific group of people, for example, managers, based on their life experience and personal beliefs. This model of decision making has some specific characteristics:

  • It fails when leaders misunderstand.
  • Leadership is responsible for failure.

However, using data to make decisions changes the process, as these decisions are based on a more accurate understanding of reality and are more likely to be correct in the long run. Data-driven decisions are less influenced by human experience and better informed.

Examples of this methodology have been used by large companies such as Google, Facebook, and Apple, which have achieved excellent results. However, this also means that certain measures need to be taken to ensure that the data is real, complete, secure and accessible.

To ensure this, a number of issues need to be clarified and addressed, including:

  • Who has access to the data?
  • What are the data protection measures?
  • What data should be stored and which data should be deleted?
  • What are the consequences of having data stored?
  • What are the uses of this data in the company?
  • What are the risks associated with this data?

The area of data governance seeks to answer these questions.

Data protection can be defined as the process of accessing, managing, storing and protecting corporate data, taking into account all stakeholders, in order to ensure data integrity, availability and security.

Role Of Data Governance In Legal Protection

If data governance means involving data subjects in the decision-making process, then governments are one of the institutions involved in these processes. They have an interest in ensuring that legal protections and rights are respected both in the digital environment and in the use of customer data.

Some countries have therefore already adopted specific legislation on this issue. One of the most important international instruments in this area is the European Union’s General Data Protection Regulation (GDPR). As the United States of America (USA) does not have a single basic data protection law, hundreds of laws have been enacted at federal and state level to protect the personal data of US citizens. At the federal level, the FTC Act gives the US Federal Trade Commission broad authority to take enforcement actions to protect consumers from unfair or deceptive practices and to enforce federal privacy and data protection laws.

Both aim to protect consumer data from unauthorized use by any organization.

The common and very advanced point of these laws is that the owner of the data is not the one who collects or uses it, but the one who created it. This ownership means they have more power over this data, for example, they know what data the companies hold.

Summary

A good data governance system not only helps your company keep data secure, but also helps you retain customers, reduce costs and seize opportunities. Well-defined processes are a great help to the project and bring many benefits to the company. Find out more about Existbi Data Governance Consulting and learn how to analyze, plan and optimize business processes to make data transparent, accurate and accessible.


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All You Need To Know About Artificial Intelligence In Business And Beyond

Artificial intelligence in business is based on research into the implementation and development of intelligence mechanisms.

Simply put, AI is the creation of tools and machines that can perform various tasks and processes without human intervention.

In other words, these innovative devices are able to automatically and dynamically think, sense, solve problems, process data, and produce and use different products, such as a used electric motor.

Artificial Intelligence In Business

Moreover, artificial intelligence is increasingly present in all processes, both in our private lives and at work.

AI in business raises many questions and debates. Ultimately, AI seems to be very useful for companies to optimize processes and save time. But what about the drawbacks?

To learn more about the merits and demerits of AL, read on this full article.

The Merits of Artificial Intelligence

Problem Solving

The first advantage of artificial intelligence is its ability to solve all of a company’s problems and needs, both operational and administrative.

For example, if a company sells plated filters and is having trouble managing the materials needed to provide the service, AI can help with automated tools that calculate the right processes and inventory levels.

Reduce Errors

We know that in many companies, especially B2B companies, rework and errors are common when executing certain processes. This is why AI is at the heart of revolutionizing and modernizing the way we work.

It allows professionals to reduce their working time and increase its utilization for example for more complex tasks.

Optimizing Communication

Did you know that you can optimize your company’s internal and external communication? Artificial intelligence is also developing tools that enable direct communication between all stakeholders.

Reduce Time

Another benefit of AI is that by optimizing repetitive and bureaucratic tasks, it can significantly reduce the time spent by professionals, allowing companies to plan their digital marketing strategies and investments in a more rational and organized way.

The Demerits Of Artificial Intelligence In Business

Having outlined some of the major AI merits, let’s look at its main disadvantages.

Artificial Intelligence In Business

New Technologies

AI is a relatively new technology that raises a number of ethical and moral questions.

The costs and benefits are also unproven, as its use can have both positive and negative effects.

Occupational Risks Of Artificial Intelligence In Business

Another demerits of AI is that as its use increases, many operational or repetitive tasks may be eliminated.

In other words, thousands of people could lose their jobs because of new technology.

More Specialization Is Needed

For example, if you have a woodworking company and want to implement AI related processes and equipment, you need to be aware that your company will incur costs not only to buy and implement the equipment, but also to hire professionals who are familiar with the new technology.

AI is technological development that is being explored by companies in different sectors.

How Can The Concept Of Artificial Intelligence Be Applied In Business

The aim of developing intelligence software is to improve the performance of professionals, not to replace them in the long term.

One of the most common examples is the hospital scenario. Doctors and surgeons are often assisted by equipment that allows them to perform surgeries and make more accurate diagnoses. But in the hands of highly skilled professionals, these tools offer effective solutions.

In the business world, artificial intelligence made a positive contribution across many industries. Their challenge is to continuously find solutions and ways to automate workflows.

File management now allows you to store files in the cloud, which saves a lot of resources. Applications are now available to check documents and contracts more quickly and translate them if necessary.

AI saves time and allows professionals to focus on strategic aspects without losing sight of day-to-day tasks. In the area of customer relations and product development, there are programs that capture online customer behavior and recommend products to potential customers.

What Role Will AI Play In The Future?

There are also important innovations in artificial intelligence. Car manufacturers are one of the industries most dependent on artificial automation of production lines.

This is not a new phenomenon in mass production. However, a new trend is already emerging, as the AI based robots are not only being used in the production of cars, but the cars themselves are becoming self-driving.

Artificial Intelligence In Business

This is a reality that has the potential to transform the transport sector. Although it may seem futuristic, tests have already begun. A final product could be on the market soon.

The tools needed to automate repetitive industrial tasks are already available today. Such robots are designed to be stealthily controlled and to make decisions according to their programmes.

Robotics in business is therefore an intangible tool in the public sector.

What Are The Current Market Trend?

In an increasingly digitized and computerized market, companies need to adapt to operate efficiently. One new development in this area is the transformation of the enterprise resource planning (ERP) system. Such applications aim to integrate business processes in one place and improve transparency and communication.

Recently, companies have become sophisticated and are using intelligent ERP systems, also known as i-ERP. The new development in it is the focus on capturing business data and transforming this information into reports for decision making.

In this way, marketing activities can be linked to sales performance. The technology is known for its ability to learn through the business use of artificial intelligence programming. 

AI compatible tools are now available that reduce the number of iterations, eliminate human error and automate certain tasks.

Reducing costs and increasing efficiency is a constant concern for managers. By implementing i-ERP systems, companies can focus on customer service, reduce operational costs and improve team productivity.

Artificial intelligence in business is a clear example that the future is closer than it seems. This means that these competitive advantages can now be used to improve business strategies in all sectors of the economy.

Artificial intelligence is now a very important topic. Would you like to invest in this new technology? Do you Have a AI/Machine Learning project in mind? Please contact our experts and see how we can make it work right for you!


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The 5 Key Predictive Analytics Models You Need To Know

Today’s businesses work with endless amounts of data and need to make informed decisions based on it. In doing so, employees often face barriers in collecting and evaluating data as they have to get used to the new complexity. In this article, we’ll provide an overview of the most popular types of predictive analytics models and algorithms currently used to solve business problems.

What Are Predictive Analytics Tools?

Predictive analytics tools are based on different models and algorithms that can be used for different applications. Identifying the benefits of predictive analytics tools for your business is key to getting the most out of your solution and using the data to make informed decisions.

The problem is that many companies want to achieve great results but don’t know where to start. Implementing advanced analytics initiatives can be a daunting task, but the following five algorithms can make it easier.

But how does predictive analytics help your business? Most often, they start with a use case. It often involves new ways of transforming and analyzing data to uncover previously unknown patterns and trends in the data. Applying new insights to business processes and practices can lead to positive changes in a company.

What Are The Top Five Types Of Predictive Analytics?

Type 1. Classification Model

The classification model is considered the simplest among the different types of predictive analytics model that classifies data and provides clear and easy to understand answers to the questions in the questionnaire. It groups data into categories based on inferences drawn from historical data. It is the model that best answers the “yes” or “no” questions and provides a comprehensive analysis that can be used to guide action. The versatility of the classification model means that it can be applied across a wide range of industries.

Type 2: Cluster Model

The cluster model organizes data according to common characteristics. It is a mechanism that bundles data into discrete, nested and intelligence based on similar behaviors. For example, if an online shoe company wants to launch targeted marketing campaigns for its customers, it can filter hundreds of thousands of records and create a personalized strategy for each user. With this model, a company can easily determine the credit risk of a borrower based on the past performance of other borrowers in the same or similar circumstances.

Type 3: Time Series Model

The time series model consists of a series of data points collected over time that serve as input data. Based on the previous year’s data, a numerical index is calculated and used to forecast data for the next three to six weeks.

This is an effective way to understand how each piece of data changes over time and is more accurate than simple averaging. It also takes into account seasons or events that may affect the index.

The number of stroke patients hospitalized in the last six months can be used to predict how many patients are expected to be admitted next week, next month or at the end of the year.

Type 4: Forecast Model

The forecast model is one of the most widely used predictive models and is used to predict metrics. This very popular model applied to anything that is numerically significant and based on learning from past data. It estimates the numerical value of new data based on past data. This model can be used wherever historical data are available.

The model also includes a number of input parameters. If a restaurant owner wants to predict how many customers he will have in the coming week, the model takes into account that affect him, such as: how many pizzas a restaurant will order next week or how many customer service calls a customer service department will handle in a day or a week.

Type 5: Outliers Model

The outliers model is based on the metrics records in the database. It works by analyzing anomalies and unusual data points. You can define outliers on their own or in combination with other outliers and categories. For example, a bank might use the outliers model to detect fraud by checking whether certain transactions deviate from the usual pattern of customer spending, or whether certain types of spending are normal.

How To Find The Best Predictive Model For Your Business?

First, decide what predictive questions you want answered and at what quality level. And above all, what you want to do with the data. Weigh up the benefits of each model, optimize the use of different predictive analytics algorithms and decide how to apply them to your business.

It is therefore not easy to decide which of these models is best for you and your business. It has to be a carefully considered decision. If you still need help or have questions, please contact Existbi’s Predictive Analytics Consulting Team and see how we can make it work right for you!


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Top 21 Microsoft Power BI Business Benefits in 2022

Microsoft Power BI is an intelligent analytic tool that can collect, analyze and visualize data from different sources in seconds. The content created can be distributed and embedded on tablets. In this way, distributed data becomes meaningful and interactive information for the business.

This blog will discuss the top 21 benefits of the Microsoft Power BI tool to help users view intuitive insights for their business in 2022.

1. Customized Dashboards And Interactive Reports

Power BI is known for its dashboard that can be tailored to your business needs. Dashboards can be customized to your business needs. It also offers intuitive and interactive visualizations. In addition, employees can quickly and easily create customized reports with drag and drop functionality. Data reports are displayed on customized dashboards to ensure a consistent user experience.

2. Drag And Drop Functionality

Drag and drop functionality makes it easy for users to create customized reports. Users can create ad hoc reports in minutes using a familiar drag and drop process.

You can click on the target field and drag it to the value column. You can add a list of customers along the Y-axis and immediately get a list of customers based on sales value.

3. No Technical Support Is Required

Power BI is a self-service business intelligence platform that allows employees to create and report without technical or IT support. It supports a natural language user interface, uses intuitive graphical design tools, and includes drag-and-drop summary tables.

4. Enable Artificial Intelligence

Power BI leverage the latest advances in Microsoft artificial intelligence to help data scientists prepare data, create machine learning models, and quickly discover information from structured and unstructured data such as text and images.

5. Fast Retrieval Of Business Information

Users can quickly gain insight into their business information with the Power BI analysis tool. Dashboard reports give you an overview of your business by viewing graphs and tables. The graphs’ metrics help you gain insight into transactions and improve decision-making.

6. Access Data In Real-Time

Dashboards are updated in real-time as data is uploaded or downloaded, allowing users to resolve issues and identify opportunities quickly. All metrics can display and update data and views in real-time. This allows staff to solve problems quickly, identify opportunities and manage time-sensitive data or situations more effectively.

7. Easy Integration With Existing Applications

The Power Business Intelligence solution integrates seamlessly with all Microsoft products and systems, allowing organizations to easily deploy and use Power BI analytics to analyze data reports. It also offers the ability to integrate data with third-party tools and solutions such as Salesforce, Google Analytics, Spark, Hadoop, etc. This means you can integrate accurate data reports into dashboards for better decision-making.

8. Affordable Solutions

Microsoft Power BI is a simple subscription-based tool that does not require the purchase of licenses, support, etc. Users can sign up for the free version and start customizing their dashboards. In addition, companies can perform analysis on the spot, saving money. If you want to collaborate with colleagues, you’ll need to upgrade to the Pro Version.

9. Question And Answer Functionality

Power BI eliminates the need for complex language to conclude data. The question-and-answer feature allows users to extract information by asking questions in natural language. This allows companies to get information about their business in a self-service format.

10. Easy To Install

Easy to start, no training required, quick to set up, and has dashboards for services such as Salesforce, Google Analytics and Microsoft Dynamics.

11. Easy To Migrate

There are no memory or speed limitations when migrating from an existing BI system to a high-performance cloud environment because Power BI is designed to extract and analyze data quickly.

12. Flexible Navigation

Application navigation allows report developers to customize navigation so users can quickly find content and understand the connections between different reports and dashboards.

13. Convenient Security Filters

Report developers can set up row-level security filters (RSL) to ensure that users only see information that is relevant to them, reducing the risk of users seeing information they shouldn’t.

14. Available On Different Devices

With Power BI, you and your team can easily access reports and dashboards wherever you are – in a client meeting, working from home, or on the go. It can be used on iOS, Android, and Windows devices. When you are connected to the internet, you can access reports instantly.

15. Simplified Visualizations And Deployment

Analysts upload reports and visualizations to Power BI rather than sending large files via email or a shared drive. Data is updated as soon as the main dataset is refreshed.

16. Direct Dashboard Sharing

Power BI Pro lets you share data views with other employees in your organization. There is a link on the Power BI dashboard, and if you click on the link, you can access the dashboard through your office 365 account.

17. Multiple Dashboards Deployment

With Power BI apps available to Power BI Pro users, you can quickly create a collection of custom dashboards and reports and use them effectively across the enterprise or for specific groups.

18. Cortana Integration

Power BI works with Microsoft’s digital assistant, Cortana. Users can ask natural language questions to access tables and graphs. This can be particularly useful for mobile device users.

19. Excel Integration

Many companies still use Excel for analysis and reporting. Power BI connects seamlessly to Excel. Users can easily add queries, data models and reports to Power BI dashboards and create interactive visualizations without learning a new program or language.

20. Large User Community

Power BI has over 5 million subscribers and is used by over 200,000 organizations. The online community has grown significantly in the last two years, and everyone is sharing ideas on how to create dashboards.

21. Regular Updates

Another important feature of Microsoft Power BI is the monthly update of the platform. It is constantly updated to provide the latest and most advanced features to help them make better business decisions. Updates are usually performed once a month, but in Power BI Pro, you can schedule daily or even hourly data updates.

Wrap-Up

Power BI is particularly useful for companies operating primarily in a Microsoft environment. If your employees already know how to work with Excel, there is no need for serious training. However, anyone can join our 3-day Analyzing Data with Power BI Training if you still want to learn various Power BI integrated solutions for varied data sources and technical requirements for visualization types.

In this training course, students will also learn how a large amount of data can be turned into clear, interactive graphs in minutes.


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How Artificial Intelligence Has A Role To Play In Digital Marketing

Artificial intelligence is widely used in areas such as health care, finance, gaming and entertainment, but how can it be used in digital marketing? By combining different technologies, machines will be able to perform cognitive functions that originally only humans could. Artificial intelligence that analyzes and learns new insights from big data can optimize marketing and make it profitable.

This blog explains what artificial intelligence is and how it can save you time and effort in digital marketing, so it’s well worth a look. The article also outlines the benefits of using AI in marketing and other opportunities it offers. In the process, you will learn why you and your company should explore AI.

What Is AI?

AI is a combination of different technologies. It enables machines to perform cognitive functions. For example, AI enables them to learn, think, interact with new content and contexts, and connect with their environment.

Artificial intelligence is now being applied in many fields, such as medical diagnostics. AI is also used in self-driving cars and facial recognition and applied to mobile phones.

AI is also being used in digital marketing. For example, intelligent algorithms can help find the right target audience, tailor advertising and improve the content design. Social listening – understanding how people talk about your brand online – can be replicated with AI.

Artificial intelligence can process large amounts of data at high speed. They can classify images, recognize faces and languages, and identify patterns. Based on these patterns, they make predictions and recommendations that can be adapted to new data sets over time.

But wait: will this AI be smarter than us humans? Don’t worry, that’s not a problem. Although machines can process much more data than we can today, they may never be able to match typical human intelligence. As in many other areas, the solution lies somewhere in between the field of artificial intelligence and digitalization. We need to combine human and machine intelligence for the benefit of all.

What Does Artificial Intelligence Have To Do With Digital Marketing

Digital marketing is characterized by rapid growth and large amounts of consumer data. This big data is often inaccessible to us humans, especially if we don’t have experience with statistics. This ambiguity makes online marketing channel choices difficult. Of course, the customer journey varies from person to person and is often carried out through several channels at once.

Good online marketing can be an overwhelming task given the growing number of marketing channels, tools, and techniques. This is where artificial intelligence comes in. For example, it can help in the following areas.

Customer Understanding

By analyzing large amounts of data, AI can identify “key moments” in the customer journey and determine whether the customer has read and understood the text.

Choosing The Right Technology

Brands such as Amazon are already using AI to evoke emotions. Analysis of customer sentiment can be used in marketing.

Use Of Avatars

Automated services such as chatbots and virtual beauties can be made smarter with AI.

Short Ads

Soon, digital outdoor ads can be smaller and more flexible to reach very specific audiences. Artificial intelligence will calculate which ads are relevant to an individual at a given moment.

But artificial intelligence always needs a baseline. The software uses existing information to determine the baseline, such as data files from previous advertising campaigns. The AI learns from the data. It makes recommendations and continuously evaluates their effectiveness against objectives.

What Are The Benefits Of Using Artificial Intelligence In Digital Marketing

Research shows that marketers see the greatest potential for AI in the areas of personalization and automation. AI applications are already helping to understand target audiences and tailor communications accordingly. AI performs automated data analysis, significantly reducing the burden on you and your team.

AI not only saves time, it simply does a better job than a human – hard but true. This is especially true for complex, data-driven tasks and those tasks that humans can’t do manually in a reasonable amount of time. After all, we’ve all made mistakes in Excel, haven’t we? Artificial intelligence can’t do that.

It is certainly not a threat to workers but a reassurance. AI gives us more time to focus on strategic issues. Together with your team, you can evaluate the effectiveness of AI and develop new algorithms to solve really interesting problems.

In many cases, AI can work not only quickly, but also in real-time. It can improve the effectiveness of marketing campaigns. Increased conversions, customer engagement and brand loyalty are the results of real-time AI support.

Artificial intelligence can also help with data collection and analysis in digital marketing. Large amounts of data can be collected and analyzed in real-time. It can create custom rules for analysis. For example, AI can predict message changes and interests.

Artificial intelligence can help you better understand your audience and customers. Segments your customer by criteria such as gender, buying habits and indecision. This segmentation can be very small if necessary. AI can also show how segmentation evolves as factors such as purchase intent change naturally. For an email marketing strategy, this is a dream come true.

With these capabilities and the benefits of AI, the future digital marketers will be better equipped to take on the tasks ahead.

Personalized Website

AI allows every part of the website to adapt in real-time to the relevant customer segments’ needs and motivate them to take action. This allows more precise targeting of customers through offers, messages, or discounts.

Social Monitoring

It can quickly gain detailed insights and, for example, suggest relevant influencers for campaigns. It can also anticipate crises and avoid them more easily.

Interacting With Chatbots

Interacting with human consultants is an important but time-consuming and costly service. AI bots can answer many questions in the same way, and if they have a question, they are directed to a human, so the digital service remains human! Of course, AI can work continuously, taking over after hours and at weekends.

Content Creation

The Washington Post uses AI-powered software that has already written hundreds of articles. Such support for content creation is invaluable, especially when internal resources are limited. It allows the team to focus on more important tasks.

Who Is Already Using Artificial Intelligence

The examples below show how artificial intelligence is helping digital marketing in different areas – maybe one or two will inspire you?

Delta Air Lines, for example, is already using AI in a number of areas, particularly in the automatic evaluation and processing of customer feedback. This benefits both the target group and the company. It also supports simple responses to emails and predicting popular destinations based on high-demand content and videos.

Artificial intelligence has been part of Starbucks’ business since 2016, collecting key customer data in the app and tailoring offers accordingly. The app can make personalized recommendations, offer relevant discounts and find the nearest coffee shop.

The Future Potential Of Artificial Intelligence In Digital Marketing

This is why artificial intelligence is already so popular in online marketing. In the future, technology will be further developed. This means an even greater focus on data and personalization for the marketing industry.

However, the use of artificial intelligence in many US companies is only just beginning. This is because the digital transformation is still some years away. There are also privacy issues and strict regulations on data collection mechanisms.

However, there is no doubt that artificial intelligence can support productive work in Digital marketing, providing new insights into target groups, optimal advertising channels, and relevant marketing content. It is, therefore, even more, important now to address this issue.


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Differences Between SAP BusinessObjects Web Intelligence 4.2 And 4.3

SAP BusinessObjects Web Intelligence has been rolled out to users starting 2020 with a complete overhaul of the tool, here are some of the improvements and changes.

Interface

There is a big difference in the design of SAP BOBJ 4.2 mimicking the older Office Suite environment, which was a carry over to the design of SAP BOBJ 4.0.

SAP BOBJ 4.3 has been overhauled to integrate SAP’s own Fiori design to the web interface.

Screenshot from SAP Blog (https://blogs.sap.com/2020/06/15/sap-bi-4.3-whats-new-in-web-intelligence-and-semantic-layer/)

JAVA Applet No More

BOBJ 4.3 has completely removed the JAVA Applet view in BI Launchpad. 

Since the introduction of BOBJ 4.2 SP1, SAP started integrating its functions that are previously exclusive to both JAVA applet and Rich Clients. There were two main reasons:

  1. JAVA support has been limited. Chrome (this includes all browsers derivatives from Chromium including the new Microsoft Edge) and in Mozilla Firefox has removed its support for JAVA. The only browser (as of this time) that supports JAVA Applet is Internet Explorer 11, which has already reached its end-of-life.
  2. Migration to modern web functions. Improvement in the Web-based HTML Interface greatly helped SAP Webi in shifting most of the functionalities from the JAVA Applet and Rich Client and preparing the tool for modernization.

With the release of Web Intelligence 4.3, the two views that can be utilized: HTML (via BI Launchpad) and Rich Client. With the introduction of these two views alleviate the following problems from Webi 4.2:

  1. Complicated steps to make it work. Development in Webi 4.2, especially in older versions, the following steps were needed:
  • Install an outdated browser, which has been terminated.
  • Install the right JAVA version, which sometimes does not work due to JAVA’s own security settings.
  • Use WINDOWS Operating System, which Mac OS and Linux-based lack support for JAVA.

2. Tricky and Time-consuming development. Since not all features are available specially in older versions of Webi 4.2, . developers need switch from HTML and JAVA applet if they need to do the following:

  • Add and modify other supported Web Intelligence Data Sources that is not available in HTML (Early versions only have Universe as Data Source for HTML).
  • Change data sources. 
  • Use exclusive features like adding and modifying Conditional formatting and adding complicated query conditions.

This has been resolved in BOBJ 4.3, which makes development easier and less time-consuming.

Revamped Settings

BOBJ 4.2 relies mostly in popups for additional options to add depth into the data.

In Webi 4.2, when creating/changing into a different visualization , it is displayed into a new popup screen.

In Webi 4.3, most of the additional options have been placed into panes. The pane appears when selecting a specific element within the Document. It also changes options depending on what element is selected.

The Option Pane is divided into two main tabs: Build Pane and Format Pane.

Build pane mostly handles additional settings within the element.

Format pane handles aesthetic-related options like Text formatting.

The Option pane consolidates all options with the exception of Conditional Formatting, Change Data Sources, Tracking options, and Input Control options.

Conclusion

There is a great improvement from BOBJ 4.2 and 4.3, which leaned to modernization and ease of use.


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How Big Data And Artificial Intelligence Differ From Each Other?

Big data and artificial intelligence are hot topics on the minds of business leaders. Together, they significantly impact a company’s ability to collect and analyze data. There are many examples of artificial intelligence and big data going hand in hand in today’s environment. However, they have evolved as different technologies and they have differences between them.

What Is Big Data?

Since the emergence of the digital age, big data has been around and refers to large amounts of data characterized by three elements known as the ‘3Vs’: volume, velocity, and variety. Big data sets are distinguished from other data sets by their size (volume), their rate of growth/change (velocity), and the variety of structured, unstructured, and semi-structured data in the data set.

The advantage of large data is that they may contain hidden patterns and trends that are only visible in such large data sets. However, because of the size and complexity of big data, its value lies not in the data itself but in its analysis, which is a difficult task. Big data is so large and complex that traditional data processing and analysis methods cannot extract business value from such large data sets.

So far, companies have spent most of their time in this area. In the past, companies had to spend a lot of time, money, and resources analyzing data to extract valuable insights. 

Fortunately, the advantages of big data strategies have enabled researchers to aggregate large data sets for practical analysis. That’s why big data analytics can transform large amounts of data into easy-to-understand formats that businesses can fully leverage and integrate technologies such as artificial intelligence and machine learning to extract other valuable insights. 

What is Artificial Intelligence

The study of “intelligent” problem-solving behavior and the creation of “intelligent” computer systems. Artificial intelligence (AI) deals with methods that enable a computer to solve those tasks that, when solved by humans, require intelligence.

The term artificial intelligence is applied to the machines’ ability to autonomously execute a set of tasks on the basis of algorithms and to adaptively react to unknown situations. They therefore behave in a similar way to humans: not only performing tasks repetitively, they learn from their successes and failures and modify their actions accordingly. In the future, AI should be able to think and communicate like humans.

The big difference between big data and artificial intelligence is that big data is raw input data that needs to be cleaned, structured and integrated before it becomes useful, while artificial intelligence is the result, the intelligence derived from processed data. This makes them inherently different.

Difference Between Big Data And Artificial Intelligence

The terms Big Data and artificial intelligence (AI) are often used in the same breath in political and social discourse. To avoid the appearance that these two terms are synonyms, this section addresses the term AI to help distinguish it from the term Big Data.

The term big data initially refers to a description of data based on various data properties. However, it is often used synonymously for its processing, application and analysis. 

The concept of artificial intelligence, on the other hand, does not focus on data or a set of data, but on algorithms that use this data as input factors. To put it briefly: Big Data is a prerequisite for artificial intelligence; but artificial intelligence is not a prerequisite for Big Data. Big Data can therefore exist without AI. For good results in the sense of sufficient data volumes for learning, AI cannot do without Big Data.

The most important foundations for AI as a sub-field of computer science are sub-symbolic pattern recognition, machine learning, computational knowledge representation, and knowledge processing, which includes methods of heuristic search, inference, and action planning.

On the one hand, this shows the characteristic that AI is a technology or application. On the other hand, this technology uses data sets in its processing. This is the essential difference between the two terms artificial intelligence and big data, which has already been briefly touched upon.

In this analysis, the difference between the two terms AI and Big Data is primarily that AI is application or algorithm, while Big Data describes data and its processing. AI is understood to be a rule- or data-based application that makes decisions, while Big Data primarily involves the generation of information. 

In another definition, it refers to “very large and heterogeneous data sets” further indicating that Big Data is given a significant role as a necessary input for AI.

Artificial intelligence is not a new phenomenon, but was used decades ago. In the early days, there were many applications of AI to human games such as chess. This application area is suitable because of the simple rule system and the thus clearly describable options for action.

These are searched by the algorithm in their combinations until a desired result is achieved. This was followed in time by the AI applications of machine learning. Here, the algorithm learns independently from the results it has generated. The algorithm learns feedback, which it uses to make optimizations. The latest development is Deep Learning, which works with the help of neural networks. The structural design of the neural networks is based on the nerve cell connections of the human brain.

The algorithm’s learning process uses multiple layers that are interconnected. Learning is done from the data and results are calculated for further explanation.

AI And Big Data Solutions 

Big data and artificial intelligence will continue to evolve and play an essential role in business solutions. Discover how Existbi’s big data solutions can make your job easier. Turn raw data into valuable insights.


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Data Warehouse And Data Lake: The Evolution Of Lakehouse

In today’s world, the new gold is the Data. Everyone now knows this. But like gold miners, companies have nothing to do with a pile of dirt and a few gold nuggets. To get the true value of gold, it needs to be filtered and processed. The data needs to be stored, cleaned and enhanced in a structured way so that it can be used in reporting analytical spreadsheets or for training machine learning and artificial intelligence. Currently, different approaches exist depending on the amount of data, the frequency of logging and the availability required. So, lets dive into the details of the origin and evolution of lakehouse and discover how it integrates the best elements of data warehouses and data lakes together.

evolution of lakehouse

Existing Solution: The Data Warehouse

A data warehouse is defined as a central data management system, specially organized for analytical purposes, which brings together data from a wide variety of sources. It is then used for data analysis and reporting. The data stored here are mostly in relational format.

This requires data to be stored in a clean structure. It can be accessed via the most commonly used database language, SQL (Structured Query Language). In addition, BI tools such as Power BI, Informatica or Tableau can be directly connected to the data warehouse. This means that analysis and dashboards can be created by business analysts who are not familiar with SQL.

When starting up a large new data project, it is often the simplest solution to store the collected data directly in the data warehouse. This is optimized for fast reads, but becomes too slow to write when continuously reading and transforming. 

Therefore, load time can be inconvenient for customers with dynamic dashboards. The buffer is needed to prevent small amounts of data from being written continuously.

Rising of Data Lakes

A data lake is a well-known data storage system that acts as a buffer. Data warehouses store data in a structured format, while data lakes can store data in an unstructured format or in different formats. However, it should be noted that again, the more uniform the structure, the faster and more efficient the access to the content.

There are several advantages to implementing a data lake. When data is loaded directly into the data warehouse, it is often not possible to transform it before loading. Therefore, ETL channels (export, transform, load) should still be used.

The transformation must be calculated by the data warehouse at each load. This results in longer waiting times for the clients and higher costs for the data warehouse. These can also be directly precomputed in the database to save resources, of course, but this only changes the problem, since the database is still loaded at the time of the calculation.

The easy availability of scalable data warehouse solutions along with cheap on-demand computing power make the data lake ideal for implementing an ETL channel. Here, raw data is loaded into the data lake. Saturated or merged copies of the data are created and loaded in the data warehouse.

Storing data in the data warehouse is often expensive, so it makes sense to store only important and frequently used data. This is not a problem for the data lake. Data can be easily removed from the data warehouse after a certain period of time, but is still available throughout the data environment and can be retrieved with a longer delay if necessary.

The data in the dataset can be accessed in several ways. Primarily Python and R but also methods such as Spark. These programming languages are among the most commonly used in data science and are used in machine learning libraries such as XGBoost, Py Torch and TensorFlow.

However, these are designed to access data lakes, as training machine learning models always requires large amounts of data to be loaded and transformed simultaneously, and the end user should not experience any delays in using the dashboard during these processes.

Data warehouses, on the other hand, are primarily designed to store the results of analyses or small amounts of data. However, there are efforts to integrate machine learning directly into the data warehouse. Examples include technologies such as AWS Amazon Redshift or direct access to Amazon’s Sagemaker machine learning platform for Redshift. The Snowflake provider also offers the possibility to train machine learning models directly in the data warehouse.

data warehouse and data lake to lake house

Data Warehouse To Data Lake

One of the disadvantages of traditional data warehouses is that storage and computing power cannot be increased independently. This leads to prohibitively high costs as data volumes grow.

Modern data warehouses, such as Redshift and Snowflake, allow storage capacity and computing power to be scaled at least partially independently, just as they would in a data lake.

Data Lake To Data Warehouse

The most important features of the data warehouse compared to the data lake are probably the DBMS management functions, such as user access rights to individual data, ACID transactions, versioning, auditing, indexing, caching and query optimization.

Open source technologies already exist that allow some of these functions to be used in a data environment. Delta Lake or Apache Hudi create a metadata layer between the data environment and the application using it. Among other things, this layer contains information about which objects belong to which version of the table. 

It allows ACID transactions and restricts user access to certain data. It also simplifies the creation of data versions in the data environment. In addition, some data schemas can be preserved by storing them in the metadata layer and checking them at load time.

These metadata can also be used to improve performance. Some of the data to be analyzed can be stored on the fastest solid state drive (SSD) or random access memory (RAM). In this case, the metadata can be used to identify stored data that is still relevant during transactions. In addition, minimum, maximum or batch sets can be stored, which speeds up the search for data points.

Evolution Of Lakehouse

Both the data warehouse and the data lake have their own advantages and can complement each other in some respect. But new technologies such as Delta Lake or Apache Hudi are increasingly combining them. The question is therefore whether the two systems will remain completely separate in a few years time or whether they will merge into a hybrid system.

Lakehouse’s open architecture approach to data storage, transformation and analysis is already gaining ground. For example, resources can be optionally reserved specifically for data entry and transformation so that they do not affect the backlog of customer order. In addition, data from the data warehouse is often stored in a proprietary format. With increasingly stringent data requirements and companies desire to avoid relying on a single provider, the long-term trend in the software industry is to open up data formats.

Data Lakes typically use the open source data format. The Lakehouse approach aims to combine the best aspects of the data lake with the data warehouse, replacing two systems. 

After all, the reduction in the number of ETL pipelines, and by eliminating multiple technologies will save money and increase the incentive to adopt Lakehouse.  


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Is SAP Business Warehouse Suitable For The Company Of The Future?

With SAP’s strategic focus on its cloud offerings, the future will be about the cloud data warehouse platform. In the long term, cloud data warehouses can be the successor to enterprise data warehouses. At the same time, the business data warehouse bridge will need to offer migration options for parts of the BW system.

However, enterprises need to prepare for the public cloud.

SAP Business Warehouse users who are considering a data warehouse and deploying SAP BW/4 to modernize HANA are faced with whether it is still appropriate for enterprises in the future.

We believe that BW/4 HANA deployment is still an appropriate solution for three reasons:

#1. SAP BW/4 HANA has been successfully deployed for several years and can be used for almost any data warehouse use case. It is now mature and ready for deployment.

#2. SAP provides support until 2040, which is a guarantee of a secure investment.

#3. Finally, BW Bridge offers an option where BW deployment in a cloud data warehouse is doomed to fail. Therefore, there is the possibility of clouds.

All SAP BW users are advised to monitor their data warehouse cloud evolution closely.

SAP BW/4 HANA And Cloud Data Warehouse Integration

From a technical perspective, there are several options for integrating the solutions:

Remote Tables Or Database Connectivity

In this scenario, HANA is connected to the BW/4 data layer and used as a remote source. This allows data to be accessed from the BW system. This is a quick and straightforward way to implement an integration solution.

If you only use this hybrid approach, you will have to recreate the semantics in DWC manually. However, this is the easiest way to quickly integrate the data models available in the cloud data warehouse so that departments can perform extensions.

Using BW/4 HANA Transfer Models

Unlike a standard database connection, a model transfer connection is linked to a BW query. In addition to the required database connection, the data warehouse cloud also scans the semantics and creates the objects required for the business layer. The significant advantage of this type of connection is that there is no need to model the business layer manually.

About BW Bridge

BW Bridge is a BW/4 HANA solution built in a data warehouse cloud environment. The DWC Bridge provides BW data models in the bridge space, so there is no native BW integration into the Data Warehouse Cloud modeling objects.

BW Bridge is not an integration of an existing BW/4 HANA system into the Data Warehouse Cloud. However, BW Bridge customers can migrate or migrate their BW system to the Data Warehouse Cloud environment. The migration of existing systems is theoretically possible. However, the range of bridge functions is minimal from a BW perspective, so much will have to be manually restored in the local “core area” of the Data Warehouse Cloud.

Is It Worth The Investment To Upgrade To SAP BW/4 HANA?

SAP BW/4 HANA is a very mature data warehouse used very successfully. Therefore, it is the right product for creating enterprise databases.

SAP has a roadmap for BW/4 HANA until at least 2040, which gives customers long-term investment security.

There is also the question of future options for companies upgrading their SAP HANA SQL data warehouse. The technologies and methodologies used are fully compatible with the HANA cloud. Companies can manage their data warehouse on-premises or in the cloud.

Because DWC is built on the HANA cloud, the SQL for HANA approach can be successfully integrated into a cloud-based data warehouse, allowing HANA-based EDW to be extended with self-service functionality. The SQL for HANA approach is also likely to be more secure from an investment security point of view, as, unlike BW/4, it is not a single product but a set of specific tools and custom methods.

There are a number of tools SAP used to develop HANA applications. Therefore you need to apply product guidelines before using them.

The mapping methods strictly conform to general DWH development standards, the SQL DWH methods in HANA are not significantly different from other SQL-based DWH frameworks, such as in Microsoft environments.

Overall, HANA SQL DWH SQL is a safe investment in line with SAP’s strategy and commitment.

The native EDWH HANA Cloud is an option for companies that already want to deploy EDWH in the cloud but do not want to do so in a non-SAP environment.

As mentioned above, HANA SQL Data Warehouse can be deployed in the cloud as it is already available. SAP has not formally announced this capability, but it exists, it works, and it should be considered as attractive investment as it largely cloud-based approach to HANA SQL.

This approach is similar to deploying EDWH on AWS, Microsoft Azure, or Snowflake, so it’s an option if you want to stay in SAP’s cloud environment.

In such cases, it is often desirable to have an open approach to data warehouse that the application-oriented approach of BW does not offer. Customers who consciously choose a BW-based strategy are also recommended to consider a HANA SQL or HANA Cloud-based strategy.

BW implementation at the national level is comparable to other BW implementation approaches that are not based on action plans. In addition, SAP has offered a highly scalable and efficient platform in the form of HANA Cloud, which we believe is the best in its class.

Conclusion

The Data Warehouse Cloud is SAP‘s most important strategic product in the DWH space. There can be no doubt about this.

Today, many users are deploying this system from scratch. They are getting a very mature solution that they can be confident will make their projects successful. 

SAP has a very long-term plan. However, we recommend that you take Data Warehouse Cloud into your consideration.


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Big Data Analytics: Importance and Benefits In Modern Businesses

The new economy, digitization, big data along with big data analytics are business buzzwords that have one thing in common: they are still big business. They represent the biggest change possible. Everybody can think of them in some way, but they are terms that are still in widespread use. 

Big data analytics can be understood more broadly. It’s one reason to study the subject in depth and understand the opportunities it presents for your business.

big-data-analytics

Big data analytics involves the analysis of large amounts of data from different data sources (Big Data). It uses the knowledge gained to make decisions, optimize business processes and exploit competitive advantages.

What Happens When Analyzing Large Amounts Of Data?

Big Data Analytics Takes Place In Three Stages:

Extracting Data From Data Sources

Today, data can be extracted from a variety of sources, from web analytics tools to smart home and smart factory applications. The challenge is to bring together this usually unstructured mass of data. 

The term data mining is often used for this purpose. This means that the data is available in raw form, for example in a mine, and needs to be extracted before it can be processed in a targeted way.

Structuring and Optimization The data Set

After the first step, there is a large amount of data that is still practically unusable. To do this, the right software will structure this amount of data according to parameters that you define.

Data Analysis And Processing

If the first two steps are mainly useful for working with the dataset, the real value lies in the third step: you can gain insights from the data analysis and use them to make decisions and optimize your business.

This step usually corresponds to big data analysis, sometimes used synonymously with big data analysis. It is a subsection of the big data analytics review.

Application Of Data Analytics

Big data analytics is used by companies in the business intelligence field. Analytics can provide users with important contextual information that can be used to optimize one or more processes. Efficiency gains can give you an advantage over your competitors.

You can also process the data for specific purposes, such as digitizing sales: effective sales tracking increases the likelihood of reaching and convincing potential customers in the long term.

Challenges In Big Data Analytics

The challenges of big data lies in the data itself:

  • Unstructured data is available in many places in many formats.
  • Data sources must be consistent with each other.
  • Data must be diverse and disparate across many sources.

Define What You Want To Achieve By Analyzing Big Data:

If you really want to use data to achieve your goal, you need to define the goal you want to achieve by analyzing big data. To do this, you need to know your company’s capabilities, know how to perform the analysis, select the right technology and use it.

The final cost of big data analysis depends on these decisions. To achieve a high return on investment, the investment should depend on the desired, preferably specific, objective.

Big Data Analytics Tools

There are many different technologies for analyzing large amounts of data. The ones listed here are well known and each focuses on a different area:

Informatica PowerCenter

Informatica PowerCenter is one of the most widely used ETL (Extract, Transform, and Load) tools in the world. No matter if you have a number of databases or a data warehouse, Informatica PowerCenter lets you safely process the data they hold while maintaining its integrity.

IBM Cognos

Today, modern businesses need different applications for proper data analysis, track events, find indicators or reporting in order to better acquisition and decision making. To solve this problem and provide a unified solution for businesses, IBM has created the IBM Cognos Business Intelligence suite. With the growing popularity of BI solutions, the demand for IBM cognos has increased dramatically.

Apache Hadoop

Apache Hadoop can be used in different architectures and on different hardware. It allows you to aggregate large amounts of data in a relatively fast cluster.

SAP BusinessObject

The use of SAP Business Objects is becoming extremely important in our constantly evolving and changing world. SAP BusinessObject BI tools are highly scalable and extensible. It can serve tens of hundreds of thousands of users and can be scaled up or down depending on the needs of the organization using it.

Splunk

Splunk provides centralized, real-time, cross-system access to historical and current data. Splunk thus becomes a data platform that enables faster problem identification and resolution.

Tableau

With Tableau, you can extract and process data. With visualization, you can gain instant insights that you can use to optimize your processes.

Zoho

Zoho is a big package with many programs. These include CRM, home office toolkit, financial platform and data analytics.

Importance of Big Data Analytics In Modern Business

Today, big data has become an asset. Take a look at some of the world’s biggest technology companies. They value their data, which they constantly analyze to make their operations more efficient and develop new products.

In a recent survey, 93% of companies consider big data initiatives “very important”. Using big data analytics solutions helps companies uncover strategic value and make the best use of their resources.

Finding value in big data is not just about analyzing the data. It’s a full exploration process that requires analysts, business users and managers to ask the right questions, identify patterns, make educated guesses and predict behavior. 

The importance of big data does not depend on how much data a company has. It’s about how the company uses the data it collects.

Each company uses the data it collects in its own way. The more efficiently a company uses its data, the faster it grows.

In today’s market, companies need to collect and analyze data. Let’s see why big data is so important:

Saves Money

Big data tools such as Apache Hadoop, Spark, etc. offer advantages to companies when they need to store large amounts of data. These tools help companies to find more efficient ways of doing business.

Saves Time

In memory, real-time analytics helps businesses collect data from multiple sources. Tools such as Hadoop help them analyze data instantly and make informed decisions quickly.

Understanding Market Conditions

Big data analysis helps businesses better understand market conditions.

For example, analyzing customer buying behavior helps companies identify their best-selling products and manufacture them accordingly. This helps companies to stay ahead of competitors.

Monitoring Social Media

Companies can use tools to process large data sets to analyze emotions. This allows them to get feedback about their company, i.e. find out who is saying what about it.

Companies can use big data tools to improve their online presence.

Improve Customer Acquisition

Customers are an important asset on which all businesses depend. No business can succeed without a solid customer base. But even with a good customer base, they should not ignore the competition in the market.

Not knowing what your customers want will affect the success of your business. This results in loss of customers, which has a negative impact on the growth of the company.

Big data analytics helps companies identify trends and patterns with customers. Analyzing customer behavior leads to profitable business.

Providing Market Information

Big data analytics shapes every business process. It enables companies to meet customer expectations. Big data analytics helps transform a company’s product portfolio. It provides effective marketing campaigns, stimulates innovation and product development.

Benefits Of Big Data Analytics

Big data analytics is well established across a variety of industries. Thus, big data is used in many industries such as finance, banking, healthcare, education, government, retail, manufacturing and many more.

big-data-analytics

Many companies such as Amazon, Spotify, Linkedin, Netflix etc. use big data analytics. The banking sector is the largest user of big data analytics. The education sector also uses data analytics to improve student performance and to help teachers teach.

Big data analytics helps retailers – both traditional and online – to understand customer behavior and offer products that match their interest. This helps them to develop new and improved products, which is very beneficial for the business.

However, many companies are still not clear about what big data is and how this analytical capability in commerce can benefit their business model. Lets see some of the sectors that are already using big data analytics.

Product Development

Analyzing large amounts of data can be a crucial advantage during development. For example, by assessing social media channels or customer feedback, you can identify social trends and market gaps early on.

Production

As manufacturing becomes smarter, it is no surprise that big data is playing an important role in this area. For example, many processes are monitored by sensors that generate large amounts of data. This data can provide predictive maintenance and prevent delays or failures in production.

Distribution And Logistics

Sensors are also increasingly being used in the supply chain, for example to measure fuel consumption or to record data on the location and condition of wearing parts. The structuring of this data means that costs can be reduced in the long term by scheduling deliveries on time, changing routes and loads, and reducing downtime and maintenance costs.

Marketing And Sales

Data analysis can significantly improve customer relations. By gaining a deeper understanding of your customers’ needs, you can target individual customers directly with personalized offers.

Banking

Big data analytics can help the financial sector make reliable forecasts or risk calculations. For example, the investment sector can react more quickly to market developments or price falls.

Conclusion

We find that big data helps companies make informed decisions and understand their customers preferences.

It helps companies achieve rapid growth by analyzing data in real time. It enables companies to outperform their competitors and achieve success.

Big data technologies help us identify inefficiencies and opportunities in-our business. They play an important role in determining the growth of a company.

Do you have experience with big data analytics? Want to get involved but don’t know where to start? 

At ExistBI, we look forward to sharing our ideas with you. We’d love to help you discover the potential of big data analytics for your business and put it into practice.


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Why Are Companies Adopting Big Data Strategy As A Road To Success

Big data seems to be both a curse and a blessing. The potential for growth and destruction is partly breaking the sea of data into an unmanageable mass. But shouldn’t simplified access to relevant industry data lead to greater security in modern business planning?

In this article, you can learn about the following topics in just a few minutes of reading:

  • How big data are affecting the corporate world.
  • Which industries are leading the way in the age of big data?
  • How big data creates an advantage over the competition.
  • How to adapt data analytics and strategy to the data culture.

Be More Efficient, Smarter And Faster With Big Data

Raw data becomes information. Information becomes knowledge. Knowledge generated by data analytics creates value for business. The goal is to capture, harmonize, structure and finally analyze large amounts of data from different sources. 

In the process of digitalisation, the almost unlimited storage space, cloud computing and faster computing speeds provide an ideal basis for profitable valuations. 

A systematic approach to data science offers companies a wide range of analytical possibilities. In this way, unknown patterns in large data sets can be uncovered and new business opportunities can be discovered. 

These discoveries have implications for the future of a company or an entire industry. 

This customer data can lead to innovative product developments and successful marketing activities, combined with the necessary expertise and human intuition.

The following types of data are particularly important for businesses:

  • System Generated Data: Market or regulatory data.
  • Customer Data: Customer behavior or community data.
  • Operational Data: Transactional or project management data.
  • Publicly Available Data: Log data or location data.

No one can afford not to take advantage of these opportunities.

Big Data Evaluation Breaks Into All Industries

No industry has a secret recipe for unlimited success in times of digitization. According to Harvard Business Research, 72% of companies fear they could be affected by the consequences of an increasingly digital world. Especially when it comes to so-called “born global” companies like Netflix or  Uber, which are massively blurring the boundaries of entire industries. 

Often with simple, dynamic and low-cost solutions that are rapidly displacing traditional competitors. Thanks to highly innovative software solutions and large capital investors, they have managed to spread through every industry worldwide in a very short time.

These successes are based not only on luck but also on good analysis of relevant data. The intelligent use of available information is a source of innovation and sustainable growth.

Data analysis is still too often carried out as ad-hoc analysis using simple IT tools such as Excel or Access. Advanced solutions should increasingly contribute to secure and sustainable business planning. The key word is digital intelligence means to wisely use data to your advantage.

Who Can Benefits From Big Data

Big data and data science have become increasingly important in many industries. Taking a leading role in the digital transformation based on big data is a central theme in many areas of top management. 

Let us first look at the results of developing a big data strategy for companies:

In terms of relevance and decision making – IT and electronics, healthcare and banking performed the worst. Together with the lack of implementation of real measures, they ranked last out of the 12 industries surveyed. 

With a focus on big data strategy development, the picture is changing again across all industries. About a third (34%) of companies say they have a big data strategy in place. 

However, there are differences between industries. For example, 56% of media companies and 46% of insurance companies have a big data strategy. Even the banking sector, which has previously shown even lower levels of data-driven decision-making, top the recent ranking.

It should also be noted that although the automotive industry is leading the way in the importance and use of data science, only 34% of companies have a big data strategy, according to Statista’s report. Like telecoms, IT and electronics are at the bottom of the league in terms of strategic focus.

In an increasingly digital world, it is no longer enough to keep an eye on the next competitor or isolated industry. Instead, you need to be aware of your own operational weaknesses. Combined with an analysis of comparable strategic groups, you can draw the next steps.

Complex and large-scale activities? This is where advanced analytical methods come in. This allows you to react quickly to changing market conditions. But how can this be achieved?

Data Analytics And Data Integration As Tools For Success

We will also briefly outline the key points that can lead to success in an era of digitization:

Data Strategy

Developing a data strategy has many benefits. According to a global survey of 270 institutional investors, conducted by KPMG, 62% of respondents are more likely to invest in a company that has integrated data analytics into its overall strategy. 

Data can be used to make more targeted strategic and operational decisions. The company’s strategy becomes clearer, easier to plan, more manageable and, above all, more transparent.

Data Governance

Integrating data in different formats from different databases is a complex challenge for companies. Sourcing and collecting data from the right sources, then synchronizing and storing it in the right format is the first objective of data governance. This allows you to sort and classify the data. 

Data Analysis

The right tools can help unlock hidden knowledge. But without the necessary human knowledge, intelligent interpretation and intuition, these assessments are of little use on their own. Therefore, more and more training and education opportunities are emerging that focus specifically on data science as a potential career area.

Data Integration

Data integration is the overall control of data access, usability, and security at the enterprise level. Combined with the right analytics and visualization tools, every byte can make the most of it.

Conclusion

Big data is not a trend that is going away, so it will be with us in the future. It is therefore worth looking into this issue and drawing the right conclusions for your business. This article only covers a small part of what you can do with big data in your industry. The possibilities are almost limitless, and with the right strategy you can quickly make the most of them!

Do you have experience with big data? Want to get involved but don’t know where to start? 

At ExistBI, we look forward to sharing our ideas with you. We’d love to help you discover the potential of big data strategy for your business and put it into practice.


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10 Common Questions You’ll Need To Explore About Data Warehouse

A data warehouse is a database used primarily for analysis and reporting. For business analysts and other users, it provides a central repository of accurate business data from which timely analytical information can be extracted to inform day-to-day business decisions.

The data warehouse is the foundation of the business intelligence system. Over time, the combined evolution of traditional systems and new technologies have led to many changes. In this blog, I’ll address the most common questions and answers about the data warehouse that you’ll need to explore in 2022.

#1. What Motivates A Business To Implement A Modern Data Warehouse?

Today we all know there is such a variety of data that can be used to facilitate better business decisions. In turn, there is invaluable knowledge in this. Businesses want to profit from it. The corporate world is becoming more and more complex as a result of digitization, so more and more decision-making tools are needed.

#2. Are There New Techniques That Can Be Used For Data Warehouse?

Many modern techniques support data warehousing. For example, Data Vault in connection with a relational database is becoming increasingly important if changes have to be made frequently in the database. Especially since the necessary computer capacities are no longer a problem today.

The Data Lake comes into play when new types of data such as sensor data or qualitative information are to be processed. Then it is a matter of recognizing patterns or trends. These need to be related to known data, which again requires a Data Warehouse.

Customization of data is a classic Data Warehouse task and the basis for all predictive processes. ERP systems are not designed for this. Thus, the interaction of data warehouse and data lake becomes a basis for predictive maintenance or predictions about customer behavior or customer churn, for example.

#3. What Role Does A Modern Data Warehouse Play In Digitization?

When more and more processes are digitized, the amount of information to be used increases gigantically. This could be sensor and machine data, extensive image/audio files or user information from the web.

Business departments are recognizing the new possibilities for analysis. AI-driven systems also bring new requirements. They would need to learn their cognitive capabilities. This also requires comparison with the past, with certain systematic, patterns or profiles derived from stored information.

#4. How Efficient Does A Data Warehouse Have To Be Today?

For many businesses today, a highly available data warehouse is crucial. After all, they want to be able to quickly use the ongoing analysis in their day-to-day activities to make decisions.

The strategy that companies ultimately choose always depends on their needs, and, in most cases, on their budget. The task, the user and the area of application determine whether it will be real-time processing.

#5. How To Get The Best Out Of Data Warehousing?

It is better to run the Data Warehouse on its own infrastructure. Then, other systems, such as the ERP, are not burdened either. 

There are two criteria for the best performance: 

First, good preparation of the database.

Second, hardware that is optimally configured for the Data Warehouses. Both play an essential role.

#6. How Do I Know If My Organization Needs A Data Warehouse?

Your organization may be in need of the data warehouse if it meets the following three common characteristics. 

a. A competitive industry that competes in a competitive market. 

c. Your organization has a huge amount of data.

b. Also, it is difficult to aggregate those highly dispersed data into one place.

If your organization fits this profile, it could benefit from implementing a data warehouse.

#7. Do User Expectations From Data Warehouses Have Changed?

In general, you can already say that users are more demanding today. Today, no one wants to wait long for reports. And there are many more areas of application that benefit from good evaluations. Users are also becoming more imaginative.

Thus, the number of requested evaluations is increasing, mostly with the desire for availability in real-time.

#8. Is It Even Worthwhile For Businesses To Modernize An Existing Data Warehouse?

As we can already see that larger unstructured data volumes, more users and queries and changed research modes are pushing older data warehouses to their limits. 

But it’s always worth taking stock and defining the new requirements for a modern data warehouse. In most cases, it’s not about a radical new start. It’s more about complementing existing solutions and architectures. There are many new tools to extend a data warehouse, even for small companies.

#9. Does A Lot of Data Mean A Lot Of Knowledge?

The largest amounts of data don’t help at first. They have to be made available and usable, and only then can you achieve targeted results. In the past, evaluations from the data warehouse were mostly reserved for management, but today there are many more users from the business departments.

Not only in control, but also increasingly in sales or making or even in production. In fact, there is no area that can no longer benefit from it.

#10. How Can A Data Warehouse Consultant Help Businesses?

A data warehouse consultant can bring their many years of experience and expertise to businesses who are new to deploying the system or want to improve the performance of an existing system. They can not only help you adopt a new system but also can help you in testing and implementation. 

A data warehouse consulting company will accompany the project or even take over the project management. For many businesses, data warehouse services have been playing a supporting role for many years.   

Finally, data warehouse consulting provides a link between the requirements of the business users and the IT. 

Contact our data warehouse consulting service to bring the best out of your customer’s data.


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How to Start Using Artificial Intelligence at Your Company

So you want to start using AI at your company. Now what? 

First, evaluate if it has an appropriate place in your company. Many organizations hire a data scientist or an entire AI team with an anticipation of a fast, massive, magical gain. Even though by now most people realize that these expectations are naive, the general public and even venture capitalists are still attracted to the idea of miraculously making everything better with AI. After all, it is tempting. 

When deciding whether or not to start using AI at your company, realistically consider how much real value it might bring. There are two questions that you should be asking yourself. First, what problems will it help me solve? Second, do I have or can I obtain large quantities of clean data to enable it? 

In order to move forward, you need to have a clear answer to the first question and a positive answer to the second question. Consult with an expert and formulate your use cases. Consider the data that you have, or might start collecting, and the level of expertise and bandwidth of your existing employees. Some straightforward and small-scale AI systems are easy to build with automatic Machine Learning tools, provided the problem statement is clear and you have relevant, abundant, and clean data. 

These off-the-shelf systems can help generate the momentum needed to prove that AI can bring value to the company and convince stakeholders that investing in it is a prudent decision. You will still likely need someone who is well-versed in machine learning and data, but they do not have to be an AI guru, and you definitely do not need an entire team. Most of the effort in the case of a small-scale project is typically focused on generating, cleaning, and maintaining the datasets that the AI is learning from. For a larger and more complex AI system, you would need to grow your team.

A common pitfall is to keep hiring data scientists. After all, they may have proven the initial value of your AI, but at the growing stage, you’ll need to invest in other roles as well: data engineering, data infrastructure, and, potentially, an in-house ML engineer. If you hire data scientists without adequate engineering support, you will be left with many concepts that never become products. 

Another important component of building and productionizing successful AI at a larger scale is leadership buy-in. Without support from the top, projects will get stifled. After an AI project has been prototyped, there is still a long road ahead towards a production implementation. This will require contributions from engineering, product, design, QA, and other teams. If leadership is too focused on the current operations and short-term gains, and not on the long-term benefits of the automation and predictive powers that AI provides, no large-scale project can be implemented. 

Bringing Artificial Intelligence into your company is no easy feat and can easily lead to wasted time and effort. But with a clear objective, abundant and clean data, and a mindfully built team with leadership support, AI can transform your company.


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Data Warehouse: What Is It Used For, Types And Advantages In Businesses

Data warehouse is essential to further improve the efficiency and profitability of your business.

With the wealth of information available online at any given time, companies are developing a culture of data to improve decision-making and develop more effective actions.

To enable this, you need to collect, organize and analyse this information. Before you can do this, you need a system to store and aggregate the data collected, such as a data warehouse, but what is it?

What Is A Data Warehouse?

A data warehouse is a data management system designed to enable and support business intelligence activities, in particular advanced analytics. They are used exclusively to perform advanced queries and advanced analytics, but they can also store more historical information about the business and can include process logs, among other things.

This allows all information to be organised in a way that provides companies with very useful data to develop better strategies to improve business performance.

The data warehouse is therefore one of the largest sources of information within the company.

Data Warehouse

What Are The Main Characteristics Of A Data Warehouse

A data warehouse is characterized by being an active system of data mining and processing to meet specific purposes.

It is different from data lakes, which are repositories of unstructured data at low cost and without a particular application.

Among its main features, we highlight:

In a Data Warehouse, relational data from transactional systems, business-oriented applications and operational databases are compiled:

  • The data must be of quality and organized
  • Enables faster queries, thanks to local storage technology
  • Can generate reports in batch, as per the Business Intelligence (BI) concept 
  • End users are generally data scientists, business analysts or data developers.
  • The elementary architecture of a data warehouse is based on different online or networked data sources.

From these, a so-called “datastage area” is implemented, in which information is collected and filtered – and also where redundancies are eliminated.

This area is interconnected to a data mart, whose function is to perform a new data filtering to send it to the tools used by the end user.

What Are The Types Of Data Warehouses

Although the structure of data warehouses varies from company to company, they can be broadly classified into four types.

In other words, depending on the intended use of the data, they can be organised as a data warehouse into one of the following types – some even combine these four models.

1- Integrated

The primary function of an integrated data warehouse is to create consistent relationships between data from different sources.

They can consolidate information from different systems so that it can be further processed in a single system.

2- By Subject

On the other hand, data warehouses organised by subject are those that meet business objectives in a given context.

For example, an accounting department that has to register and record various customers and taxpayers, as well as the taxes to be calculated and collected.

2- Variable Over Time

For data characterised by variable over time, data mining sources that use one or more time periods as a baseline are used.

Therefore, data mining is not used in real time, like OLTP (online transaction processing) banks.

4- Non-Volatile

Data in data warehouses is always ready for further processing.

This means that they must go through deletion and retrieval processes where they are modified before being used by the end user.

This makes them static, i.e. non-volatile.

But What Exactly Is A Data Warehouse For?

Like a warehouse, a data warehouse helps to bring together or integrate data from different sources for easy use by business managers and data analysts.

These main sources include ERP, spreadsheets, CRM and others. Information can be extracted from these sources in a variety of formats, including database languages such as SQL, XML, TXT and many others.

Once extracted, this information is stored in a repository that is reserved exclusively for data standardization and even business quality assurance processes, which brings many benefits to the business organization.

What Are The Main Advantages Of Using Data Warehouses In Businesses?

Now that you understand what the data warehouse is and its types, and what it is used for, we will point out the main advantages of having a data warehouse in businesses.

See what they are:

  • Agility in queries: data warehouse systems are not only capable of storing data, but are a complete solution for companies that frequently deal with information.
  • Increased data processing capacity: with the expansion of cloud computing, the storage and processing capacity of data warehouse systems has been greatly increased.
  • Access to historical data: when it is necessary to have a historical reference to perform an online operation, data warehouses prove to be even more valuable as they work with OLTP systems.
  • Centralization of data: another important advantage is that they operate from centralized data compiled in a single repository.

Basic Elements Of The Data Warehouse

Below we can see the basic elements that make up the architectures of a Data Warehouse.

Data Stage

Composed of a storage area and a set of processes. Its function is to extract data from transactional systems, proceed to cleaning, transformation, combination, duplication and preparation of data for use in the Data Warehouse. This data is not presented to the end user.

Presentation Server

Environment where data is organized and stored for direct consultation by end users. Typically data is available on these servers in relational databases, but can also be stored in OLAP technology (OnLine Analytical Processing) since many data marts work only with data in the dimensional model.

Data Mining

Also known as data mining, Data Mining works on large masses of data where there are many correlations between the data that are not easily noticeable. Data warehouses usually consist of huge amounts of data, there is a need for a tool to automatically scan the data warehouse in order to search for trends and patterns through pre-defined rules that would hardly be found in a common search.

Data Source

Transactional systems of the company can be composed of various forms of data.

Data Mart

Logical subset of the Data Warehouse, usually divided by department or views needed by users.

What Is The Difference Between Data Warehouse And Database?

From everything we have seen so far, we can say that the data warehouse is an information system that stores historical and relational data from single or multiple sources.

It is designed to analyze, report and integrate transaction data from different sources.

DW facilitates the analysis and reporting work of a company and is also the primary source to guide the decision-making and forecasting process.

The database is a collection of related data that represent some aspects of the real world and is designed to record such elements.

So, can point to some differences between these two resources:

  • The database is designed to record data, while the data warehouse is designed to analyze them.
  • The database is an application-oriented data collection, while the data warehouse is the subject-oriented data collection.
  • The former uses Online Transactional Processing (OLTP), while DW uses Online Analytical Processing (OLAP)
  • The database is designed using Entity Relationship Diagram (ERD) modeling techniques, while the data warehouse uses data modeling techniques to design.

What To Expect From The Data Warehouse?

With the increasing integration of business intelligence, machine learning and artificial intelligence solutions and functions, the future trend of data storage will become more intuitive.

This can be expected from the new concept of Data Warehouse 2.0, where the most advanced architecture treats data as if it were in a lifecycle.

The growing use of cloud computing is also a very strong trend.

Enterprises are turning to cloud storage technologies for efficiency, security, scalability and ease of use.

In the future, data warehouses are expected to become true integrated analytical ecosystems.

Analytics processes and projects will be based on different types of data (transactional, event and reporting data) from business systems and databases, as well as from big data sources.

Therefore, in the future, data from data warehouses will need to be integrated into the analytics ecosystem and work with the data warehouse to provide the complete data set required for analysis.

Conclusion

In this article, we have learned about the uses, advantages, types, definitions, features, and key elements used to build data warehouses for business.

Want to put this knowledge into practice but don’t know how? We can help.

ExistBI’s data warehouse consulting services are ideal for companies that want to create a data warehouse that meets their goals.

Whatever your goal, we can help your business from start to finish with processes to improve market analysis.

Contact us: we are always at your service.


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Learn How Data Integration Can Fuel Your Business Growth

Data is increasingly becoming the digital currency of businesses. To stay competitive and optimize processes and applications, information must be collected, evaluated, and used. This is where Data Integration comes into play as a solution. It ensures that information is not scattered across different business units, but is centralized and always available.

Data integration is about combining diversified data from different sources into one clear picture. Data Integration facilitates the evaluation and use of corporate data to achieve business goals and optimize business processes.

Implementing Data Integration Will Ensure Data Integrity And Consistency

Integrating data sources with existing systems is part of the daily routine of almost every company. It plays a particularly important role in the digitization of business processes. 

Businesses get a lot of valuable data from their websites, social media channels and email marketing campaigns. The goal is to get a 360-degree view of customers and learn as much as possible about them.

In Data Integration, Companies Aim To Provide The Following:

Business Intelligence (BI)

Business intelligence is the process of evaluating existing business data and making informed, market-oriented business decisions. BI is the final step in the data integration process where all existing data is standardized and systematically processed.

Master Data Utilization

Customer data arrives at a company through various channels such as sales, marketing and customer relationship management (CRM). Therefore, it is important that all data is created in a consistent way and is available to all business units. This ensures that everyone knows exactly which customers they are working with.

Data Warehouse

A data warehouse is a collection of data used by employees to visualize, evaluate, and use company data. For this purpose, data is stored centrally and created in a consistent manner. A data warehouse ensures that all business units have access to one system and one repository of information.

Different Ways To Create Data

There are several ways to retrieve data from a data warehouse:

Uniform Access Integration

Used to visually standardize data records. The information remains in its original location but is displayed in a uniform way on the front panel.

Manual Data Integration

Data is manually collected by staff and transferred to the data warehouse. This process is time consuming and only suitable for small businesses with small data sets.

ETL (Extract, Transform and Load)

ETL (extraction, transformation, loading) is a sub-process of data integration. In this process, data available in a source system is extracted and transformed so that it can be loaded into a data warehouse.

Middleware-Based Data Integration

This form of data integration is best suited to the use of legacy databases and systems. It acts as a middleware adapter that enables the use of this data in modern applications.

Common-Storage Integration

Unlike uniform access integration, in this approach data does not remain at the data source but is copied to a single data warehouse.

The Goals Of Data Integration Are:

Digitization of sales means that companies have more data. Data comes from a variety of sources, including customer surveys, questionnaires and sales data, each with different characteristics. Some of this data includes traditional business data:

  • Product Data
  • Market Data
  • Customer Data
  • Sales Data
  • Safety Data
  • Environmental Data

Because this data is used in different ways by companies, it is typically stored in different places. Over time, so-called data silos are created, where a company’s data resides in different places but is not linked together. This makes it difficult to evaluate and use the data for business purposes.

This is where data integration comes in, combining business data from different sources into a single entity. The result is a high level of data integrity and consistency, meaning that the data is accurate or reliable.

Here Are Some Of The Benefits Businesses Can Gain From Data Integration

Data Integration brings many benefits to enterprises:

  • Data is centralized and standardized, so there is no separate data.
  • Data integration can save your company a significant amount of time and resources.
  • Using complete and accurate data reduces the likelihood of errors and poor decisions.
  • Data integration ensures reliable, understandable and meaningful information and business decisions.
  • Workflow can be improved by ensuring that all departments are using the same data sets.
  • Your business achieves a high level of data completeness or consistency, and the information available is accurate and complete.

Here Are Some Of The Challenges That Companies Face In Managing Their Data

Companies face many challenges when consolidating data. First and foremost, they need to understand how to leverage existing data sets and integrate existing systems. They also need to evaluate information from different data sources, such as cloud, video, and sensors, which require different approaches. Real-time data analysis is becoming increasingly necessary.

In addition, there is a need to differentiate between internal and external sources as each data set has different characteristics and presentation formats. Data integration systems need to constantly improve and adapt to market changes.

Data Integration Is An Ongoing Process

Data integration doesn’t stop there. New data and new systems are constantly appearing in the market. Therefore, the concept of data integration must be improved to ensure that data sets are continuously collected, analyzed, and used.

We also need to encourage employees to integrate data effectively to avoid data silos and to ensure consistency and integrity.


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Learn The Top Business Intelligence Tools SAP Has To Offer

Business Intelligence is the collection and presentation of data related to a company’s strategic planning and decision-making processes. The information gathered in this way allows an objective and understandable basis for the company’s future strategic plans and operations. SAP offers different business intelligence tools for using BI on the SAP Business Intelligence Platform. SAP’s corresponding business intelligence platform is SAP BusinessObjects.

In it, all SAP users must use self-service tools to collect, evaluate and visualize data to use the information to drive their success. Each tool is tailored to different purposes and needs.

SAP Business Intelligence Tools

What Are The Benefits Of Business Intelligence?

Business Intelligence supports the company’s decision-making process by providing an overview of all relevant data and presenting it in an understandable form. In this way, decisions can be made faster and justified on a sound basis.

In general, SAP offers a range of BI tools for the intelligent evaluation of business processes. It is recommended that you apply these tools according to your company’s needs and use them in your decision-making process.

This will make more rational and faster decision-making and lead to a better understanding of all business processes. This overview can lead to optimization of existing strategies and processes and ultimately to the success of new ventures.

How Does BI Works?

The collection of business performance information includes past and current business data. The data is extracted and transmitted to the company’s so-called “action points” from this database. The knowledge gained here can be directly applied to maximize the business’s success.

Data can be collected through OLAP (Online Analytical Processing) aggregation or mathematical analysis using data mining techniques. This has many business benefits for management and business units. All necessary information is available in real-time. Data must be presented in a way that all relevant employees can understand and use independently. It is also structured to be scalable and flexible. It is therefore accessible from any location, platform, and device.

BI is neither a predictive tool nor a simple reporting tool. In many cases, generating data has many benefits. For example, if a company is experiencing delivery problems, BI can show which products are most affected by delivery delays. It can also indicate which parts of the customer journey have been particularly successful and which factors have involved staff turnover.

However, BI can also be useful for organizations that are not financial. Schools, for example, can use BI to optimize their school systems by linking student attendance to results.

Top SAP Business Intelligence Tools

Many essential business intelligence tools are available from SAP BI platform, which popularly known as SAP BusinessObjects. Here, all SAP users should be able to use self-service tools to collect, evaluate and visualize data to use the information according to their different purpose and needs.  

SAP Business Intelligence Tools

Below are the top SAP business intelligence tools you would love to try this year:

SAP Cloud

SAP Cloud is an analytic tool that connects BI products to the cloud and enables predictive analytics and planning. It also facilitates the processing and management of complex data through centralized use.

SAP Lumira

Lumira is a self-service BI tool that combines multiple sources for analysis. Visualization is done by drag and drop. In the free version, you can open CSV and Excel files. If you need to import SAP HANA or SAP BW, you need the paid version of Lumira.

SAP Crystal Reports

SAP Crystal Reports allows users to create reports from data sources or text files and format them according to their preferences. This includes filtering, sorting, and categorizing to get a clear picture.

SAP Design Studio

Like SAP Crystal Reports, Design Studio is a tool for creating interactive dashboards and data analysis and visualization applications. The following data sources are available: SAP BW, SAP HANA, BO Universes, and CSV files.

SAP Web Intelligence

This tool is described as easy to use and fully functional. Data and ad hoc reports are available online. It is a flexible tool that can be used anywhere and everywhere.

SAP Digital Boardroom

SAP Digital Boardroom is a tool specifically designed for top executives. Real-time information, best practices, and intelligent meetings are ideal for making crucial business decisions.

SAP Roambi

SAP Roambi is SAP’s next step towards mobile analytics. By extending the functionality to mobile devices, you can use business information more flexibly, no matter where you are.

SAP Business Intelligence Tools

The Difference Between Business Analytics And Business Intelligence

Since business intelligence and business analytics are concerned with the generation and presentation of data, the two terms are often used synonymously.

The reason for this distinction is that business intelligence is descriptive and analytics is prescriptive. In other words, BI deals with describing current data and situations, while analytics deals with predicting the future.

For BI, this means that only the actual condition on the dashboard is described. All conclusions about future behavior must be drawn independently.

Conclusion

Business intelligence is the smart improvement of business performance. It is unlikely that this important goal can be achieved through manual processes because the business itself is a huge concept.

Advanced business intelligence reporting tools make this task achievable and straightforward. Business intelligence platforms change with the dynamics of business needs and technology, but they are by far the best way to achieve business goals.


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How to Analyze Data with Power BI Training

Microsoft Power BI suite has powerful services and tools, which facilitate businesses with a deeper understanding of business data by providing robust data analytics and visualizations. Encompassing Power BI will ensure that data does not stay in the large databases that can never be used. When you start Analyzing Data with Power BI Training, you’ll learn various Power BI integrated solutions for varied data sources and visualization types.

The most important advantage of Power BI is that it helps you quickly discover the insights buried inside your data. It enables you to find answers to your most important business queries in minutes. It supports a broad range of data sources, such as Databases, Flat Files, Data Feed, Blank Query, AZURE, online services, Cloud platforms, and other data sources like Hadoop, Active Directory, or Exchange.

Analyzing Data with Power BI Training

Here’s an overview of how it will help in data analytics:

Understand Your Data Quickly

Convert rows of data into visualizations that help you understand the big picture of data at a glance.

  • Identify patterns and trends faster
  • View the big picture
  • Illustrate insights in a way everybody can understand
  • Discover insights amongst numerous datasets from different sources

Discover Opportunities and Risks

Find out opportunities for more effectiveness and identify possible risks before they impact your business.

Visualize all of your data in a single view

  • Build robust, reusable, live models
  • Carry out “what if” analyses visually
  • Discover trends visually and respond quickly to real results
  • Get a deeper understanding with drill-downs and real-time data

Make Data-Driven Decisions 

Make decisions based on data, not on opinions, and share reports and dashboards to get every person on the same page so your team can proceed with confidence in the right direction.

  • Forecast the results based on diverse options
  • Use visuals to refine the most relevant information
  • Split the data silos across the organization
  • Analyze data in a way that provides understandable insights
Power BI Training

Why Should You Choose Power BI?

With Power BI, you can rely on one of the largest and quickly growing business intelligence tools. You can generate and share interactive data visualizations across international datacenters, comprising public clouds to fulfill your compliance and regulatory needs. Explore the key reasons why organizations should choose Power BI to meet their self-service and business intelligence (BI) needs.

Unify Self-Service and Business analytics

Microsoft Power BI helps you to meet both your self-service and business data analytics needs on a single platform. You can access influential semantic models, an application lifecycle management (ALM) set of tools, an open connectivity framework, and pixel-perfect paginated reports in fixed layouts.

Faster Big Data Prep with Azure Integration

Analyzing and sharing massive volumes of data is made easier. You can utilize Azure Data Lake with unlimited storage to trim down the time for leveraging insights and enhance collaboration between business analysts, data scientists, and data engineers.

Discover Answers Faster with Industry-Leading AI

Leverage benefits of the advanced technologies with Microsoft AI to assist non-IT users in preparing data, creating machine learning models, and discovering insights rapidly from both structured and unstructured data, also from texts and images.

Better Publishing Competence and BI Content Accuracy

You can quickly find out differences and transfer content from development and testing processes to production confidently by exploiting the simple visual signs in deployment channels. This way, the effectiveness in publishing data and the accuracy of BI content will be greatly improved.

Power Bi

Get Supreme Excel Integration

If you know how to use Office 365, then you can easily connect Excel queries, data models, and reports to Power BI Dashboards. It will help in gathering, analyzing, publishing, and sharing Excel business data faster in new ways.

Convert Insights Into Action With Power BI Training

With the Microsoft Power Platform, you will transform data into insights and insights to actions by its Power Apps and Power Automate to effortlessly develop business applications and automate workflows. So you don’t need to make efforts to understand data after attaining actionable insights.

Conduct Analytics in Real-Time

You can just find out what’s happening now, which didn’t occur in the past with real-time analytics. Right from factory machine sensors to social media sources, you’ll get access to real-time analytics, so you always stay ready to make correct decisions in a timely manner.

Key Benefits Of Using Power BI

Improve Productivity: Power BI lets the end-user drive data and generate reports by allowing the conversion from static data representation to an entirely interactive and dynamic user experience. It helps the customer to recognize their business performance and objectives.

Grow Sales and Market Intelligence: It helps businesses to gain new customers and services and track current customers and makes the decision-making process better.

Track and Set-Up Goals: With Power BI, the user can keep track of the available information and establish their goals according to the existing information.

Get Insights into Customer Behavior: Improves the capability to evaluate the existing consumer’s purchasing trends, so the organization can design products for utilization and facilitates real-time analysis with fast navigation.

Better Return on Investment (ROI): When you have a better strategic understanding, quicker reporting capabilities, it decreases the operating costs and lowers overheads that help in increasing ROI.

Convert Data into Actionable Information: Power BI System is an ideal tool for data analytics that presents insights that end-user needs to produce a successful strategic plan for their organization.

Final Thoughts

Power BI is an industry-leading platform, which helps you connect to and visualize any type of data. Its unified, scalable tools for self-service and enterprise business intelligence let you access data easily and help you achieve in-depth data insights. So if you are looking for a BI tool to meet your business needs, there is nothing that can work better.

Are you ready to leverage the above benefits with this platform? If yes, you need to learn different aspects of using this tool for various use cases. To do so, you must provide your employees with the necessary training, helping them to save their efforts and get the work done more effectively. ExistBI offers Microsoft Power BI Essentials Training throughout the United States, United Kingdom, and Europe.


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Best Practices for Data Governance Services to Lower Risk and Drive Value

Modern business leaders, including chief data officers, line of business owners, and other enterprise data stewards, are always asked to discover more value from existing data. Obviously, the most important insights often come from consuming data that is valuable in itself, presenting a target for exploitations internally and possible ex-filtration of bad performers, if not governed correctly. The overall objective of Data Governance Services is to set up correct rules and provide the required security to reduce violation risks.

Keeping the data safe and suitable for its use without breaking consumer and data owner trust assurance is often the main hurdle when approving digital transformation programs to move ahead. It’s improbable that business leaders will get permission to unleash personal information or similar IP if they have no knowledge of how exposure can be a liability or produce the expected ROI.

Data Governance Consulting

Data Governance is Your Key to Speed up DX, CX, and Reduce OpEx

A data governance program for overall enterprise is your key to speeding up digital transformation programs, such as cloud migration, making the customer experience better with trust assurance, and reducing operating expenses when data use is optimized, aligned with your business policies.

In today’s world, when more data is available from multiple sources, it is no surprise that companies seek an automated and scalable method to manage this data. Data governance is a regulation that includes the policies, roles, rules, responsibilities, and tools you put in place to make sure our data is correct, reliable, complete, available, and secure to enable trust in the results you try to achieve.

Here are three best practices in data governance to maximize the success of business transformation programs, decrease uncertainty and ensure safe and proper data use.

Improve the quality and consistency of sensitive personal data to provide a 360-degree view of your customer 

There are many procedures and strategies implemented to get this done. You need to know what a customer buys, how the payment is done, and in which mode. As a result, you can obtain identity information, such as name, shipping address, type of item, size ordered, and many more details about the purchaser. You can also compare and link records. After linking records, you can have a more comprehensive view of your customer, improve data quality and facilitate more appropriate and actionable use by closing gaps in dependability for better insights.

Data Governance Services

Ensure a reliable set of definitions, terminology and metadata

Data governance ensures the utilization of consistent, standard naming conventions and clean, brief definitions of data elements. How can you do this? It is done by combining together the right stakeholders, such as data stewards, business leaders, data architects, and others who need to interact in a common language and develop transparency within the workflow for codifying policies to operationalize and automate data governance controls.

Protect against illegal access to data and govern right uses

We all need to address and fulfill data privacy regulations around personal data use that are progressively more mandated by law. Keeping regulatory compliance to one side, industry statistics reveal that improving trust assurance that strengthens loyalty and confidence in insightful data use is a priority for businesses.

Again, data governance plays a vital role in assuring all companies can identify their confined data, using data discovery and classification procedures. It also helps to set processes, policies, and enforceable operational controls to make sure the security and privacy of that data are approved for access and suitable exposure.

While these are the basics, there is plenty of nuance and room for arguments as to how this can be done most proficiently with limited time and resources. Sometimes, this requires depending upon automation through AI and machine learning to speed up insights, get more with fewer resources and improve data governance plans that optimize results based on business goals for data usage.

illegal access to data

How can Informatica help?

Leading international organizations are leveraging integrated and intelligent Data Governance and Privacy solution from the Informatica range to proactively add value to their results. They provide the right data to the right people at the right time, facilitating the entire organization to be practical, in order to recognize and take action on new opportunities and plan for the best outcomes, instead of responding to unexpected surprises.

Governing data is a responsibility that resides with every individual in an organization to lessen risks by handling data sensibly, resulting in a clear necessity for common solutions and governance models to guard and share data on different levels through every company.

As part of any company’s success, everyday interactions with business-critical data are very important. Every person within the organization is responsible for data protection while unlocking new opportunities in parallel. If you have an improved solution, knowledge of a more competent way to manage the data, or discover a barrier to resolve, you should feel empowered to proceed with that information. Then, you can really make a difference!

Start to Accelerating Data Governance and Privacy Best Practices to Optimize Results

A common question for the people who want to get started with data governance is, “where to initiate for the maximum impact?”

Whether beginning a new program within an organization that is short of maturity or selecting an inherited collection of elements and parts from a forerunner, it depends on the responsibilities and business model of each organization for understanding data and setting up the most appropriate data controls. Even with changeable schemas across industries and organizations, there is a reliable set of best practices that can help to avoid missteps and make the most out of budgets and resources.

Having a rich legacy of data management solutions for more than 25 years, Informatica has developed a wealth of experience, implemented globally across top organizations. Whether securely revealing data in a marketplace, transferring workloads to the cloud with fewer risks, supporting customer loyalty programs with stewardship best practices for trust guarantee or beyond, Data Governance Services can really help you get success with reduced risks. ExistBI has Data Governance consultants in the United States, United Kingdom, and Europe who can help you navigate this journey successfully, contact us today.


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Explore Guidelines in Informatica Training for Data Security and Privacy

Learn How Mandatory Security Policies Empowering Data Privacy and Protection

All the businesses and organizations now have to follow Data Privacy and Security rules presented by consumer policies, like the GDPR (General Data Protection Regulation), the CCPA (California Consumer Privacy Act), etc. With an increasing number of data breaches and social media privacy abuses today, the need for data privacy and protection has become a high-level concern.

By Informatica Training, you will know how Informatica uses IT solutions to confront future data challenges with ease and security that save businesses from data privacy violations.

Consumer Policies- Data Privacy and Protection

  1. Membership in IAPP (International Association of Privacy Professionals) has made a growth dramatically with 60,000 privacy professionals all around the globe and IT users have shown immense interest in privacy events.
  2. The GDPR came into effect on May 25 of 2018, since this time new legislation is emerging from numerous nations and states, influencing IT professionals worldwide.
  3. California has brought a new act, the CCPA, which is going to take effect on January 1, 2020.
  4. The European Commission and the Japanese government have also published a shared declaration on July 6, 2018, about international transfers of private data.
  5. Across the globe many countries are creating policies and regulations, with the United States have shown their great interest in creating and acquiring a national uniform privacy law for data security.

Learn Data Protection with Informatica training

Informatica is currently serving the clients with a positive data security policy and regulations in the framework to help them maintain the workflow with a steady and scalable approach. The boards of such businesses identify the worth of data privacy and protection to maintain customer trust and stay competitive by taking a safe value concept initiative in the course.

The GDPR, CCPA, and other policies have produced a great storm and have brought a break for the organizations to maintain their data privacy and protection thoroughly by escalating data governance best practices. Organizations do need to understand that delicate and susceptible data they embrace is proactive to risks and need solutions to remediate risks, observe data threats and manage privacy rules.

Informatica presents an approach called Intelligent Data Privacy to engage in the systemic data privacy and protection framework, having the potential to evaluate, guard and manage personal and delicate data across the organization.

Enterprises need to analyze, manage, protect and assess security constantly with a trusted end-to-end data privacy approach to:

  1. Identify and manage Governance policies
  2. Determine and categorize personal and sensitive data, and recognize their usage
  3. Link identification to individual delicate data for intelligence
  4. Evaluate the data’s risk and present the solution
  5. Enact intelligence to protect data, manage subject rights and permissions
  6. Calculate, communicate and Review data preparation

The only reason behind putting all the regulations and policies in the data governance is to ensure data privacy for delicate and sensitive data within the business environment. The current state of affairs of data governance indicates the rapid growth of such policies in the future to protect personal and sensitive data.

Informatica’s Data Privacy and Protection solution leverage organizations to improve their data privacy and protection and recognizes new and existing data assets.

Join the Informatica classes that will help you to analyze data risk across the organization, identify sensitive data, recognize the value of data protection and automate protection workflows for security teams. Enroll in today, ExistBI is Informatica Partners and offers Informatica training and Informatica consulting in the US, UK, and Europe.


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Reasons to Leverage Business Intelligence – Microsoft Power BI Consulting

Business intelligence and the capability to develop actionable insights from your data are essential for any organization striving to be agile, future-ready, and outperform its competitors. Almost all organizations develop a Business Intelligence strategy to drive better insights. Microsoft Power BI Consulting helps clients to plan a comprehensive strategy for improving their performance by leveraging the available data.

As companies enlarge and develop, it can become more difficult to manage data on a constant basis. As an outcome, businesses repeatedly face obstructions that stop them from performing a broad analysis of their data to drive well-versed business decisions.

Whether it is unsuited systems that prevent the sharing of data, or the silo approach fostering conflicting goals and completely different reporting structures, it’s common for organizations to be lured into action based on insufficient data that doesn’t give the full picture. However, there are some modern and powerful enterprise tools that can help.

Microsoft Power BI- Leading BI Software

In 2019, Gartner forecasted that the BI market will grow up to US$20 billion. With radical growth from 2020-2025.

There is an overabundance of business intelligence software and services you can currently leverage to collect and manage your data more effectively, make information accessibility better across your company, and eventually help sustain more accurate reliable results.

Microsoft Power BI is one of the trending BI tools and is a leader in its field for its cost-effective model and wide-ranging analytics capabilities. It’s confirmed to facilitate significant cost savings and improved productivity, with numerous high-status international companies using the software.

Top 10 Reasons to Have Power BI within Your Organization

Whether it is interactive dashboards to combine key metrics or affluent reports to join datasets from workloads, Power BI is a key tool to connect with business data, draw it from a wide range of different sources and facilitate smarter data-driven decisions.

Apart from many other benefits of Power BI, it also provides features for data preparation and discovery, interactive dashboards, and valuable visualizations in a single solution with its self-service features that make it an intuitive tool for cooperating with data and transforming it into insights more easily.

Microsoft Power BI Consulting

Here are the following 10 reasons why you should choose Microsoft Power BI to fulfill your business goals and facilitate smarter insights with better efficiency, why you should start using it within your organization to bring business intelligence.

1. It’s Easy to Combine Your Data Together

Power Bi presently holds up to 70 plus connectors out-of-the-box, allowing businesses to load data from a broad range of frequently used cloud-based sources such as Azure Data Warehouse, DropBox, Google Analytics, OneDrive, and SalesForce other than Excel spreadsheets, CSV files, and data available on-premises like SQL Database.

You can always customize components further to your preferences, or have your data experts begin from scratch by transferring your datasets and building your own dashboard and reports.

The drag-and-drop interface available in Power BI also means you don’t need to code or copy and paste anything to get started and it can join multiple files, such as Excel spreadsheets, and enable you to evaluate the merged data in one report.

2. It’s Powerful and High Performing

Power Pivot data modeling engine in Power BI is an extremely performant columnar database, making use of present tabular database technologies to compact databases and make sure they load completely into memory for optimal performance.

It’s common for your Power BI Workbook (.PBIX file) to be considerably smaller than your original data sets. Actually, 1GB databases are generally compressed down to about 50 – 200MB in size.  In comparison, while Excel starts to reduce speed when dealing with large models, Power BI is optimized to tackle tables with more than 100 million records without making much effort.

Power BI also deploys automated, incremental refreshes and ensures data is always updated, an important advantage that additionally streamlines visual reporting for end-users.

3. It has Custom, Open-Source Visuals

Power BI has a vast amount of pre-packed typical data visuals to utilize in your interactive reports, such as bar, column, line, map, matrix, pie charts, table, scatter, and waterfall, each one with its own diversity of customization options for better presentation and functionality.  For extra impact, you can also utilize free custom visuals built by developers and shared with the Power BI community to signify your data in a way that supports your data story the best.

With custom visual files presented by both Microsoft and the community over at the AppSource Marketplace, there’s a remarkable range of affluent and complex visuals to take benefit from, comprising bullet graphs, correlation plots, sparklines, decision-trees, heatmaps, and more.

If you want to show your data in a very precise way, Power BI makes it very easy to generate your own visuals rather than being stuck with the standard ones. It is also extremely helpful to view and use what the wider Power BI community is using to enhance your own design techniques.

4. Qualified Data Experts can Use Its Native R Integration

The core strength of Power BI is its simplicity, but it also empowers advanced data experts. One way it attains this is through its integration for R, an open-source programming language that presently has over 7,000 packages and is mainly used by data miners and statisticians.

R scripts utilize compound graphical techniques and statistical computing for data exploitation, machine learning, and statistical modeling. It includes data visualization and as expected, Power BI enables you to integrate these detailed R visualizations straight into a typical dashboard.

Power BI is vast on its own to explore data further and slice it down for displaying relationships, key metrics, and hierarchies in a better way. But with its native integration with R scripts, users can present more advanced business analytics and shape such as machine learning, predictive trends, and smoothing.

Microsoft Power BI Consulting

5. Enable more Advanced Analytics with Common Excel Features

Advanced Excel users are well-informed to Data Analysis Expressions (DAX) formula language that can mine deeper into data and discover patterns easier with Power BI, with its well-known Power Pivot features such as; clustering, forecasting, grouping, and quick measures.

The integral self-service Power Query tool will also be recognizable to Excel users, making it easy to ingest, integrate, modify and enhance business data in Power BI from the get-go.

One other simple benefit is Power BI flawlessly integrates with Excel, opposing the need to export files, just click on ‘Analyze in Excel’ and Power BI provides an interface nearly identical to Excel. If you’ve had issues getting your business users to transition to a new tool, Power BI’s native integration of Excel can’t be overlooked.

Overall, the influential toolset of Power BI will be easy to lift for MS Excel users, enabling you to empower in-hand organizational expertise and ease into Power BI quicker.

6. It Brings Data Governance and Security Together

Power BI allows you to deal with security and user access and security within the same interface, eliminating the requirement of using other tools to make sure you meet strict compliance and regulatory standards.

This service also encompasses Azure Active Directory (AAD) built-in for user authentication, letting you empower Single Sign-On (SSO), along with your general Power BI login credentials to access your data.

7. You can Ask Queries and Get Answers about Your Data

Power BI includes natural language search interfaces to enable users to generate visualizations and determine insights using search terms in simple English, without requiring any code or syntax.

Using the Q&A functionality, you can discover more specific insights by double-clicking an empty part of your report image and using the ‘Ask a Question’ section to ask data-specific queries.

The mobile Power BI applications also support a voice recognition system for Q&A, enabling you to ask for information on the go.

At first, it may sound like a gimmick, however, the Power BI’s natural language query engine is very spontaneous and works enormously well. And with regular updates from Microsoft, it can only evolve and become more precise with time.

Microsoft Power BI Consulting

8. You can Embed Power BI Tiles In your Custom PowerApps

Do you utilize PowerApps? If so, you can make use of Power BI custom visuals to insert your Power BI tiles within your app.

If you are not familiar with PowerApps, it is a powerful enterprise tool used to produce business apps that perform on approximately all Web browsers and operating systems such as; Android, iOS, and Windows. It is an easy interface that doesn’t need coding experience, resembling usage to Power BI.

Having native integration between these services means it is even easier to share important insights with employees using your internal custom apps without requiring any access to Power BI itself. End-users can furthermore dig deeper into the data just by clicking on the fixed Power BI tile to be shifted to its dashboard if it is public.

9. A leader in Magic Quadrant of Gartner for Analytics and Business Intelligence

Power BI has always been on the top of the list in aspects of analytics and business intelligence. It has always been recognized as a leader in Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms and is known as one of the leading data analytics software solutions for many consecutive years next to other popular competitors.

10. It Helps to Find Past Trends, Current Performance and Future Predictions

Advanced data modeling has made it possible to find out trends and predict future results comparatively correctly with modern-day software. Power BI is one such tool that offers great predictive analytics and predicting features to discover dependable future outcomes.

Using the analytics and predicting tools in the Power BI desktop, you can perform and evaluate different ‘What If’ circumstances on your information, such as financial predictions or industry-specific development markets by attaching a forecast to your line chart, all without any clear coding requirements.

It uses integral predictive forecasting models to automatically distinguish seasonality and upcoming reporting periods, such as a week, month, or year and presents forecasting results. These models gain knowledge from historical data using numerical algorithms to obtain possible results and showcase them in a helpful manner with a graphical presentation.

Why Should You Use Power BI?

Power BI is a popular Business intelligence application that empowers you to evaluate your data and make your company proficient. It provides you the tools essential for better strategic analysis of how you can merge your data streams, progress accessibility, and leverage smarter insights.

It is not difficult to understand why Power BI is rising in popularity among businesses looking for better insights, intuitive dashboards, and competent reporting. So, this is the time to leverage this amazing BI tool and its existing services, which can help you to get ready for a successful Power BI adoption and make your data insights better within your organization.

Make your business fly higher by joining the trend and utilizing the required Business Intelligence within your organization. If you want to deploy this popular tool, you will require the right guidance of Microsoft Power BI Consulting experts to implement, manage and maintain this useful tool.ExistBI has experienced Power BI consultants in the United States, United Kingdom, and Europe, contact us today to find out more.


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Why Do You Need Data Warehouse Consulting? – A Brief Discussion

As small and large businesses upgrade operations through digitalization, there is so much more information to handle and store. This includes data on customers, suppliers, operations, transactions, and more. If you have relied on manual structures or plain spreadsheets, you have probably experienced a tedious process in order to get a final report. Untangling the web of which data goes where can create reviews that are too long, unhelpful, and even erroneous.

Data Warehouse Consulting

In order to address this problem, many enterprises have opted to invest in a data warehouse. This is a system that retrieves data from various sources. This is used to have helpful data analytics with the chosen relevant information and in turn used in evaluating business decisions. Better yet, these systems do not need to be run by specialists – once installed in your operations, anyone will be able to use the data warehouse for their specific queries. This subject-focused, analysis-based structure is implemented by companies of various sizes because of the time and cost savings that they are able to achieve with this level of automation.

While many businesses have databases to store their different facts and figures, they do not have a system that can perform specific kinds of searches that will help answer complex questions and help make more accurate high-level reports. Different rows and columns of information make up the data infrastructure and thus require additional manual support to attain the necessary pieces and make the final conclusions. Using the same information, a data warehouse is able to perform queries that cannot be accomplished by regular database programs.

Of course, not everyone has the capabilities to construct a working model for themselves, and that is why data warehouse consulting has become a popular service. Professionals are able to figure out the parameters of what a company needs. They will be able to learn what functions can help solve problems and streamline information, and equip the company with the basic knowledge on how to retrieve these helpful reports for decision-making purposes. The digital transformation cannot be achieved by buying a system off the shelf, as a good fit needs to be ensured for your company’s specific needs.

Enterprise Data Warehouse or Data Lake

To know if you should look into hiring a data warehouse consultant, consider the following factors:

  1. You are an organization that deals with a large volume of data.
  2. You are an organization that handles data from various sources.
  3. You need to recover data in a quicker and more streamlined way, no matter the source.
  4. You do not have a specialist for handling the required system in-house.
  5. You would like to have detailed analytical reports for your business.

Data warehouse consulting is a great investment if you fit into these categories. Many people have chosen to install or upgrade their data warehouse system to accomplish their business goals, and it is a trend in digitization that will continue to grow. With these capabilities, you can also learn how to achieve more speed, agility, and effectiveness in your company’s resolutions and reporting.


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What Is Data Governance and Some of the Benefits?

Data Governance is a collection of components – data, roles, processes, communications, metrics, and tools – that help organizations formally manage and gain better control over data assets. As a result, organizations can best balance security with accessibility and be compliant with standards and regulations while ensuring data assets go where the business needs them most. 

Benefits-of-Data-Governance

Outcomes for better data control lead to efficient methods, technologies, and behaviors around the proper management of data, across all levels of the organization. From the senior leadership team to daily operations, governance ensures alignment by providing structure and services.

Data Governance often includes other concepts such as Data Stewardship and Data Quality. These bases help connect governance details with the data lifecycle, improving data integrity, usability, and integration. Both internal and external data flows, within an organization, fall under the jurisdiction of governance.

Other Definitions of Data Governance Include:

  • “The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” (DAMA International)
  • “A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (Data Governance Institute)
  • “The specification of decision rights and accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.” (Gartner Glossary)

Use Cases Include:

  • Creating a Data Stewardship Model at Global 2000 Company making it operational, simplifying its Data Architecture and gaining buy-in from peers and stakeholders
  • Growing governance to better balance security and availability for Financial Services firm
  • Building Data Management teams by showing how governance provides business value, greater trust, and alignment in data and metrics

Some Benefits Include:

  • Lower costs associated with other areas of Data Management
  • More accurate procedures around regulation and compliance activities
  • Greater transparency within any data-related activities
  • Help with instituting better training and educational practices around the management of data assets
  • Increase in value of an organization’s data
  • Ability to provide standardized data systems, data policies, data procedures, and data standards
  • Better resolution of past and current data issues
  • Improved monitoring and tracking mechanisms for Data Quality and other data-related activities
  • Overall enterprise revenue growth


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How Industries Automate Reporting with Tableau Software?

Undoubtedly, the future of business functioning is automation. It’s obviously practical that everyone wants to save time and money. And Tableau software is serving successfully to make the companies and government bodies automate the reporting process easier with a simple drag and drop feature. Removing the need for coding. Do you know how? If not, join Tableau Bootcamp to learn all the tips and tricks to using Tableau software.

Tableau Training

Almost every industry in the market, including agriculture, health, production retail, has recognized the value of automation in the present competitive business world. In the industry of finance, there are giant and complex algorithmic programming solutions, but now these financial associations also want to include automation for fulfilling their analytics and reporting requirements.

Top Reasons to Consider Tableau for Automate Reporting

  1. One of the major reasons is that Tableau can run programmatic automation tasks in R, C, C++, Python and Java. The harmony of such programs helps to smooth the learning curve implicated with system integration. 
  2. Another thing that makes Tableau perfect for automation is the user-friendly and easy interface with drag and drop style, which enables the users with no coding experience to contribute to having superior insights. 
  3. The last one is the enormous added values supported by the Tableau platform that ensures you save time and effort.
Tableau Training

Let’s explore here how Tableau makes it easy to automate reporting tasks:

Rest API (Application Programming Interface):

Just like a language or set of rules created for systems, these are used to communicate and give instructions to each other. Rest API of Tableau automates tiresome tasks like site management and users, workbook updates, and custom app integration, etc.

JavaScript API –

JavaScript API allows you to pick your available web applications and integrate them to get Tableau Visualizations. Pick out the dashboards from Tableau Public, Tableau Online, and Tableau Server and place them on your web page. You can employ HTML controls to maneuver and filter these dashboards.

Extract API – 

Extract API enables you to drag data into the extracts, which allows offline access also that improves performance. Data sources not supported by Tableau can be dragged into Tableau with the Extract API that makes them in a fully supported format. You can create custom scripts in Python, R, Java, C, C++ and run them on Mac, Windows, and Linux.

Document API – 

Document API helps you to modify the programming of Tableau files, create templates and transfer workbooks from test to production.

Conclusion

Tableau offers a profoundly sustained platform to automate all reporting tasks. Whether it is a large or a small company, irrespective of their size or industry, Tableau is constantly putting efforts to help every industry to make their work easier and faster. And, when it is related to intelligent business decisions, it doesn’t matter how big your company is, data analysis and reporting remain the core requirement for the smooth functioning of the organization.

If you are still not aware of the features, functionalities, use cases and best practices of tableau software, join the Tableau Bootcamp today! ExistBI provides unique Bootcamp training in the United States, United Kingdom, and Europe.


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Learn How to Make Data Management Easier by Using Catalog in Tableau Bootcamp

Sometimes, you need to transfer your data or files from one system to another or to eliminate and add a few fields in the same table. In these cases, it is tough to identify the users and affected workbooks or dashboards. It is difficult to handle the queries of the users during maintenance hours when people are unable to find the right data for analysis. In In this Tableau bootcamp we’ll highlight how Catalog in Tableau software arrived as a solution for all these problems that business users were facing.

So, if you are struggling with the same troubles in data migration and management, let your data engineers join a Tableau Bootcamp to help them learn all the tips and tricks of data management using tableau Catalog.

catalog in tableau

Whether you are a business user or IT firm, Tableau Catalog is a real-time solution for all to make more impactful and data-driven decisions and insights. It can track, manage and communicate the various updates and changes in data sources to the users by providing a comprehensive view of data in Tableau. Data users will get actionable and reliable insights that they can use for further processes.

Tableau Catalog: Track, Manage, and Commune

Eliminating all the presumptions and manual work, Tableau Catalog provides a correct and trusted view of the analytics environment. It captures the stock records from data sources automatically, builds up a connection between various data sources, analyzes content, and conveys the details about data quality to the users. Let’s check out some key components of the Tableau Catalog.

External Assets list

With Tableau Catalog, you can view the data comprised in your tableau environment easily. You don’t need indexing and configuration for processing with automatic ingestion. The External Assets Lists allow viewing an inventory of all files, databases, and tables that exist in your environment. Moreover, it also provides the tools to identify the disused data, which you can remove easily.

Tableau-Bootcamp

Lineage and Impact Analysis

Tableau Catalog helps the data users to visualize the relationship between various tables, preps, databases, columns, and workbooks by using a lineage graph. It will also help you to identify the workbooks connecting with a particular table or column and let you know about the changes in those tables. Lineage and Impact Analysis lets you know about the users operating the column, and also about the sheets or dashboards of the column.

Data Quality Warnings

When a data asset gets outdated or under maintenance, it is vital to inform users about that to avoid them from making decisions using corrupted files. You can add a data quality warning to all the data sources under maintenance, and it will be shown on all contents within that source.

Final Thoughts

The most important feature or functionality of Tableau Catalog is how it handles Metadata differently and provides powerful and actionable insights to all data users in the organization. Tableau software imparts an enhanced data management facility with better visibility, trust, searchability, and governance to organizations with Tableau Catalog.

Tableau Bootcamp will help you to learn the functionalities and features of tableau that help the business users to organize, manage and search the data more efficiently. ExistBI offers Tableau classes and Tableau consulting in the United States, United Kingdom, and Europe, contact them for more details.


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How to Handle Data Challenges with Data Integration Consultants

Data is created everywhere within an enterprise. Various sources generate different types of data in all shapes and sizes, and companies need an instant IT solution to integrate that data in an easy-to-manage way. Almost all smart organizations opt for Data Integration Consultants to deploy a data integration solution that flexibly unites the systems and applications that are leveraging critical information flows.

Data Integration Consulting

What is Data Integration?

Data integration is the process of gathering data from numerous different sources into one joined vision to make the data more actionable and valuable to an enterprise. It provides business users with constant access and delivery of data throughout different business processes to meet the information utilization requirements of all applications.

While there is not any common method that can work as a general solution for all to solve data integration needs, the majority of solutions offer a few common features, such as a data source network, one master server, and allowing clients access to data from a master server.

Benefits of Data Integration

Data integration tools powerfully aggregate data and make it available to the users who require it. There are a lot of benefits for an organization that uses a data integration solution.

  1. Various types of data require different levels of specialty that the dataset achieves. Each set of data has individual qualities for everything ranging from metadata, structure and schema. An integration solution assists all such datasets and qualities.
  2. Some dedicated applications serve specific business information needs, but they also bring new opportunities to take benefit from data in new customs. Data integration lets the users switch between formats and view data in traditional or cloud applications and consume the information these systems deliver.
  3. Data turn out to be less complex. Data integration handles the intricacy that arises from a data transfer and rationalizes those connections to make it deliver to any system effortlessly.
  4. Data gains exceptional value than ever before. Users can now split their internal data and combine it with external data and combine structured and unstructured data from various sources.
  5. Data gets more centralized. Hence, it becomes easy for anyone in the company to utilize it. Centralized data can be converted easily earlier to data integration.
  6. Collaboration within data also improves due to its accessibility. Now employees can easily share data with one another internally or throughout the organization.
  7. Data accuracy gets better. It grows to be more consistent and is typically error-free to make certain that the data is valid and feasible.

These are a few of the ways that an organization can actually take benefit from a genuine data integration strategy. Without a pre-decided plan, it may be tough to manage, but having the right strategy can support the companies to realize considerable business value from a data integration solution.

Data Integration Consultants

General Business Use Cases for Data Integration

Are you aware of the ways you can put data integration into action? What is the reason that makes it so appealing in the first choice? Here are some ways confident organizations use data integration solutions:

Leverage Big Data

A big data analytical solution presents a way to collect important information from your structured, unstructured, and semi-structured data. Big data integration enables the IT team of an enterprise to integrate and merge all data at once and make it available for analysis and helps to gain actionable insights to make valuable business decisions. It doesn’t matter what type of data IT needs to split and analyze, whether it’s conventional data, machine-generated data, social media, web data, or data from the Internet of Things networks as data integration conducts real-time ingestion of data quickly.

Integrated Customer Data

Customer Relationship Management (CRM) Software 

One trendy approach that enterprises use to take benefit of data integration is through customer relationship management (CRM) software. CRM enables an enterprise to capture and collect information about the customers who are interested in their services. Therefore, it is easier for an organization to recognize and target their customers and also garner benefits that boost revenue, including updated records that imitate correct customer information, managing a database of sales leads that is tracked and monitored across the process, and find out future opportunities to move toward or associate with customers.

Better Visibility

Generally, it is hard to understand the true value that a single part of data embodies. But with data integration, it has become easier to track and monitor data throughout a whole business process and the business value from data is actually visible. A business user can see an inclusive customer view, from the ordering process through completion, which was built inside a data integration solution in the type of data synchronization. Data integration captures that entire customer’s information, prepares and delivers that data in a mode that is easy to digest and track.  

Business Intelligence

Efficient business intelligence has some definite number of requirements to make an aggregated and intended data set that enters into a data warehouse and needs to be repurposed a little amount. Data integration tools assemble data and convert it according to the required structure so that a business intelligence solution can perform it deliberately. For making this happen, data integration also conducts major business processes such as business performance management, reporting, dashboards, and advanced analytics to build some important and tactical strategies.

Selecting the Best Approach!

There are numerous ways that a company can adopt to make use of data integration technology. These approaches correspond to functionality that no other tool does. The type of approach you select to conduct data integration depends entirely on the specific requirements of an organization and the outcome which you desired from the data integration. Here are some ways through which a company can utilize data integration technology:

  1. Data Consolidation
  2. Data Warehousing
  3. Extract, Transform, and Load (ETL)
  4. Integration Platform as a Service (iPaaS)
  5. Enterprise Service Bus (ESB)

The major step to a successful digital transformation strategy, data integration can reform your business technology to work together with customers, vendors, suppliers, and applications. Contact a leading Data Integration Consultants today, ExistBI has offices in the United States, United Kingdom, and Europe.


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Understanding the Benefits for Businesses with Data Governance Consulting

Businesses require more than just data if they want to be successful. They require good data- information that is correct, absolute, and easily accessible. If you want to maintain the initial quality of the data as it’s traced, then you can’t expect it to magically fulfill your organization’s needs. This is why Data Governance Consulting is a vital part of the overall data management process.

Significantly, you understand the benefits of data governance (DG) beyond the General Data Protection Regulation (GDPR) compliance. Data governance is compulsory for GDPR, so the inducement in applying it is clear.

The data existing in your organization is a strategic asset. Exactly like your finances and customer relationships, it needs to be managed properly. When sensitive data is disorganized, organizations can face penalties for not fulfilling regulations, growing costs for holding and managing duplicate data, and other expenditures. Moreover, they cannot ensure their business decisions are based on accurate information. To reduce these risks, you need the right data governance strategies in place.

Benefits of Data Governance

What is Data Governance?  

Data governance is described as the management of data to confirm its accuracy as per the requirements, standards, or rules that a specific organization needs for its definite business.  

It is a combination of data management applications and processes that help an organization manage its internal and external data flows. By implementing Data Governance, your business can make data quality more efficiently and help secure the accessibility, safety, integrity, and usability of its data assets.

According to Gartner, data governance is the specification of decision rights and an accountability structure to make sure the suitable behavior in the assessment, creation, consumption, and control of data and analytics.

When building your Data Governance strategy, you should customize the data governance definition according to your company’s concerns and goals. 

Data Governance strategy

Benefits of an Established Data Governance Strategy

Better Decision-Making

One of the major benefits of data governance is improved decision-making. This is relevant to both the decision-making process and also the decisions themselves.

Well-governed data is more reachable, making it easier for the applicable parties to discover useful insights. It also means decisions will be based on accurate data, ensuring better precision and reliance.

Operational Efficiency

Data is extremely valuable in this digital era of data-driven business. Thus, it should be treated as an important asset. Well-performing manufacturing companies ensure their production-line machinery undertakes regular inspections, maintenance, and upgrades, so the line operates efficiently with limited downtime. The same approach applies to data. Having the right data in hand will help to improve your operational efficiency.

Greater Data Quality

As data governance helps to improve discoverability, businesses with efficient data governance programs also take advantage of improved data quality. Though technically two different initiatives, some of their objectives overlap, for the consistency of data and its consistency. One way to visibly distinguish the two programs is to consider the questions imparted by each field.

Data quality helps to know how useful and absolute data is, whereas data governance helps to know where the data stays and who is accountable for it. Data governance makes data quality better.

Regulatory Compliance

If you haven’t yet implemented a data governance strategy, compliance can be the best reason to do so. GDPR penalties are only incentivizing something you should already be eager to do. Data-driven businesses that have not taken advantage of the above benefits are basically oppressing their own performance.

Increased Revenue

Bringing more revenue should be higher on the Data Governance benefit list. Although the above benefits collectively also influence it. All the advantages of data governance explained above help businesses make better, quicker decisions with more confidence. It means that fewer expensive errors are made, such as fake starts and data violations. It means that you need to spend less money by optimizing risk and finishing the most susceptible gaps in your business’ security, in spite of more money, dealing with PR and financial crises.

Data Governance Consulting

Why Data Governance Matters?

Data governance plans are often driven by the requirement of complying with internal policies, regulatory consents, such as SOX, GDPR, HIPAA, frameworks, or standards. But the profit of setting up clear rules and procedures for data-related actions is further than compliance. Here are some of the other general advantages of a well-established data governance program:

  1. Enhanced security, which is attained by locating critical data, finding out data owners and data users and assessing and reducing risk to critical data
  2. Better data quality that enables improved business decision-making
  3. More operational efficiency due to processes and procedures that facilitate faster and simpler data management
  4. Less data management and storage costs
  5. Reduced security breaches due to superior training on proper handling of data assets

Implementation Process for Data Governance

The data governance plan can be very intricate and costly to implement. Here are the steps included and the aspects that need special attention.

Step 1– Set up a value statement and create a thorough plan

Step 2– Identify and employ the right people

Step 3– Build a data governance policy

Step 4– Apply the policy

Step 6– Assess growth continuously

Summary

A successful data governance process allows businesses to realize that whether the data they are entering is historical or recent, it will be reliable and functional for data analysis.

Data is an extremely important and strategic raw material for any business. With the elevated volume of data flowing into organizations today, and the diversity of formats, both structured and unstructured, it is vital to get the correct information at the right time to the right people to facilitate the entire organization to develop and take benefit from new opportunities.

If an organization recognizes the full and enduring impact of data as a correct and valued asset and treats it in a steady manner through a whole data governance strategy, they can utilize data intelligently to empower their business for success. Do you need help in creating a long-term data governance strategy for your business? Contact Data Governance Consulting experts for the right guidance, ExistBI has consulting teams in the United States, United Kingdom, and Europe.


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Why Upgrade to SAP BusinessObjects Web Intelligence 4.3?

It has been a long time since SAP BusinessObjects has a major upgrade. Since 2011, Desktop Intelligence was transformed into Web Intelligence 4.0 that introduced us to new and improved reporting experience. With BusinessObjects 4.3, the tool has been transformed into a modern look, based on the Fiori design which improves not only in development but also in presenting reports.

SAP-BusinessObjects-Web-Intelligence-4.3

Design

Unlike BOBJ 4.2, which functions and features similarly patterned with Microsoft Office 2003 buttons, BOBJ 4.3 looks modern and the design is fluid that looks lighter and more modern, which end users and developers will love to work on.

Compatibility

Unlike BOBJ 4.2, 3 types of the view exist: Rich Client, Java Applet, and HTML. Rich Client and Java Applet both offer the full features and capabilities and HTML serves as a viewing tool for the users with limited functionalities.

Unfortunately, if you need to use certain functionalities that are not available in HTML, you need to switch from either Rich Client or Java Applet and your momentum is abrupted because of this change. Furthermore, the browsers that support Java become scarce. With the end-of-life support to Internet Explorer (not to be confused with Microsoft Edge), which is the last known browser that supports Java, companies, and developers resort to outdated browsers that support Java. 

With BOBJ 4.3, only two exist: Rich Client and HTML, with both tools equal in functionalities. And you can now use any browser of your choosing, as long asit supports HTML5.

There is only one caveat: BOBJ4.3 does not support Data view, which allows users to display the row data from the source. However, this should not be an issue as data can be viewed directly by dragging all objects to the report view to display data.

Properties Panels

Unlike BOBJ 4.2, in which you need to interact into popup window to change a specific feature in the report.

With BOBJ4.3, all options, except for Filters and Conditional formatting, can be interacted with Properties Panel.

Properties panel are subdivided into two tabs: Data Panel and Format Panel.

Data Panel allows users to modify which regard to anything that relates to Data behaviors (like Breaks, Filters, Sorts, and more.), which change according to the object that is currently selected.

Format Panel allows users to modify which regard to anything that formatting the block, which changes according to the object that is currently selected.

These are now hosted in one area that appears when you are modifying an object.

Chart Categories

Unlike BOBJ 4.2, BOBJ4.3 is now categorizing the charts into different groups based on its use. You can now select a chart base on how it will be presented in the report and not based on the family where it came from.

An example of this is the Column and Bar charts. Standard Column and Bar Charts in BOBJ4.3 belong to Comparison categories. Whereas, 100% Stacked Column and Bar charts are grouped under Proportion since these 100% Stacked Column and Bar charts works differently as it is best to show the share of its members based on the total value.

Revamped Filters

Unlike BOBJ4.2, wherein we can filter using filter bar and input controls: Filter bars only allow one value; whereas Input controls can be flexible from one value to multiple values.

With BOBJ4.3, Filter Bar and Input controls are now merged into one. Filter bar capabilities are now equipped with different selection options (Single value or multiple value), which can be incorporated with Grouped Filters for users to drill down data according to their selections.

Fold and Unfold

With the BOBJ 4.3, Showing and hiding of data sections has been revamped. From the use of plus buttons, which similarly works with grouping cells from Microsoft Excel, we can now hide areas using the down arrow placed on the either the headers of the table or the section headers.


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8 Data Processing Engine Concepts You’ll Learn in Informatica Classes

When you hear the sound of a Ferrari, you’ll find that sound so unique, which is a result of years of hard work by the designing engineers, connecting the driver’s experience to the car. Similarly, the data processing engine plays the role of connecting the user experience to the data. If you want to dive deep into the data solutions implementation, joining Informatica Classes will help you learn the various aspects of data needs and their fulfillment.

When you talk about data management in an organization, data processing engines receive the data pipelines, conceptualize the business sense, either simple or complicated.  Then you can process data on various frameworks like Apache Spark in optimized, streaming, or batch-wise approach in cloud or on-premises.

Many data engines are available in the market, but just like selecting a car for your use, you search for different main features and differentiators that change your opinion from one to another. Informatica is designing data processing engines for at least 25 years. Over this time, it has implemented top-class and enterprise-ready data engines to assist different data workloads on-premises or in the cloud.

8 Key Concepts of Data Processing Engine

Informatica with its strong experience, these are 8 concepts of data processing engine that you should know when evaluating various data platforms:

Validation:

A lot of design tools normally produce an XML or JSON depiction of a data pipeline.  The data engine usually revalidates the definition of pipeline and substitutes placeholder parameters with actual parameters generated while processing. If the data pipeline displays reusable components of a pipeline or mapplets, they are also extended.

Optimization:

Design tools enable the users to create data pipelines in a simple step-by-step process. And, the data processing engine has to ensure that the data pipeline is logical and easy to maintain, so it is suitably interpreted to code processed in that engine. For instance, if the data pipeline is translating data from a relational table and implements a filter, it is suitable to push that filter down to the relational database. This simple way of optimization has the following advantages:

  1. Quickly translating data from a relational table when you do it on a small subset of the data
  2. A relational database engine allows speedy reads by using database index
  3. Combining the steps between “read” and “filter”, this method eradicates the need for unnecessary data flow

Code Generation & Pushdown:

After validation and optimization of the pipeline, it needs to be translated into an optimized code to carry workloads regarding the transactional, database, big data, and analytical. The data processing engines present two modes of code translation to support various computations of workloads that are: native and ecosystem pushdown.

The data processing engine of Informatica provides its own execution environment with its native-mode capabilities. For execution, the ecosystem pushdown mode translates the data pipeline into another abstraction, such as Spark or Spark stream processing.

Resource Acquisition:

The execution of the data pipeline may fail and result in loss of computing resources without an appropriate resource acquisition upfront, and you may fail to notice SLAs. But, while using Informatica’s native execution mode of the data processing engine, it will hold back the resources where the engine is processing, such as on Linux or Windows.

If it is in pushdown mode, the data processing engine will obtain the necessary resource right from the ecosystem like AWS Redshift, Spark, Azure SQL or a relational database. In the streaming condition, where the processing of workload is continuous, the resource strategy should be flexible and should consider the received streaming data.

Runtime:

When the data processing pipeline is validated, optimized, translated and necessary resources are acquired, it is required to process the code and run. The data processing engine should be capable of running low-level data operations. It must store data in memory efficiently, reduce marshaling and unmarshalling of data, maintain buffer management, etc. Informatica’s native engine is customized for competent run-time processing and Apache Spark utilizes Project Tungsten to attain efficiency.

Monitoring:

When processing a task, an efficient data processing engine must show the progress and its health-related data. Monitoring must present meaningful insights into data, which can be made possible by monitoring UI, API or CLI. Monitoring varies delicately for different batches and streaming workloads. For example, due to the continuous streaming of workloads, you will have to monitor data volume versus the number of jobs run under process. 

Error Handling:

The data processing engine must be able to detect an error condition and resource allocations for cleanup, temporary documents, and files, etc. Error handling can be achieved at the data engine level and all processing engines will follow the same format or can be done at the data pipeline level, where every pipeline holds its own error handling directions. Similar to monitoring, here also the errors are handles separately between batch and streaming workloads. When an error takes place in a batch workload, this task can be started again and the processing of data occurs in the next workload invocation. While in real-time streaming mode, restarting option might not be available out there.

Statistics Collection:

After the completion of the task, the data processing engine should have to record various statistics like total runtime, status, the runtime of every single transformation, and the number of requested resources and used. The noted information is recorded and made available for use in future optimization tasks, particularly for the “Resource Acquisition” step.

Summary

Here you’ve covered a few concepts of data processing engines that will help you to learn how a data processing engine works like a central component for a data platform. Into the deeper details, you’ll get to learn the further details of vast concepts and capabilities of data engines, such as push-down optimization and serverless compute. But before you get into details, you have to know about creating various data processing pipelines in Informatica’s Cloud server.

If you want to learn more technical aspects, tips and tricks, data needs and their solutions, etc. joining Informatica Classes will help you to earn the best practical and technical knowledge about various concepts. ExistBI is authorized Informatica Partners and offers custom or fit-for-purpose Informatica training in the United States, United Kingdom, and Europe. Contact us today for more details.


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Importance of ERP and the Things You Should Consider When Implementing It

In this blog post, we are going to discuss the importance of ERP and things to consider before implementing it.

What Is An ERP (Enterprise Resource Planning)?

ERP (Enterprise Resource Planning) is a software system that manages and supports business operations. A business is an activity that a company performs on a daily basis to add value to its business and generate profits. Types of activities include the tasks that are usually performed in real-time.

What Is The Purpose Of ERP Software?

ERP (Enterprise Resource Planning) is an integrated software system that automatically manages most aspects of a company’s operations and production, including finance, purchasing, production, logistics, human resources, marketing service, and customer support.

Importance of ERP

Key Features of ERP

ERP offers a wide range of services to companies that want to optimize their operations. The systems used are constantly updated to provide the fastest and most reliable services.

As the name suggests, the main objective of ERP is to manage and utilize the various resources of a company in an economical manner. It is also designed to ensure that all functions are used correctly.

The ERP system is particularly well suited for tracking and managing the company’s production capacity, available cash, availability of raw materials and supplies, payroll, purchase orders, etc. A purchase order is the main document issued by the company’s purchasing department when an order is placed with a company or supplier.

The Importance of ERP in the Enterprise

The most tangible change that ERP systems have brought to the enterprise is undoubtedly the increased reliability of data, which can now be viewed in real time, and the reduction of duplication of effort. This can be achieved through the systematic updating of data in the chain of ERP modules and, ultimately, through the cooperation and commitment of the employees who interact with the business.

This allows information to flow through the modules in real time. In other words, a customer’s order triggers a production process, which in turn sends information to multiple locations, from the warehouse to product logistics. All of this is done through seamlessly integrated and unduplicated data.

To better understand this, you can think of an ERP system as a large database of information that interacts with and responds each other.

For example, a sales order becomes a finished product that is distributed to the company’s warehouse. An ERP system eliminates the need to track each process individually. This gives you the support and time to plan, reduces costs and analyzes your supply chain to produce more efficiently, reduce costs and improve product quality.

Six Benefits of ERP

Simplify IT – An integrated ERP application using the same database simplifies IT and makes everyone’s job easier.

Increased productivity – By simplifying and automating key business processes, everyone in your company can do more with fewer resources.

Insights – Eliminate information gaps, create a single source of truth and get quick answers to important business questions.

Reduce risk – Maximize visibility and control of operations, ensure compliance, and anticipate and avoid risk.

Greater flexibility – Streamlined operations and instant access to real-time data allow you to quickly identify and seize new opportunities.

Accelerate reporting – Accelerate financial and operational reporting and simplify the sharing of results. Leverage information to improve performance in real time.

5 Things to Consider For ERP Implementation

Many businesses start by using several simple, standalone tools such as QuickBooks and Excel spreadsheets to manage their various processes. Here are five reasons when your business needs to get out and buy a modern ERP system.

#1. You have unmanaged business processes: Do you have uncontrolled processes in certain areas? Managing inventory, improving customer satisfaction, and keeping costs within budget can be more challenging. In this case, you need to reorganize your business processes as your business grows and priorities change – the ideal environment for ERP software.

#2. You are spending more time on day-to-day operations: ERP software integrates solutions and data into a single system with a common interface to facilitate communication and collaboration between business units.

#3. Have many unanswered business questions: Can you easily answer key business questions such as sales metrics and product line performance? If not, your system may be fragmented or you may not have access to key metrics, which could hurt your business. Enterprise resource planning software is designed to solve these problems.

#4. Your business has missed the opportunities in brief: Are you spending too much time managing your business and not taking advantage of new opportunities? Today’s ERP systems include advanced intelligence features such as machine learning and predictive analytics that make it easier to identify and exploit new business opportunities.

#5. Manually processing multiple data sets: Do most departments in your business use their own applications and processes to get the job done? If so, you’re wasting time entering duplicate data. When data doesn’t flow from one system to another, reports take longer to run, errors occur more often, and decision-making is delayed.

Having an integrated ERP system is essential for any industry to get the most out of its resources. From the smallest to the largest, it helps companies of all sizes to successfully implement strategic business plans.


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What is Nursing Informatics and why is Nursing Informatics Important?

Informatics is changing the face of medical services. With the advancement of the latest technology, healthcare professionals and organizations can gather, analyze and leverage information more effectively, affecting the way care is provided, assets are managed and teams work every day. You would be unable to discover an aspect of medicine that presently can’t seem to be touched by the mass analysis and collection of data that has been introduced by the Information Age.

One explicit area that health informatics is essentially affecting is the practice of nursing. Despite the fact that the mission of nursing stays unaltered, the day-by-day work of these experts is by and large affected by informatics, with specific attention to the communication and accuracy of patient information and care.

Nursing Informatics

What is Nursing Informatics?

Nursing Informatics is a specific area of nursing and a profession that has lots of potentials. The purpose of this blog post is to be an introduction to nursing informatics and the importance of nursing informatics.

The nursing profession is quickly changing to keep up with new challenges and advancements in healthcare services. As one-to-one caregivers, nurses are the frontliners of patient care and always feel the impact of changes in best practices more quickly than other medical services experts.

One of the primary ways that informatics has changed nursing practice is through documentation. The time of paper charts is gone where all the records updated with handwritten notes. Nowadays, nurses have to keep notes in digital health records and different systems that keep a patient’s clinical history easily accessible and up-to-date.

Read More: How Big Data Can Help in Fighting Against Coronavirus (COVID-19)

Health informatics is also a significant piece of care coordination in nursing. The capacity to track staffing, communication, and workflow can assist nurses to identify areas where current workflow can be improved. This can also help to make sure that staffing levels stay sufficient, which is important for giving patients the best possible healthcare. The more data that is gathered and analyzed, the more accurate the results will be and giving the most ideal data to deciding how best care for patients can be provided in the future. If nurse to patient ratio drops low, patients are more likely to suffer the worst outcomes.

Nursing Career Option in Informatics

Nurses at every level presently work with informatics through patient records and other healthcare technologies. Some nurses decide to focus their careers on the intersection of informatics and clinical practice. There are various career choices accessible in this path, including the following:

  • Clinical informatics specialist
  • Clinical informatics organizer
  • Nursing informatics specialist
  • Clinical informatics administrator
  • Clinical Analyst
  • Nursing informatics Analyst

These roles can be found at each level and feature of healthcare organizations, including management and leadership, support, risk analysis, consultation, research, education, and evaluation. As informatics turns into a more noticeable part of the nursing field, job opportunities will probably keep on creating.

While healthcare informatics jobs are available to experts from different backgrounds, nurses are especially appropriate for these roles because of their deep insight into clinical workflow, past healthcare training, and experience in information systems and the latest healthcare technology.

With the proper informatics training combined with your existing medical knowledge and clinical, you could have an effect on inpatient care in a medical organization through a career in nursing informatics.

Role of Nursing Informatics

What a Nursing Informatics Expert Does

Strongly focused on data, information, and communication, the main responsibility of nurse informatics is: how to utilize numbers to boost performance, both for patients and for an organization all in all. The purpose of the job is to “boost proficiency, cut expenses, and boost patient care quality”. Nursing experts are positioned at the intersection of computer science, nursing science, and data science, where they can “better manage and communicate data, information, and knowledge in the practice of nursing.

Nursing informatics experts encourage data integration, data, and knowledge so that they offer better service to patients, nurses, and other healthcare professionals. One thing on which they spend lots of their energy is documentation, because “highest quality of patients care is completely dependent on strong communication among the wide range of healthcare providers. A nurse informatics analyst increases the speed of the charting process, which means the healthcare professionals have better access to the patient’s chart, notes and take proper Medicare.

Read More: Benefits of iPaaS, Explanation, and iPaaS Use Cases

Where Nurse Informatics Professionals Work

Nurse informatics professionals work in a wide range of fields like Consulting firms, big corporations, hospitals, and Universities. Job titles for this field that match this professional competency include:

  • Clinical analyst
  • Director – clinical informatics
  • Clinical informatics organizer
  • Informatics nurse expert

Why Nursing Informatics is Important?

Nursing is progressively turning out to be as “high tech” as it is a “high touch” job.

Nowadays, Nurses have more technology in their hands than any other medical professionals ever before, and as one may anticipate, it’s impressively improving patient care.

So how are nurses utilizing informatics as an approach to improve the healthcare providing to patients? Let’s discuss the several ways that nursing informatics is being used and why is it so important…

1. Improved Documentation

One of the most important parts of the nursing profession is Documentation and it has been recognized as a more vital part of patient care. The standard of nursing practice, practice and theory of nursing, ethical and legal concerns, and other factors that are taught in the advanced nursing programs make an impact on patient care.

Nowadays, modern nursing care is organized patient history and special care requirements by using data generated and organized in electronic patient records. By documenting a patient’s physical condition and added that information electronically, nurses can manage patient care more effectively. Also, nurses can improve the quality of patient care.

Lots of documentation is automatically produced by connected devices. Those devices collect patient-oriented specific data in real-time and send it to patient records. Taking a look at the documentation of a patient’s medical situation from time to time, nurses can make better decisions about how to give the best medical care, when adjustments, or changes need to be made.

2. Makes Sure There are No Medical Errors

The safety of a Patient is the main concern of any health care professional, and nurses are the frontliners of patient care to ensure that their patients remain safe and reducing medication errors, falls, misdiagnoses, and other difficulties. Health informatics gives valuable data that can stop these medical errors; for instance, an electronic document can store data about a serious medication communication or allergy that might not otherwise be instantly visible. Loaded with data, nurses can make smart choices that keep their patients secure and safe.

Patient complaints and nurse training errors are some of the main reasons for disciplinary actions, nursing board license investigations, and malpractice lawsuits. Accusations have been growing in recent years because of the ease of registering complaints online. Health informatics makes sure regulating many patient care decisions which makes it simpler for healthcare industries to restrict their responsibility and ensure compliance with the Nursing Practice Act and other medical care patterns.

3. Decrease the Medical Costs

Medical service’s errors expenses cost nearly 40 billion every year, and many of these errors can be solved with health informatics. Not only with the information with health informatics nurses can avoid errors but also they can automate different tasks such as create doctor note templates, improving patient care, increase nurse’s productivity, and stopping some of the expenses related to healthcare.

4. Improved Coordination of Care

Nurses are often called upon to help organize the medical care of their patients. This means sending information from therapists, physicians, pharmacies, billing, and more services during medical care and at discharge. Without all of the important data, patient care can suffer. Health informatics increases the coordination of this data, improving both satisfaction and outcomes with care, and allowing nurses to provide their patients all of the information they require.


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Benefits of iPaaS, Explanation, and iPaaS Use Cases

In this blog post, you will learn:

  1. What is Integration Platform as a Service (iPaaS)?
  2. 4 iPaaS Benefits
  3. 3 iPaaS Integration Patterns
  4. Common Challenges with an iPaaS
  5. iPaaS Options
  6. What you should look for in an iPaaS?
What is ipaas

What is Integration Platform as a Service (iPaaS)?

An Integration Platform as a Service or iPaaS – gives incorporated support to manage, administer, and coordinate cloud-based applications, utilizing fools that interface cloud applications and services, and control joining stream. Organizations use iPaaS solutions for scale execution needs, add product usefulness, and design SaaS applications and on-premise application coordination, all to expand the estimation of their business connections.

While it is not difficult to perceive any reason why an iPaaS is a particularly successful tool for integration, there are a couple of various types of iPaaS that are different from each other. Depending on your necessities inside the enterprise, a particular class might be more qualified to solve the most pivotal integration challenges you face.

Why Use an iPaaS?

These days, to satisfy client needs, stay in front of competitors, and improve activity; companies should have an enterprise integration solution installed that can adequately incorporate always growing integration prerequisites across various applications, data, and ecosystem processes. That is the reason an ever-increasing number of companies are hoping to tap the potential for expansive integration capacities offered by a powerful subset of the application framework and middleware (AIM) technology market – (iPaaS) Integration Platform as a Service.

4 Benefits of iPaaS

As an ever-increasing number of companies take their business in form of cloud computing, the struggle turns out to be managing various tools and business processes efficiently. Enter iPaaS, which is intended to incorporate the many cloud applications with each other in a consistent, simple-to-manage way. Attempting to integrate numerous cloud frameworks can be a significant pain for enterprise IT solutions, which is the reason iPaaS is growing so quickly. In fact, the iPaaS market is expected to reach $10.3 billion by 2025.

In any case, there are numerous ways an enterprise can gain the advantage of an iPaaS platform such as:

1. Better Connectivity

An enterprise’s IT situation can get complicated in a quick time. The advantage of iPaaS is that it can possibly associate all that a venture requires connected. How can you be benefited from software, applications, and other business processes if they don’t even work together? Here comes the iPaaS, which permits the business to incorporate a huge variety of on-premise apps to make easier hybrid data flows, improve operational work processes, synchronize information, and gain better visibility.

2. Cost Control

Assemble it or buy it? It is a well-established inquiry for the IT industry. Companies that utilize a multitude of coders to plan and keep an in-house integration framework will regularly discover costs out of their hand while paying for consultants to develop custom connections with various 3rd party providers can likewise dramatically raise costs. Alternately, iPaaS is commonly consumed as a service permitting the flexibility and adaptability of an enterprise that reduce the expenses of traditional integration.

3. Better API Management

Effective and easy-to-use API management has become a struggle and difficult task as companies look beyond the specialized need of APIs and deploy more business-situated APIs. In order for an enterprise to rapidly and effectively access and share real-time data, it’s very crucial to have a level of API management functionality. Through iPaaS, organizations acquire a single platform to integrate and manage all of their APIs with the capacity to scale depending on the situation. Companies are then ready to make, convey, and manage APIs while adding new capacities and tools as needed.

4. Secure Your Enterprise

It might be the greatest concern enterprises have in regard to cloud computing. Because enterprises constantly facing security and thereof problem within their system. An iPaaS arrangement can decrease the danger of a data breach because the vendor continually deals with the infrastructure and framework. IPaaS vendors additionally give confirmation and verification methods to the different data flows streaming in from all over the business ecosystem.

An iPaaS solution likewise gives companies a tension-free sleep at night, realizing that their systems and applications are genuinely secure.

Read More: 12 Top Business Intelligence Tools in 2021

Common Challenges with an iPaaS

The advantages that an enterprise can acquire from iPaaS are clear. Yet, while iPaaS can deal with the entirety of your business needs, in order for a platform to really succeed and run proficiently, there are a couple of difficulties that ventures should explore to do as such.

1. Complexity

One of the promising guarantees of iPaaS is that it can take a complex environment, regardless of whether it’s on-premise or cloud, or a mix-and-match of both, and afterward work on it. However, that situation is still beautiful. An iPaaS can frequently require specific developer integration ability, particularly as data intricacy increases within the business and it is harder than ever to discover employees who have this specific talent.

2. Security

Indeed, security is also a strength with regards to iPaaS, but since this is still cloud computing we are discussing, it also should be added as a challenge. The cloud, especially the publicly shared cloud is a fear for some businesses when it comes to security breaks and keeping a high level of safety.

3. Scalability

Yes, adaptability is also one of the advantages of iPaaS, however for certain enterprises that can cause an issue if they aren’t set up to manage an uptick in scalability. While using a platform, IT professionals should pay attention to the scalability of their model, which incorporates the size of individual exchanges, just as the general speed of exchanges each hour. Businesses should take careful consideration about what their iPaaS can and can’t deal with.

3 iPaaS Integration Patterns

As an ever-increasing number of businesses choose some type of cloud computing, the struggle turns out to be managing various applications and business measures viably. Enter iPaaS, which is designed to integrate the many cloud administrations with each other in a consistent, simple-to-manage way. Attempting to integrate various cloud frameworks can be a pain for big business IT, which is the reason iPaaS is becoming so quick. In fact, in 2017, the iPaaS market managed to surpass $1 billion for the first time. Here are 3 iPaaS integration patterns:

1. B2B Ecosystem Integration

Present-day B2B integration technology provides ecosystem enablement through multi-enterprise business continuity and communication in its capacity to control, administer, and automate frictionless data trades past the four walls of the business. A domain-specific platform permits businesses to meet far-reaching communication necessities with clients and partners, move data between unique internal systems, and integrate and connect cloud services and applications in a well-represented manner.

2. Hybrid Integration

Also, an iPaaS platform empowers organizations to speed up ground-to-cloud and cloud-to-cloud integration measures that effectively integrate applications, and storage and business platforms, to connect all data, regardless of whether it’s on-premise or in the cloud. Through iPaaS, it’s simpler than ever to hybrid connectivity to SaaS (Software as Service applications) and other cloud applications with a safe strategy to access on-premises applications behind a firewall.

3. Application Integration

Perhaps the greatest challenge facing companies today is the expansion of cloud applications across the enterprise. An iPaaS is regularly the primary line of protection in giving the capacity to unify integrations among applications and give some rationality across all the data moving through the enterprise. However, independent cloud application integration without considering the need to tie in on-premise integration and ecological integration necessities. Thus, an overemphasis on application integration alone possibly makes another sort of integration silo.

What to Look for in an iPaaS

An iPaaS architecture offers a ton of promise, however businesses by and large search for some basic features and capacities during the selection and discovery stages. Some of the things to look for in an iPaaS architecture include:

  1. The capacity to integrate with new data sources and other business processes.
  2. Data reliability, security, and uptime.
  3. Including API management, management solutions.
  4. Monitoring solutions that provide start to finish visibility.
  5. Ability to scale and adjust to meet developing business needs
  6. Storing data on-premises, in the cloud, and in a hybrid scenario

The Future of iPaaS

The real question is – what’s to come in for big business IT? Enterprises should have an integration solution, even if it is confronted with the most complex situations. Integration platforms as a service get more famous and widely utilized in businesses as the year passes. Technological platforms will keep on advancing, as more enterprises are involving. Cloud-based integration solutions will become more evident than on-premise ones. Companies that have been terrified of moving to the cloud will be forced to dip their toes into the iPaaS market, and before they know it, will jump headfirst after understanding the advantages that come from iPaaS.


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SAP BusinessObject BI Launchpad and Web Intelligence 4.3

SAP BusinessObjects BI 4.3 is a major follow-up with BI 4.2 which aims to bring new features (BI Launchpad) and enhancements with a modern twist. The well-loved tool with a fresh look aims not only to make development easier for developers but also for front-end users with its improved user interface.

1. BI Next Generation Launchpad

It is getting an overhaul with the design based on the Fiori Theme. From the Windows XP-like interface, the new Fiorified BI Launchpad has been redesigned and packed with the old and new functions that make the user navigation easy to do.

BI Launchpad

Home Page

Redesigned bringing the Fiori Tile, which is can be rearranged according to the user preference.

Sap BI Launchpad

Documents

This new feature allows users to display all the user-created documents in one single view according to the last saved date/time. 

Revamped Scheduling and Publication

Newly revamped scheduling and publication are now grouped into two distinct categories: General and Report Features.

Other than the revamped look, a new Recurrence type is added: Business Hours. This schedule sends the scheduled report every hour within the specified start and end hours (which is considered as Business Hours) only on weekdays. This enhancement is available for both BI Launchpad and Central Management Console.

Other than the revamped look and new feature, the Retry Option has been added in the options, which was an exclusive Central Management Console function.

Another feature that is introduced is creating multiple destinations in one scheduling job. This eliminates the workaround of creating multiple jobs for different destinations.

Instance

Previously an exclusive Central Management Console function, BI Launchpad’s Instance Manager displays all scheduled instances of the user with user-friendly search functions.

In addition to the user-friendly search functions, this instance manager now allows the management of multiple instances in one click.

With the addition of multiple destinations, a new status has been introduced: Partial Success (which this status is seen previously when Promoting objects to another BusinessObject system)

SAP Businessobjects BI Launchpad

Themes

This new allows users to change the look and feel of the Launchpad, which now includes a dark theme.

Revamped Folders Page: New Design, New Features, Similar Functions

Folders navigation has been redesigned to integrate the Fiori Theme and with enhanced feature: Navigation path, description, and last modified date/time of the Folder are already available in the view.

Folder options have been redesigned and consolidated. Object only when selecting an object, using the checkbox beside the object.

User Notifications

The notification has been revamped and centralized to now display in the top right corner instead of the default Home page in the Classic BI Launchpad.

2. Currently, Open Documents Drop Down List

Located on the top of the page, this new feature replaces the original tab-like interface.

BI Launchpad

3. BI Inbox

All alerts, documents sent by others, and scheduled instances from scheduled jobs have been simplified into one view.

 4. HTML5 Web Intelligence

Web Intelligence has improved with the new look taking advantage of HTML5 architecture.

Originally design for quick edit, the HTML interface features have been expanded since the release of BI 4.2. SAP has slowly integrated all features from the Rich-client and Java Applet interface to its HTML Interfaces such as adding SAP Business Warehouse and local files as data sources, format data values, conditional formatting, and more.

With SAP BI 4.3, the interfaces have been consolidated into one: HTML interface with a revamped design, which is inspired by Microsoft Office.

HTML5 Web Intelligence

The most noticeable change in the BusinessObjects 4.3 is the OptionPane. This replaced the traditional dialog box options that are displayed when an element or an object is selected. The New Option Pane is organized into two categories: Build and Format.

Other than Option Pane, the following has redesigned or removed:

  1. Icon side panels have been revamped into panes. Report Structure and My Objects (formerly Available Objects) are displayed by default. Other panels can be shown or be hidden by using icon buttons located on the top right.
  2. Document Views has been consolidated into one button: “Edit”, which toggles to Design Mode and Reading Mode. Data mode has been completely removed.
  3. Most Options appearing as a dialog box have been removed except for Formulas, Complex Filtering, Input Controls, Break, and Conditional Formatting. All options have been incorporated into Option Pane.
  4. Report tabs have been repositioned at the top of the working page with an add button displayed at the end of the section.
  5. Charts are now organized based on their use. Report elements dialog box has been removed.

The changes made in BOBJ 4.3 make the user experience simple but packed with great features. These improvements not only remove some of the workarounds from 4.2 but also deliver the best user experience and best visualization will greatly help the company in their data analysis.


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12 Top Business Intelligence Tools in 2021 – Top BI Tools Review

Business intelligence has been a growing profession, and its importance is only increasing every day. Organizations and businesses, both small and large, realized how important the concept is for their progress and development. In case you don’t know what business intelligence is, visit ExistBi and read everything about it, including the components of business intelligence, why it is essential for small businesses, and much more. 

As technical as it is, specific tools can make it simpler for professionals. For companies that need help with organization and growth, here are the top 12 top business intelligence tools.

Benefits of BI tools

Before moving on to the actual tools, let’s talk a little more about what they do and how they’re helpful. Why do you even need a business intelligence tool? What does it do to help a business grow?

BI tools

Centralization Of Data

An efficient business intelligence tool helps bring all different kinds of relevant data together. All enterprises and companies collect their data from various portals, databases, and flat files, etc. Using all this data and making sense of it can be challenging since it comes from different directions in various languages and formats. So, modern business intelligence tools can help you centralize these sources. They give you one single point of view on all the different processes going on. This way, They help you identify the various issues, recognize patterns, and make critical decisions based on data and evidence.

Read More: Top 12 Business Intelligence Trends Of 2021

Self-Service 

Wherever there is technology involved, the IT department is always under stress. Professionals and personnel from different departments report their issues to IT. Since access is also limited to IT personnel, everyone has to request entry to the data to use it. Efficient business intelligent software can enable selective people to explore data by themselves. Every personnel on the selected list is equipped with enough skills to find their way about the data. This way, the IT department’s stress significantly reduces, allowing them to focus on their important jobs and tasks. Plus, people don’t have to wait for approvals, so their jobs become much faster and smoother.

Prediction Data

Business intelligence tools give you specific data that you can help to make meaningful predictions. Basically, they make the jobs of data analysts and scientists much more manageable. Also, if the tool is user-friendly enough, it is easily interpretable by a non-professional. You can recognize patterns and routines in the data to make plans and decisions. You can make or change strategies for the best results and efficiently avoid a hazard. 

Elimination Of Manual Tasks

Since the tool is doing most of the job for you, you can take a break. For example, such tools can make reports and presentations. You can automate processes and do a lot more work, which typically requires manual assistance. Since it reduces the workload on you and your staff, you can focus on more critical aspects of your job. 

Economical Benefits

When you are eliminating manual work, you don’t need as many employers anymore. This reduction in staff reduces your labor costs. Plus, these tools allow you to collect, analyze, and interpret data faster. As a result, you make faster decisions, implement strategies more efficiently, and your revenues increase by several folds.

24/7 Services

Unlike manual labor, business intelligence tools are always available at your service. They continuously collect data and store it somewhere you can have direct access to. It might be a cloud or hardware, where everything is kept until you decide to delete it. This way, you can explore the analytics at any time and choose who you give access to. 

For Consulting, Click Here: https://www.existbi.com/services/implementation-services/business-intelligence/

Top Business Intelligence Tools

12 Top Business Intelligence Tools in 2021

Finally, let’s talk about the actual tools you should consider. The following is a list of 12 Top Business Intelligence Tools in 2021 and their reviews. Based on their features and characteristics, you can select the one that suits you best.

1. Microsoft Power BI 

Isn’t it obvious that Microsoft would have something for business intelligence? Microsoft Power BI is entirely web-based, and it features downloadable software. Thanks to this feature, you can access your analytics through your reporting server or a cloud. You can get a 60-day trial if you want to explore the software first. This trial includes connectivity to Microsoft applications, Oracle, Sybase, Facebook, and many other sources. Since you can connect all these platforms, you can centralize the data and make reports in minutes! 

Microsoft Power BI
Image Credit: Microsoft Power BI

The good thing about the software is it’s relatively affordable even if you decide to buy it after the trial. Plus, it features a mobile app which enables touch-screen annotation of your reports. The only con of the software so far is that it requires downloading, requiring time, money, and space on your computer.

Website: Powerbi.microsoft.com

2. Board

This business intelligence tool is a three-in-one. It is a combination of performance management, predictive analytics, and business intelligence. It’s a Spanish company, but you can get it in different languages, including Italian, German, French, Japanese, Chinese, and English, of course. Even though its target audience is finance-oriented business intelligence, it still has something for everyone. It’s got modules like: 

1. Marketing

  • Social media analysis
  • Loyalty
  • Retention monitoring

2. Sales

  • Up-selling and cross-selling analysis

3. Supply chain

  • Supplier management 
  • Delivery optimization

4. Finance

  • Consolidation 
  • Finance planning

5. HR 

  • Workforce planning 
  • Skills mapping

6. IT

  • Service levels 
  • KPIs

The good thing about the Board software is that it’s straightforward to use and is inclusive in terms of language. However, the drawback is that prices can vary according to the role of the user. There’s no fixed license fee, which can be troubling for some people.

Website: Board.com

3. Tibco

Image Credit: Tibco

This one is a self-service artificial intelligence-powered platform for data visualization. It handles workload, data preparation, interactive visualization, and dashboards. Tibco Spotfire features data preparation capabilities based on machine learning and supports the development of complicated data models. Tibco is exceptionally versatile and user-friendly. You can use it in different departments, including life sciences, healthcare, travel and logistics, government, consumer packaged goods, manufacturing, energy, financial services, and whatnot. Tibco’s latest version also supports Python. The software is strategically targeted towards citizen data scientists and analysts to make their jobs easier. 

The great thing about Tibco is that it can use different data science techniques, real-time streaming data, and geo-analytics. It does everything using natural language generation and natural language query. The prices, however, are a little too expensive for small businesses and startups. Stability, integrations, and management issues are sometimes a problem with Tibco. 

Website: Tibco.com

4. Oracle Analytics Cloud

Oracle recently added Cloud HCM to its catalog in 2020. This feature promotes self-service workforce analytics for business leaders, analysts, and HR executives. This time, Oracle has primarily focused on it creating user-friendly and intuitive clouds. They have used popular machine learning features and robust reporting characteristics to create a masterpiece. The Oracle analytics cloud features options like embedded analytics support, a mobile app, predictive analytics, visualizations, data connectors, data preparation, and much more. They’ve basically targeted all kinds of users in large and midsize enterprises. 

Oracle analytics cloud as advantageous features, like natural language queries, to support conversational analytics. Plus, it can generate explanations in natural language automatically. This way, it helps explain trends and visualizations to non-professionals. The catch is that the prices aren’t ideal for small businesses. So, only a selective population of enterprises can use the software.

Website: https://www.oracle.com/business-analytics/analytics-cloud.html

5. SAS

Image Crdit: SAS

SAS offers tools for business intelligence through microservices based on their SAS Viya platform or its cloud. Its purpose is to highlight the critical relationships in data and do it automatically without manual effort. Its latest edition now comes with automated suggestions to highlight relevant factors. Other important characteristics include insights that are expressed through natural language and visualizations, making them easily interpretable.

Other than that, you can also get self-service data preparation, mapping, chart generation, and data extraction from social media and other platforms. Moreover, most of these features are automated, so there are significantly less stress and effort on your side. You can deploy software within the premises, on private clouds, in public, or through their Cloud Foundry platform. 

Indeed, the automated functions are a pro with this software. But since it targets large enterprises only, some of the features and prices might not be suitable for small businesses and startups.

Website: Sas.com

6. Datapine

Image Credit: Datapine

This business intelligence software enables you to connect different sources and analyze the data using advanced analytics features. These features are also predictive, by the way, which can help you make important decisions based on evidence. Using these analysis results, you can develop powerful business dashboards and generate reports, both standard and customized. You can even incorporate alerts and get notified whenever a target is achieved, or there is an anomaly. Moreover, it can manage all data sizes, and you can implement its features in various industries and platforms. Overall, it is a powerful solution for small, midsize, and large businesses.

The highlight of this software is that it features tools for both advanced and average analysts and business users. Its SQL mode allows data analysts to develop their queries. The interface is drag-and-drop, so businesses can use this intuitive feature as well. It makes sure that you create powerful dashboards and charts that make an impact. The only drawback is that the mobile platform does not allow access to the dashboard unless you download the app. Then, you must customize the dashboards to make them mobile view-friendly.

Website: Datapine.com

7. Clear Analytics 

Image Credit: Clearanalytics.com

This tool centralizes data, collecting it from internal systems, CRM, accounting, and the cloud. It enables the user to drag and drop all this data and put it into Excel. It can use Power Query to collaborate with Microsoft Power BI and use PowerPivot to model and clean the various data sets. The self-service platform enables non-professionals to explore the databases, dynamic query designers, and drag and drop interfaces. 

The best thing about Clear Analytics is that you can use various features, like Pivot and Desktop and Power Maps, to share all of your insights to your gadgets. These devices included smartphones and your iWatch as well. However, there is still a shortcoming of the software. Since Excel spreadsheets are the ground of this software, it is not a sustainable option in the long run.

Website: Clearanalyticsbi.com

8. YellowFin BI

Image Credit: YellowFin BI

YellowFin is a complete catalog of smart products. It includes YellowFin data preparation, data discovery, stories, signals, and dashboards. This business intelligence analytics tool enables usage through mobile applications available for both iOS and Android devices. The software is specialized in three primary areas of analytical solutions and business intelligence. They are analytical application builders, embedded analytics, and enterprise analytics. 

Automatic trigger-based tasks are a highlight feature of YellowFin. The software sends the tasks to the person responsible if a particular KPI doesn’t reach the set standard. This specific feature enables all the business employees to be alert, and the right person can take the right action whenever needed. The catch, however, is that some people complain about missing features. They say that the features mentioned above aren’t always available in the tool, which can be frustrating and misleading.

Website: Yellowfinbi.com

9. QlikView

Image Credit: Qlik.com

This one is a business intelligence application that focuses on fast development and analytics applications and dashboards. The software has been built on the Associative Engine, enabling data discovery without using different tools based on queries. This way, it eliminates the risk of inaccurate results and loss of data. Other features include visually-highlighted dashboards, associated exploration, dual-use strategy, and much more.

What’s more, developers can also use several resources and tools like the Qlik Branch Community, the Qlik Branch Playground, the Qlik Core Documentation, and the Qlik Knowledge Hub. The only drawback of QlikView is that it has a very professional interface. Usability can be a problem if you’re not an expert. You need to be willing to learn to use this one!

Website: Qlik.com

10. IBM Cognos Analytics

Image Credit: Ibm.com

Another one from Microsoft, this one is a cloud-based tool for business intelligence. It uses artificial intelligence to create reports and dashboards. It uses its geospatial abilities to display your data physically. IBM allows you to ask it questions in everyday English language to obtain answers in interpretable forms. Other key features involved in search mechanisms that allow users to access and discover data inside the software and save it. Plus, it joins different sources of data and centralizes it into a single module. This way, it allows multiple users to generate insights and work with this information by themselves.

The good thing about IBM Cognos analytics is that it has a very vast knowledge center. They have customer support and a community that aids users in understanding their product and learn how to use it. But as beneficial as that is, it’s a major con for some people. Most users don’t want to refer to professional support every time they need to do something. Instead, they want an interface that is easy to navigate on their own.

Website: https://www.ibm.com/products/cognos-analytics

11. Microstrategy

Image Credit: Microstrategy.com

This mobility platform and enterprise analytics software is a crowd-favorite. It focuses on cloud solutions, federated analytics, and hyper-intelligence. With their mobile dossiers, you can generate your interactive analytical books, and they work on both Android and iOS devices. You can even download an app called Microstrategy Mobile, so you can deploy your analytics wherever you are. 

Voice-technology integration is probably the most notable feature of this platform. It works on processing natural language as well as machine learning. Chatbots and voice integrations, like Google Home and Alexa, can also be integrated. This feature adds to the overall usability of the software. However, there’s a catch: the software’s initial setup is quite complicated for some people.

Website: Microstrategy.com

12. Good Data 

Image Credit: Gooddata.com

This software provides various tools for application integration, visualizations, analytic queries, storage, and data ingestion. The user can easily embed the Good Data analytics into their mobile applications, desktops, and websites. Pls, you can create reports and dashboards every day without any professional knowledge whatsoever. A modular data pipeline and a suitable platform for all developers are vital features of the software. Plus, Good Data comprises four separate data centers: Canada, the EU, Dallas, and Chicago. 

Additional support and services provide a complete life cycle of data analytics. It includes maintenance, launch, testing, and development. However, despite the training sessions and higher costs, some users still find it challenging to navigate.

Website: Gooddata.com

Business Intelligence Consulting 

Even if you have the top business intelligence tools, sometimes, you need extra help. You need information, support, and professional guidance. ExistBi can do that for you! Business intelligence consultants offer advice to people who are struggling with issues like:

  • Do I need business intelligence? 
  • What kind of tools would work best for me? 
  • Why isn’t my tool or strategy working the way I want it to?
  • How long should a business owner or strategist wait before changing the tool and try another one? 

Basically, they look inside the business analytics and figure out what’s wrong and what should be done to fix it. Business intelligence consultants collect, organize, and use computerized data to help you solve your problems. You can contact ExistBi to learn more about these services and how to book one for yourself. 

Business Intelligence Training

If you want to become a business intelligence analyst or consultant yourself, you need training for that. ExistBi can help you with that as well. We offer five-star business intelligence training at ExistBi. If you want to see some recent testimonials and what you get in the training, you can check out the website for more information.

Conclusion

Above are only some of the top business intelligence tools. There are many others on the market as well. So, if you are confused as to which one to choose and how to make a decision, here’s a small criterion you can use. The software you choose should:

  • Be easy to use and navigate
  • Be budget-friendly according to your separate financial status
  • Enable easy access to data 
  • Allow you to deploy data and use it easily

Now, considering your professional knowledge, your staff, your budget, you can choose one that fits this criterion the best! 


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How To Use Ibm Watson Analytics: Overview of Watson Analytics

IBM Watson Analytics has successfully gained wide recognition across the world of self-service business intelligence. A part of this fame has indeed resulted from their creative marketing campaigns. However, that’s not all that makes it so well-loved among the audience. IBM has made data discovery easier for companies, allowing them to make crucial decisions quickly and efficiently. For this reason and many more, Watson Analytics has quickly become one of the topmost loved tools in businesses and organizations. But that’s not all there is to the software; there’s a lot more that you should know!

Overview of Watson Analytics

What is the IBM Watson Analytics?

Let’s take a more in-depth look into what IBM Watson analytics really is: it is a smart, cloud-available solution to data discovery. It features guidance on data exploration and automatic predictive analytics. This way, it allows easy, simple, and effortless creation of infographics and dashboards. As a result, you can get quick and accurate answers to your questions, obtain newer, better insights, and make swift, confident decisions about your business within minutes. And you can do all this entirely on your own, all by yourself! 

You don’t need a massive team of experts. Even if you hire professionals, you won’t be clueless about what’s happening on the slideshow. You’ll be able to understand data in a much better way and make sense out of everything. Ultimately, you can make more conscious decisions about your organization and lower the risks of failure.

IBM Watson has three versions: the free trial, Plus, and Professional version.

  • The free trial gives you access to data, Discovery, and Display tools with the data capacity of 1 MB of free storage.
  • The Plus version includes all free features with additional complete access to the resources of Analytics Exchange. It has a data capacity of 2 gigabytes of free storage, 256 columns, and a million rows. Plus, you also have the opportunity to purchase more storage.
  • The Professional version of the IBM Watson analytics includes all the free features and the Plus features. Moreover, it has a data storage capacity of a hundred gigabytes, 500 columns, and 10 million rows.

You can pick and choose whatever works for you based on what you need and what you can afford. Trying the free version first will also give you a quick idea of what the software is doing for you and whether you actually need it. If you have a positive feeling about it and would like to invest in the time, you can purchase the Plus or Professional version.

How To Use The IBM Watson Analytics

How To Use The IBM Watson Analytics

It’s not hard to use the IBM Watson analytics; it just requires a little exploring and a lot of practice.

  • Get started by navigating through their site and signing into your IBM account. If you don’t already have one of these, register yourself as a free or paid user.
  • The process starts by loading the data and shaping it. It explores all the data and discovers insights, uses visualization tools and dashboarding features to help display, and communicates these insights to the end-user, that is, you.
  • You can go into your account settings and check out your current account. The tab called Overview gives you the user’s information like your username, active licenses, subscription type, space you’re using, purchase history, and other details.
  • The next step is to import and refine data. Import data by downloading the CSV file on your computer. Go to the Data tab and choose “New Data” to import the local file.
  • The next step is data refinement. Click on the ellipses and check out the refine options. You can select particular fields to visualize every value and set specific conditions.
  • You can also see three expandable windows called:
    1. Column Properties (which reviews data, sorting options, and aggregation modes),
    2. Data Metrics (it summarises data quality, distribution, and missing values), and
    3. Actions (exposes a complete list of auto-detected hierarchies, other available columns, and creates calculations).
  • You can also use Watson’s cognitive skills to discover insights. Click on the Airline Satisfaction Survey- Refined Dataset. It exposes all the cognitive starting points, relationships, and trends that the software has detected throughout the uploading process. 
  • You can utilize Watson’s natural language processing capabilities, as mentioned earlier.
  • You can create custom visualizations by choosing a combination chart and dragging it onto a column tray or the x-axis.

There are many other features and components of this software, and it will be hard to put it all in a nutshell here. However, these are all the basics of using the tool. The rest is all about how you experiment and explore the platform to get the most out of it.

The Different Applications Of IBM Watson

Applications Of IBM Watson

Companies worldwide are using IBM Watson Analytics, and it’s helping in every industry you can think of. The following are only some of the many different applications of this genius tool:

Finance

The finance sector is also taking advantage of Watson, particularly its capability of questioning and answering. Watson helps give useful financial guidance by answering questions and analyzing them efficiently. It also helps lower, as well as manage, all the financial risks of an organization. For example, the Singaporean DBS bank utilizes IBM to ensure customers get proper advice and adequate guidance for customers. Similarly, an Australian-based company ANZ Global Wealth is using Watson for this exact purpose. The company explicitly prefers the Watson Engagement Advisor Tool and observes customer questions to improve their experiences.

Healthcare

Watson has massively impacted the medical industry. The top three cancer hospitals in the U.S., namely, the Mayo Clinic, University of Texas MD Anderson Cancer Centre, and Memorial Sloan Kettering Cancer Centre, use the tool everyday. In these hospitals and centers, IBM is helping with patient care and cancer research. In terms of the latter, Watson helps speed up the process of DNA analysis for cancer patients. As a result, it helps make treatment procedures more efficient and effective.

Moreover, physicians are using Watson for help with diagnoses. SchEMA, a digital application, enables doctors to put in the patient data, use NLP (natural language processing), and identify potential symptoms with their respective treatments. Plus, IBM utilizes vision recognition and helps doctors read x-rays, MRIs, and other scans. It helps them identify possible ailments quickly and narrow their focus. As a result, it saves time, ensures that they don’t miss anything important on the scan, and helps make an accurate judgment. 

Retail

Today, modern retail consumers prefer personalization, and, luckily, Watson allows you to do that. It helps you gather valuable data and present your products and services in a way that maximizes profit. For example, Sell Points created an application called Natural Selection, which uses IBM Watson Analytics in a very genius way. It basically takes advantage of the tool’s natural language processing capabilities and presents products to the shopper at the ideal point in the buying process. This way, they successfully lower the total click numbers until conversion. 

Another good example is online travel purchasing. WayBkazer, a travel company, created a unique Discovery Engine that utilizes IBM to collect data and analyze it. Then, it links the data to additional offers and customizes the lists of products for individual shoppers. This way, the company subtly yet effectively boosts its sales and improves customer experience.

Law and Legality

This one might be a little hard to imagine, but it is definitely practical, and it’s happening. Companies and organizations are using Watson to make law information more accessible and easy to obtain. They’re aiming to create more awareness, promote the law’s understanding, and help users understand legal knowledge and use it to their benefit. 

ROSS Intelligence Inc. is one of the example startups using IBM for law and doing it successfully. The company is using Watson to obtain answers to legal questions easily and quickly. As per their website, consumers have the opportunity to ask questions on the site and get quick, informative, and accurate answers within seconds. You can use plain English. The application then uses NLP to interpret your questions. Then, it efficiently filters through an entire database to find you a cited answer to your problem with legislation that’s relevant to it. The company also effectively monitors any potential alterations and changes that happen in the legal world. It then alerts you if any changes have occurred. 

Another example is the Singaporean organization called the Inland Revenue Authority, which uses Watson to answer the most recurring and most important legal questions about taxes. 

IBM Watson Analytics Architecture

The architecture of IBM Watson analytics is not as complicated as you would think. It basically comprises three Ds: Data, Discovery, and Display. 

  1. Data refers to the information it gathers from online platforms, user interaction, and manual input.
  2. Discovery refers to the analysis and processing of the data. It’s basically discovering data and making sense out of it.
  3. The Display means showing the data and explaining it to the user or audience in the simplest way. Finally, the data is collected at the beginning turns into understandable, readable information. Using this information, users can make quick decisions about the future.

IBM Watson Analytics vs. Microsoft Azure

IBM Watson Analytics vs. Microsoft Azure

While it’s a standard comparison, the IBM Watson Analytics is very different from Microsoft Azure. The latter is a machine learning software or tool. Azure automates specific tasks in the pipeline while assuming familiarity with the basic techniques of data science. On the contrary, IBM Watson Analytics is an interface that allows the deposition of data. You can ask your questions in simple, everyday English, and it will use natural language processing to give you answers that make sense to you. However, the similarity between the two is that they are both simple to use and have an awe-inspiring design. They both make questioning and answering easier, albeit having different ways of doing so.

In a nutshell, both tools and software present different features even though there are some overlapping aspects. IBM aims to make interrogation of data possible for the layman. In contrast, Microsoft Azure offers a user-friendly interface, making machine learning tasks more modest and more comfortable for a user. It integrates machine learning with the existing workflow of a business. 

For Microsoft Azure consulting: https://www.existbi.com/technology-consulting/microsoft-power-bi-consulting/

IBM Cognos

IBM Cognos Analytics, or IBM Cognos Business Intelligence, is a web-based software, an integrated IBM business intelligence suite. It basically provides a complete toolset that enables monitoring, scorecard, analyzing, and reporting metrics and events. The software also consists of many components specifically designed to meet all the necessary information requirements of a company. 

In simpler words, Cognos allows the user to create dashboards that are intelligent and interactive. This way, it helps businesses make important, informed decisions. It is based on machine learning systems and artificial intelligence, which helps automate all the data creation processes. It also helps analyze the data and allows the users to obtain required relevant answers to all of their questions.

IBM Cognos offers a free trial for unsure people or just wants to try the tool first before investing in it. As a user, you can explore the entire product with all the features for a maximum of 30 days. Then, you must purchase one of the two paid plans: Premium or Enterprise. 

IBM Cognos Training

Using the IBM Cognos can be a little tricky, so specific training programs make you a certified Cognos user. Most of these training programs are online courses that involve business intelligence, data warehousing, Cognos Analytics, and much more.

If you want to know more about how the IBM Cognos training can benefit your organization and the IBM Cognos’ different insights, ExistBi has everything in detail. You can find multiple articles on the subject, including tips, tricks, and other valuable information. Enlighten yourself!

Pros & Cons Of IBM Watson Analytics

Even though IBM has been a massive hit and is being used in various industries worldwide, it still has its benefits and drawbacks. While we’re still on the subject, let’s take a look into what exactly you should expect from IBM Watson Analytics. 

Pros: 

  • Easily understandable user interface
  • Strong, secure querying
  • Information in a visually appealing format
  • Fast analytics
  • Accessible from various gadgets and devices
  • Capacity to process natural language
  • Technologically-advanced guidance features
  • Patterns are easy to detect
  • Faster future decisions

Cons 

  • Lacks the option to stream real-time
  • Doesn’t cooperate with relational databases

Conclusion

The IBM Watson analytics quickly made its way into most data exploration offices. Thanks to the features, help, and guidance it offered, the tool became a crowd-favorite very soon. It helped organizations make quick, evidence-based decisions, and businesses that utilize data as evidence grow very quickly! More information on IBM Watson, IBM Cognos training, and other relevant information is on ExistBi. Hop on to the website and make sure you go through all of it. It’ll help you decide what your team needs and what you should and should not invest in. Besides, we all want the best for our business, right?


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ExistBI’s MicroStrategy Consulting Services That Facilitate Self Service and Empowerment

If you are feeling lost in the quagmire of data and struggling to make sense of it then ExistBI can help you with data management and strategizing through its MicroStrategy Consulting Services. It can help you make use of your data assets and actualize its value. This service can enable you to effectively and rapidly respond to changes in the market.

Steps Involved In ExistBI’s MicroStrategy Consulting Services

ExistBI’s MicroStrategy Consulting services are aimed towards knowledge creation, self service and empowerment. They comprise the following steps:

Assess

  • Assess and document a business’s needs
  • Determine the scope of implementation
  • Take into account the available resources

Strategize 

  • Outline a comprehensive conceptual solution
  • Chalk out the approach and process
  • Compute complex data
  • Test the approach

Implement

  • Implement the solution
  • Deploy the necessary resources
  • Develop and deliver the necessary assistance, training and help manual
  • Review the process

ExistBI can equip you with tools that can help you run your business efficiently and effectively. Our MicroStrategy consulting services are scalable to an organization’s needs and size. Reach us out on our US number +1 800 280 4376 or our UK number +44 (0)207 554 8568.


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Why Data Management for Small Businesses Is Very Crucial?

Imagine you have a business of your own, and you’re so busy making products and selling them that you have no clue where all the data is. At the end of the day, you’d have no idea how many products you sold, who you sold them to, and what kind of profit or loss you made. That is basically why you need data management for small businesses.

Small businesses usually spend a lot of time managing finances, delivering products, and maintaining profitable commerce. However, amidst all of these priorities, they forget about one essential aspect- their data.

If you’re interested to know what data management comprises and what it does for small companies, scroll down this article and read it thoroughly. You won’t regret it.

Data Management for Small Businesses

What Is Data Management, Anyway?

Consider data management an administrative process. Its job is to acquire, validate, store, process, and protect necessary data. This way, it ensures easy reachability, accessibility, timeliness, and reliability of your data.

If that definition was too complicated for you, here’s a more straightforward explanation:

If you run a business, the data that is relevant to your products and services, as well as your audience, is essential to you for many reasons. So, you probably write everything down in a register. This register now holds your data, and the hardcover protects it from environmental damage, right? Essentially, you’re managing your data! This is data management and how people used to do it several decades ago. Today, you have software and tools that can collect data for you. Also, some specific professionals are dedicated to data management only – data managers. And they don’t just hold and protect your data; they also organize, verify, and process it.

Who Is A Data Manager? What Does He Do?

Data managers are knowledgeable professionals, educated in their specific field of, you guessed it, data management. They help develop and govern systems that are primarily data-oriented. These systems aim to meet an organization’s needs and make decision-making more straightforward and more efficient. 

The various functions of a data management consulting services include:

  1. Raising more awareness about data and its importance across an entire organization
  2. Promoting the best practices and implementing them
  3. Promoting the adoption of useful and practical data-related guidelines, standards, and processes
  4. Lowering all types of duplicative efforts

As a data manager, you should also have the following essential and useful skills:

i. Being able to look at important, unorganized data and analyze it

Data analysis is a massive part of the management. It incorporates looking at various summaries and lists, identifying any patterns, and analyzing the results. Then, the data manager should be able to create presentations and make this data easily readable for other people in the team. His skills also involve using the information effectively and improving various programs after the analysis.

ii. Database Software Navigation 

As mentioned earlier, there are various software and online tools that help with data management. As a data manager, you should be able to navigate this software and use them creatively for benefits.

iii. Files And Account Management

One of the many vital skills of a data manager is to track all the online files and accounts efficiently. By doing this, they help other people in the team keep track of their own accounts, IDs, passwords, and further details. They should be able to organize different files and folders, both on a network and a computer. Plus, they should also have enough knowledge about copying, moving, uploading, and downloading different files and folders. Understanding emails, sending attachments, and managing the inbox is also a part of a data manager’s skill set.

iv. Designing And Planning A Database

Database design concepts should come easily to a data manager. They should understand the different benefits and limitations of various database types, like an online and PC database. Being able to actively participate in both short and long-term plans regarding database projects is a must. Moreover, figuring out an efficient storage and analysis plan is also one of the expertise of a skilled data manager.

v. Understanding and helping small businesses

Data management for small business is a skill. A new startup requires unique expertise in guidance, building effective plans, and implementing various techniques. A skilled data manager should be able to tackle a small business just as wisely as a large-scale organization.

For IBM Data Management Consulting: https://www.existbi.com/technology-consulting/ibm-consulting/

What Is The Goal Of Data Management?

The ultimate purpose and goal of data management are to help businesses, organizations, and individual people collect different forms of data and use it for beneficial reasons. They help business owners optimize their company and make decisions based on relevant, important information. It serves to prevent a business from wandering blindly in the market, making guesses, and risking their money. Its aim is to give the user more accurate details so they can take every step with calculated risk and a proper plan. 

ExistBI Provides Data Management Consulting

Why Data Management for Small Businesses is Necessary ?

Suppose you run a makeup store online, offering multiple kinds of skincare and beauty items. You take orders from customers online and deliver their parcels to their doorsteps. After a month, you will probably want to know how many people ordered from you, how many packages you delivered, and what profits you made. When, suddenly, you realize you never gathered all of that critical information. So, now, you have no idea what your audience likes, what products are more in-demand, and whether you made a net profit. 

Now, if you had all that information, you would be able to:

  1. Understand your target audience better and focus on their needs 
  2. Make your advertisements and marketing strategies more targeted so they would work more efficiently
  3. Innovate your products and services based on what’s more in-demand
  4. Fix prices based on their affordability and your desired profit
  5. Invest in products that give you more profit
  6. Simultaneously track how much profit or loss you’re getting 
  7. Take appropriate actions and decisions to grow your business and fix mistakes

This scenario explains precisely why businesses need proper data management. If they don’t collect, store, and protect the data they need, they cannot make relevant decisions to grow. It hinders their growth and development. They make guesses and risky decisions that should have otherwise been based on evidence and accurate data. 

Data management for small businesses is even more important because these decisions mentioned above are essential to their growth and development as a startup. It helps them move on the right track, be aware of their competition, and make appropriate decisions that are wholly based on real, accurate data. 

Read More: Big Data and Knowledge Management for Small Businesses

How Does Big Data Benefit Small Business?

There are several different ways that small businesses can put their data to fair use. Along with the ones mentioned above, here are other ways such companies can utilize their big data and benefit from it:

1. Identify customer preferences

As discussed earlier, your data helps you understand what your customers like best and what is not their favorite. Based on this information, you can invest in the products that your audience wants and is interested in. So, this way, your big data gives you leverage. It helps you attract your target audience as well as retain it. 

2. Identify different trends 

A data manager’s job is to identify different patterns, behaviors, and trends in the data. Are people moving more towards skincare as opposed to makeup? Is your audience reacting better to video ads instead of text and graphics? This type of big data helps you improvise and go with the flow. Otherwise, you’re just stuck in one place. You’ll keep doing what you have been doing for the past many years. Even though the trends have changed and these tactics do not work anymore, you won’t have any idea. Thus, it stops you from growing. 

3. Being aware of the competition

Thankfully, we have come very far from when businesses had to pretend to be customers to know more about their competition business and its insights. Today, financial data is readily available. You can do your research to determine which brands are doing better than you and precisely what they’re doing to make them better. 

4. Improving transactions, processes, and operations

Industrial and manufacturing companies can use big data to improve their operations by several folds. Machines show real-time data, they’re connected to various tools, and you can quicken many processes that initially involve a lot of time and effort. Retail companies can now successfully manage their stock based on data generated from their websites, weather-forecast, web searches, social media, and whatnot. The possibilities are endless if you’re an innovative individual.

5. Recruit and manage talent 

Individual data from within the business can help you identify more talented, devoted, and knowledgeable personnel. This way, you can engage them in activities that they could do better. It helps manage your team so you can get more efficiency out of every individual. 

6. Upgrade business model and strategies 

Suppose you’re noticing a more extensive response on fashion instead of beauty. If makeup and skincare aren’t generating enough revenue anymore, you can completely change your business model. You can transform your business into a clothing store, for example. Or, you can merge them both and add a clothing section to your makeup store. Big data helps you think of different ways to generate income and indicates when it’s time to upgrade. 

Data Management Guidelines

Some data management guidelines and suggested practices for small businesses:

If you are a small business, here are a few tips to make data management more effective and convenient for you:

1. A maintenance schedule is essential

A regular schedule to maintain data is non-negotiable. It helps ensure that your information has zero errors and there is no security risk.

2. Outsource when you can 

Contrary to what most people believe, outsourcing isn’t really that bad. Third-party operators can turn out to be a great help because they’re sometimes more equipped and ready to take care of data than you are. 

Read More: Data Management Services are Increasing Value and Importance of Business Data

3. Visual expression is always more effective.

When displaying data and explaining it to your team members, try to incorporate more visual explanations rather than texts. Use charts, graphs, and diagrams to present your findings so those who do not know data management can understand you better. This way, you can all be at the same level of understanding and make decisions quickly.  

4. Prioritize security

Considering how important your data is, don’t forget about prioritizing its security and privacy. Make sure you perform all the necessary measures to protect the business’s data from hackers, data thieves, and viruses. The security system should be consistent and very robust. 

5. Make sure you have a backup. 

Backing up data is usually forgotten, but it is actually crucial. Use online clouds and backup all your data there regularly. If not, save your information on an external hard drive or put it in a USB flash. Make sure you have it on you at all times for quick and easy backup. 

6. Allow access to the data. 

It’s good that you want to keep your data private and secure but don’t forget to make it accessible for other business members. You and your team members should not have to spend days trying to unlock and access the information they need. 

FAQS

Here are some faqs about data management…

Why Is Data Management Important In Businesses?

Without proper management of data, a company would completely lose all insights of the business. Everything depends on it, from making decisions and investments to recruiting employees. So, if a business is to grow, it requires proper data management.

How Can Small Businesses Utilize Big Data For Their Benefit?

Small businesses can use big data to decide what stocks they want to invest in and what should be left out. Plus, they can use it to make the right business strategy and be competent enough in the market. A new startup can sometimes have a hard time challenging its competitors. So, data management can become a strong backbone for these small businesses. 

What Is A Data Management Strategy?

Data management for small businesses involves a good strategy. It comprises precisely how you’re going to manage the data, the software, the tools, backup, and much more. In simpler words, it’s a roadmap for businesses to achieve the goals they have set. You can learn more about the various important benefits of having a sound data management system on ExistBi. Plus, they offer consultations to help you create a strategy as well.

In Conclusion 

Data management isn’t a complex and complicated concept. It’s actually quite simple, and if you’re using a register to write down your profits and losses, you’re already doing it. If you’re a small business, it’s important to take data management seriously. If you need help, guidance, and professional advice, Contact ExistBiWe have plenty of everything.


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Why Your Company Needs a Data Governance Framework

If your company is using data you need Data Governance Framework.  Some people may not believe that Data Governance is sexy, but it is essential for every org.  It doesn’t need to be a complex issue that adds controls and obstacles to getting things done. Data Governance consulting and the application of data governance policy should be a practical approach, designed to proactively manage the data that is most important.

In this blog, we are going to look at why your org. should be jumping at the chance to introduce data governance. When we tell people what we do, we get a mixed response. Some people seem genuinely surprised that everyone isn’t already doing Data Governance, and an awful lot of people ask why would you need that?

A few years ago the main driver of Data Governance initiatives was regulatory compliance and while that is definitely still a factor, there is a move towards companies embracing Data Governance for the business value which it can enable. For example, if your company is starting a digital transformation or wants to become “data-driven”, you are not going to be successful if your data is currently not well understood, managed, and is of poor quality (dirty data).

If you embrace Data Governance and achieve better quality data, many benefits begin to appear. But you don’t have to take our word for it; take a look at the DAMA DMBoK Wheel: 

Data Governance

As you can see, it lists all the Data Management disciplines around the outside of the wheel. There in the middle, at the heart of it all, is Data Governance because it provides the foundation for all other data management disciplines.

Let’s look at a few of these disciplines to illustrate the point:

DATA QUALITY

Without Data Governance all data quality efforts tend to be tactical at best. This means a company will be constantly cleaning or fixing data, perhaps adding default values when a key field has been left blank. With Data Governance in place, you will have processes, roles, and responsibilities to ensure that the root causes of poor data quality are identified and fixed so that data cleansing is not necessary on an on-going basis.

REFERENCE AND MASTER DATA

Anyone who has been involved in any master data projects will have no doubt heard or read numerous dire warnings about the dangers of attempting these without having Data Governance in place. While I am not a fan of wholesale scaremongering to get people to embrace Data Governance, these warnings are genuine.

For master data projects to be successful, you need data owners identified and definitions of all the fields involved drafted and agreed, as well as processes for how suspect matches will be dealt with. Without these things (which of course Data Governance provides) you are likely to be faced with a mess of under, over, or mismatching!

DATA SECURITY

Of course, Data Security is primarily an IT managed area, but it makes things a lot easier to manage consistently if there are agreed Data Owners in place to make decisions on who should and should not have access to a given set of data.

I hope you agree that these examples and explanations make sense, but don’t forget that is a theory, and explaining this in data management terms to your senior stakeholders in order to get agreement to start a Data Governance initiative is unlikely to be successful. Instead, you are going to need to explain it in terms of the benefits it will bring. The primary reason to do Data Governance is to improve the quality of data.  So the benefits of Data Governance are those things that will improve if the quality of your data improves.  This can cover a whole myriad of areas including the following:

IMPROVED EFFICIENCY

Have a look around your company. How many creative workarounds exist due to data issues? What costs could be reduced if all the manual cleansing and fixing of data were reduced or totally removed?

BETTER DECISIONS

We have to assume that the senior management in your Org. intends to make the best decisions. But what happens if they make those decisions based on reports that contain poor quality data? Better quality data leads to more accurate reporting.

COMPLIANCE

Very few companies operate in an industry that does not have to comply with some regulations, and many regulations now require that you manage your data better such as the California Consumer Privacy Act in the US or GDPR in the EU. Take GDPR (the General Data Protection Regulation), it impacts everyone who holds data on European Union Citizens (customers and employees) and having a solid Data Governance Framework in place will enable you to manage your data better and meet regulatory requirements.

So, at this point, you are probably thinking, “isn’t it just a generic best practice thing that everyone ought to do?” And the answer is, yes – I do believe that every company could benefit from having a Data Governance Framework that is appropriate for its needs.

WHAT HAPPENS IF YOU DON’T HAVE DATA GOVERNANCE?

Well I’ll leave that to you have a look around you and decide what the likely consequences for your company could be, but it is usually the opposite of the benefits that can be achieved.

Remember data is used for dealing with your customers, making decisions, generating reports, understanding revenue, and expenditures. Everyone from the Customer Service Team to your Senior Executive Team uses data and relies on it being good enough to use.

Data Governance provides the foundation so that everything else can work.  This will include obvious “data” activities like Master Data Management, Business Intelligence, Big Data Analytics, Machine Learning, and Artificial Intelligence.  But don’t get stuck thinking only in terms of data.  Lots of processes in your Company can go wrong if the data is wrong, leading to customer complaints, damaged stock, and halted production lines. Don’t limit your thinking to only data activities.


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Explaining Star and Snowflake Schemas in Data Warehouse with Examples

If you are a Data expert who deals with data warehouse consulting and different schemas in data warehouses, you probably already know the importance of these terms. However, if you are a beginner, you probably don’t know the subjects’ basic knowledge. As a data expert, it is essential for you to understand these basic terminologies, what they mean, and what purpose they serve. Throughout this article, you will find everything you need to know about schemas in data warehouse. We will discuss their two significant types, Star schema, Snowflake schema, and each’s advantages and challenges.

What Are Schemas In A Data Warehouse?

Schemas in data warehouse are logical descriptions of a database. One schema is a complete collection of objects like synonyms, indexes, views, and tables from a database. You can arrange schema objects in a variety of ways in different models for data warehousing.

Different kinds of schemas in data warehouses include Galaxy schema, Star schema, and Snowflake schema. We will discuss two of them ahead, but if you want to know more about data warehouses, ExistBi has plenty of information on the subject. You can find out what a data warehouse is, why it is essential, its advantages and disadvantages, and everything else relevant.

What Is Star Schema? 

As mentioned earlier, one of the two schemas in data warehouse is the Star schema. It is undoubtedly the most straightforward data mart schema styling. Therefore, it is one of the most widely used approaches when developing dimensional data marts and data warehouses.

Schemas in Data warehouse

A star schema’s characteristics and components include a dimension table that is connected to a fact table through a foreign key. The schema also includes dimension tables that are not interrelated. Other characteristics include BI tools that support a schema, non-normalized dimension tables, easy understandability, and disk usage.

Designing A Star Schema: 

Creating a Star schema isn’t a tough job if you know what you’re doing. Understanding how to make it can also clarify many concepts regarding the topic, like what it’s made of, how complex it is, and how you can enhance its usage. Here, the process is broken down into simple steps for you to understand:

Step 1: Identification after the business process to analyze. These business processes include sales.

Step 2: Identification of the facts and measures, such as the sales dollar.

Step 3: Identification of the various factual dimensions. These include the organization dimension, time dimension, location dimension, and product dimension.

Step 4: Organization of the columns describing every dimension, including the region name, branch name, etc. Lining up these dimensions and organizing them is an important aspect of the job.

Step 5: Determination of a fact table’s lowest summary level, which includes the sales dollar.

And that’s how you create a Star schema on your own!

Its Advantages:

The star schema is so widely used because it has several benefits over types of schemas. Some of these fantastic benefits are the following:

  1. Star schemas have a higher speed, and they are relatively faster. 
  2. Their read-only performance is very high and efficient. 
  3. Theories are more compatible and manageable to perform since one large table of data represents various dimension tables. 
  4. The star schema provides data for Online Analytical Processing systems. 
  5. It also simplifies the transactions of making the period over period and business reports. 

What Is Snowflake? 

This one is the other type of significant schema in data warehouse. Snowflake schemas are logical arrangements of various tables In a single multi-dimensional database. This arrangement happens so that the diagram mimics the shape of a snowflake, hence its name. This particular schema is actually an extension of the Star schema, meaning that they’re both pretty similar with added dimensions. In this schema, however, the dimensional table is normalized and divides the data into various separate tables.  

Snowflake Schema

A snowflake schema comes with its own interesting characteristics. For example, they are relatively more high maintenance And require more effort because of the excessive lookup tables. Plus, they involve multiple tables query, so the performance is somewhat reduced. They take more time and effort than the Star schema, which is why it intimidates many people. However, if you know how to make it and understand its composition, you can slowly start to like it! 

Designing a Snowflake Schema: 

Like the characteristics, creating a Snowflake Schema is also different from that of a Star schema. The following parameters are a part of this process: 

  1. Name: you must create a unique name for your schema.
  2. Transient: it presents a schema that is temporary and volatile. Hence, it is automatically deleted once you terminate the session. 
  3. Clone: a clone creates an identical copy of a schema that already exists. You simply have to enter the specific name of the selected schema. 
  4. At|Before: this part provides a timestamp for cloning an existing schema. It chooses a particular period from where you wish to copy the data. 
  5. With Managed Access: this particular field identifies managed schemas. It adds you to monitor your access controls. 
  6. Data Retention: data retention specifies a particular number of days that the object remains retained within the memory. Data retention has a default value of 1, but you can alter it as you wish. 
  7. Comments: the comments provide a minimalistic description of your schema that you just created. 

And this way, you can create your own schema using these specific components of a Snowflake schema model. 

Its Advantages:

Despite the challenging characteristics we just discussed above, there are some significant advantages of the Snowflake schema. These benefits include:

  • A Snowflake schema occupies a much smaller amount of disk space compared to the Star schema. Lesser disk space means more convenience and less hassle. 
  • Snowflake schema of small protection from various Data integrity issues. Most people tend to prefer the Snowflake schema because of how safe if it is.

For Snowflake Consulting: https://www.existbi.com/technology-consulting/snowflake-consulting/

Which Schema Is Best For A Data Warehouse?

Considering that both the systems have their perks and drawbacks, different experts prefer Snowflake and Star schema depending on their needs and preferences. The Snowflake schemas generally take up less space, which is always convenient. However, the Star schema is much faster and involves a more straightforward design. So, depending on what your priorities and needs are, you can choose one that fits you best.

That being said, IT teams around the world generally like to prefer the Star schema versus the snowflake schema. This worldwide preference is a result of several reasons. One of these reasons is that a star schema consists of one or more tables, much more straightforward than the other schema. Since this schema does not compromise the team’s speed and efficiency, experts around the world tend to widely use the Star schema, as mentioned in the beginning.

Examples of Dimensional Schemas

Apart from the Star schema and Snowflake schema, there is another type of schemas as well. It’s called the Galaxy schema or Fact Constellation Schema.

This one is another extension of the star schema and is a collection of multiple stars. A fact constellation measures online analytical processing, and it consists of dimensions segregated into several independent ones depending on their hierarchy levels. It has various fact tables and is often called a Galaxy schema, even though some argue that they’re both different systems. At this point, there is quite a lot of mixed information and opinions you’ll find on the web. 

For example, suppose geography has a total of five hierarchy levels. These include city, state, country, region, and territory. In such a case, a fact constellation schema would consist of five dimensions and not one. Also, if you split a 1-star schema into multiple star schemes, you can generate a Galaxy schema. The sizes are relatively more extensive in a Galaxy schema, and it is helpful to aggregate fact tables and get a better understanding of the data. 

Snowflake schema in Data Warehouse

Is Snowflake OLAP or OLTP?

Before discussing the answer to this question, let’s first discuss the terms OLTP and OLAP and what they stand for.

Both of these are different systems. OLTP refers to online transaction processing, which gathers data from various transactions and stores, processes, and captures them in real-time. On the other side, OLAP involves analyzing aggregated historical data through complex queries from OLTP systems. 

Now, let us use this information and co-relate it with the question. Apparently, a snowflake schema is an OLAP system and was specifically designed to be one. One of the most significant and highlighted aspects of a Snowflake schema is that it separates between processing and storage, clearly making it an OLAP database.

For IBM Cognos Transformer: Design OLAP Models Training: https://www.existbi.com/ibm-cognos-training/ibm-cognos-transformer-design-olap-models-training/

What Are The Major Differences Between The Star And Snowflake Schemas?

Indeed, different schemas in data warehouses are an extension of each other, and they have a lot in common. However, they are significantly different from each other in various aspects. For example, even though the Snowflake schema is an extension of the Star schema, some characteristics differ massively between the two. These differences are discussed below in detail:

  1. The star schema offers queries with relatively higher performance through the Star Join Query Optimisation system. The tables in the schema can connect through multiple dimensions. In contrast, the Snowflake schema involves a centralized fact table, improbable to connect with other various dimensions.
  2. Cube processing is much faster in a Star schema as compared to the Snowflake schema. The reason behind this difference, as mentioned earlier, is because a Snowflake schema is much more complicated, and it requires more time and effort.
  3. Thanks to this reduced time and effort, the productivity and efficiency levels are much higher for star schemas compared to Snowflake schemas. Since the processes are simpler and easier, transactions are smoother, and results are faster, more accurate.
  4. Star schema also has a higher data redundancy. In contrast, a Snowflake schema has deficient levels of data redundancy.
  5. The single dimension table of a Star schema consists of aggregated data while the data is split into various dimension tables in a snowflake schema.
  6. Star schemas have a de-normalized data structure, which is why their queries run much faster. On the opposite side, a Snowflake schema has a normalized data structure.
  7. A Star schema has a relatively more uncomplicated and more straightforward DB design, while a snowflake schema has more complex and complicated DB designs.
  8. Star schemas involve a single join only, which generates a relationship between dimension tables and a fact table. A Snowflake schema, however, needs multiple joins to gather the data and collect it.
  9. Star schemas involve fact tables that are surrounded by multiple dimension tables. A snowflake schema contains just one fact table that is surrounded by dimension tables.
  10. Hierarchies in a star schema gather in a dimensional table, while the hierarchies in a snowflake schema are further divided into multiple tables.

Which Schema Is Faster, Star Or Snowflake?

As discussed earlier, Star schemas are widely popular for their fast speed and efficiency. Since their dimension tables and fact tables are much more straightforward, they result in faster, more straightforward SQL queries. For this reason, IT teams and specialists around the world prefer to use the Star schema since it provides aid and speeds up their work. Snowflake schemas, on the other hand, use less space compared to a Star schema, but they are relatively more complex. They require more effort, so they take more time and lower efficiency. 

Conclusion

Various schemas in data warehouses serve different purposes but understanding them is essential for professionals. Identifying which schemas work best in specific scenarios can help you identify what would work best and how you can maximize efficiency. For a data warehouse expert, this knowledge is essential.

If you lack the necessary expertise in data warehouse, check out ExistBi first and read through the articles related to data warehouses. Once you understand the basics of a data warehouse and how it works, you can come back and learn more about the schemas. If you wish to take Snowflake consulting services and professional guidance, you can also find this particular facility on ExistBi.


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Tableau 2019.4 with Webhooks to Build Custom Workflows – Tableau Bootcamp

It’s obvious that people don’t like to wait for things to occur. For example, you don’t want to check your email box again and again until you get a notification or alert of an incoming email. And while working on a tableau 2019.4 platform, you also want to have the same service.

In a Tableau Bootcamp, you’ll discover that almost every tableau user has created some processes and events on this platform, building a workflow from certification of data sources to filing a ticket. Many of these procedures need you to continuously check Tableau to see if something you want to happen, and then respond.

Tableau 2019.4

Therefore, Webhooks has been added to the Tableau Developer Platform in the upcoming release of Tableau 2019.4. Webhooks allows you to generate the specific workflows on the tableau. So when an event occurs, you’ll get the notification on a specified gadget. Hence, when a workflow is created, you don’t need to wait for its completion and have to check repeatedly.

What are Webhooks?

Webhooks are simple techniques through which one computer system is able to notify another when an event happens by using typical web technologies, like HTTP and JSON. Webhooks enables you to join Tableau to your apps, which means any action in Tableau Server or Online can trigger a different app. To understand this in a simple way for initial setups, review this example, it will send an e-mail alert whenever a new workbook is published or deleted. And in complex setups, you can integrate various Tableau triggers to refresh extracts in superior workflows. Webhooks brings in a lot of stimulating opportunities to automate your Tableau usage. So here you’ll know what Webhooks are, why you should use Webhooks, and how can you use them in your Tableau setup.

Tableau 2019.4

Assume that you have any System X handling lots of works, and System Y needs to respond to some particular works or processes taking place on System X. So here you get a few options:

  1. Constantly Checking: – Continuously keep an eye on system X to check if the particular task is going straight or not.  In that case, System Y has to uphold a copy of the preceding status of System X and continually test out for a new state that imparts some additional load on System X. Both systems are doing a ton of extra work for something that may not happen that often.
  2. Scheduled Checking: -Check the System X after a specific time interval or at a scheduled time. The load is compacted by merely checking from time to time, but also depends on the schedule period. There may be extensive delays for checking by System Y whenever anything happens on System X.
  3. Requesting Notification: – In this process, System Y asks System X to informs when some specified events occur and after that waits for the notification. And when anything happens, System X informs System Y.

What is the Use of Webhooks in Tableau?

Webhooks has great potential to perform the following works:

  • When refreshing an extract fails, automatically it files a ticket in Service.
  • When updating of the workbook completes, it notifies your team through their Slack channel.
  • When publishing of data source is done, emails a data steward requesting the team to evaluate and verify it.
  • When refreshing the workbook is completed successfully, produce a PDF, and publish it to SharePoint.

Webhooks will always inform you when something happens in a system on Tableau, so the information will help you to understand that when you can proceed further. In the preliminary release of Webhooks with Tableau 2019.4, there are only 13 events accessible to create custom workflows:

  • For Workbook
  • Workbook Created                       
  • Workbook Updated
  • Workbook Deleted
  • Workbook Refresh Started
  • Workbook Refresh Succeeded
  • Workbook Refresh Failed
  • Workbook View Deleted
  • For Data Source
  • Data Source Created
  • Data Source Updated
  • Data Source Deleted
  • Data Source Refresh Started
  • Data Source Refresh Succeeded
  • Data Source Refresh Failed

With the upcoming releases, more events are expected to be added to the workflow in Tableau 2019.4.

Create and Manage Webhooks

Site and System admins are allowed to create and manage Webhooks with RESR API within the site. Either you can write your own code for this, or you can utilize the Postman API Client tool from the existing Webhooks REST API collection. “Postman” is a great tool that allows easy access to RESTful API, and you don’t require writing code.

For creating a Webhook message, these three things need to be specified in Webhooks to issue a create command in endpoint:

  • Event for which you want notification/alert
  • URL where you want to receive the message
  • Name defined for the task done by Webhook

Testing of Webhooks

You must need to verify and test the created Webhook carefully to find whether it is working correctly or not, and then you can build your workflow accurately. Luckily, you’ve got a number of sites, such as webhook. site or testwebhooks.com, which provides free access to test your Webhooks without doing any kind of setup. They do provide a temporary URL to point at your Webhook.

For testing the Webhook, point it at the URL presented by the site and click the Webhook. If everything is fine and functioning well, a pop-up message will appear inclusive of information regarding the event.

Responding to Webhooks

You need a well-developed system for responding to the messages received through Webhooks. You might require an IT specialist or developer to build such a program. Moreover, there are several low-code websites like Zapier and Automate.io, which offer native support for Webhooks and help to create automated workflows.

Start Automating Your Workflows!

Webhooks is a general approach to activate automated workflows that counter any action on events in your Tableau environment. So you can initiate creating custom workflows with Tableau Server and Tableau Online with the upcoming release of Tableau 2019.4.

You can sign up for a Tableau Bootcamp consulting to discover more features and functionalities of Webhooks in Tableau.  ExistBI offers Tableau training and Tableau consulting in the US, UK, and Europe.  Join the Tableau 2019.4 beta to start creating automatic workflows today.


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Top 12 Business Intelligence Trends Of 2021

Business intelligence revolves around different technologies and strategies to analyze business information. Simply put, it allows you to interpret the stats of your business, measure its growth, and make appropriate decisions based on data. The reason why we’re discussing this complex topic is its rising importance in the world of commerce. Today, every business person should know what business intelligence is and how they can incorporate it to develop their company. If you’re interested to learn more about it, let’s discuss the top twelve business intelligence trends in 2021.

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Business Intelligence trends

Top 12 Business Intelligence trends of 2021

Considering how fast technology and businesses are developing, it is essential today for every business person to be aware of the current and upcoming business intelligence trends. In order to help you be a step ahead of the game, here are the top twelve business intelligence trends you should work on.

1. Collaborative Business Intelligence

Interaction within a business was always important. However, the newest trends involve a different kind of communication. We’re talking about the latest tools and software to communicate within a business community. These include modern technologies, social media, and various applications. Such advanced and real-time communication allows the business to make collective decisions based on collaborative information and information enhancement.

2. Data Discovery and Visualization

Data A has only increased in terms of importance during the last year. Naturally, data discovery tools have also seen a spike and are expected to become more in-demand in 2021. Similarly, the use of tools for online data visualization has also become a crowd-favorite. They have become a valuable resource for developing relevant insights and a sustainable process for business decision-making. Furthermore, these tools enable easy management of various kinds of heavy volume data. Plus, they are straightforward to use, highly flexible, and reduce the time and effort to insight.

3. Self-service Business Intelligence

The world of technology is changing so fast that you no longer need extensive teams and professionals to handle analytics tasks for a business. The latest trends in business intelligence promote self-service interfaces so you can manage your own analytical procedures. Such services and tools allow business users to handle data tasks themselves without IT teams and data scientists’ involvement. It is especially beneficial for small businesses that cannot yet afford to hire professionals.

4. Mobile Business Intelligence

Since 2020 was all about using your smartphone for everything, even business intelligence has made its way into your pocket. The newest trends support access to BI data and tools through your mobile phone. It’s not only going to make navigation more comfortable but also reduces the need to carry bulky computers and laptops all the time.

Mobile Data Analytics

5. Embedded Analytics

Embedded business intelligence significantly decreases the workload of a data worker. It gives them a much faster way to generate insights so they can focus on more things. These analytics provide the users with better power to work with data by zooming in, aggregating it, and looking at it from multiple angles by just pressing a button. Since it’s so beneficial for the users and data workers, this trend will hopefully see a significant spike in 2021.

6. Story Telling

It has always been a concern that business owners and managers cannot interpret and understand the data provided and interpreted by analysts. Since they don’t have the proper knowledge or know-how of the jargon, they cannot utilize all of that valuable information to make the right decisions.

However, in 2021, this problem will hopefully see a solution. The upcoming trends involve analysts describing the information in a very story-telling manner. This particular technique adds a little context to the data and statistics. This way, it provides a proper narrative for business management to use all the insights and make the right decisions.

7. Data Governance

The process of data governance stipulates blueprints to manage a business’s data assets. It includes the process, architecture, and operational infrastructure. Simply put, data governance forms a robust foundation for organizations to manage their data on a broader scale. Overall, the process impacts the strategic, tactical, and operational levels of an organization and, as a result, helps the business use the data as efficiently as possible. The trend is seeing a significant rise and will be even more popular in 2021. Why? Because it will help instill confidence in business leaders and promote the use of business intelligence.

8. Connected Clouds

The use of connected clouds has seen a significant spike during 2020 for obvious reasons. However, it is easy to say that this trend is not going anywhere in 2021 either. The cloud-connection strategy reduces costs and risks associated with data work. Plus, it provides the required flexibility to develop relevant essential data and use it to make data-driven decisions. Moreover, it enhances the quality of real-time communication within the business, and we’ve already discussed how important that is.

9. Data Security

After the significant security breaches on popular online platforms, like Facebook, businesses and consumers have become aware of how important this concern is. As a result, data security trends have been on the rise since 2019. Experts say that the trend will prevail in 2021 as well. Database security has become a priority for all businesses to avoid breaches and cyber-attacks. They’re taking appropriate measures, using the right tools, and taking the proper precautions to make sure it never happens again.

10. Artificial Intelligence

In the last decade or so, artificial intelligence has improved by several folds. Lately, it has started to make a significant appearance in business intelligence as well. Speculations for the year 2021 say that the latest digital assistants will make work even easier for data workers. They will simplify business intelligence processes through voice-activation, voice transcription, and efficient conversion of data.

11. Predictive And Prescriptive Analytics Tools

Predictive analytics allow data workers to extract information from a bundle of data and set it in order. Doing this will enable them to forecast probabilities for the future and take the necessary precautions and actions. Similarly, prescriptive tools are a step further than that. They help you examine data and content to make essential decisions and take the right steps to achieve a goal. The techniques involved in the prescriptive analysis include simulation, graph analysis, neural networks, heuristics, complex event processing, machine learning, and recommendation engines. All of these techniques help you optimize production, scheduling, supply chain, and inventory to deliver to your customers efficiently.

12. Real-Time Data And Analytics

Up-to-date information and real-time data have become more and more critical during the last several years. Thanks to the quick collection of information, analysts can now be fully and quickly aware of the business’s ups and downs. In 2021, this trend will also see a significant spike. The analytics industry and business intelligence will incorporate more real-time data for forecasting, alarms, and business development strategies. Based on the real-time data, they will respond appropriately and make the right data-driven decisions.

FAQs about Business Intelligence

Apart from the significant upcoming business intelligence trends, there are certain other aspects that most people are curious about.

1. Does Business Intelligence Have A Future?

The use of the latest technology, artificial intelligence, and efficient strategies have become more critical for businesses and organizations. There is overwhelming pressure on various industries to implement all of these changes. It’s becoming an absolute necessity for companies and bodies worldwide to adapt to these changes. As a result, the incorporation of business intelligence is helping companies stay relevant, robust, and competitive. 

As for the BI industry’s future, the trends that have been on the rise and are upcoming in 2021 seem to promise a rapid shift in the business intelligence landscape. It’s safe to say that yes, business intelligence has a bright and shining future!

If you are willing to learn more about it, ExistBi offers business intelligence consulting services. You can know more about what it is and how you can use it.

business intelligence

2. What Companies Use Business Intelligence?

Generally speaking, almost every kind of company and organization can take help from business intelligence. If you are looking for some real-world examples, here are a few of the most popular brands that go hand-in-hand with business intelligence: 

  • Amazon 
  • Chipotle
  • Coca-Cola
  • Hello Fresh
  • REI 
  • Starbucks
  • Des Moines Public Schools, and many more. 

3. What Are Some New Uses Of Business Analytics That May Become Possible With This Trend In The Next Few Years?

If 2021 goes as planned and speculations come out correct, business data will become easier to interpret. Everyone will be able to collect, analyze, and use data for proper business development, strategies, and growth. Also, data and content will become more secure, and consumers will feel safer purchasing and transacting online. 

Also, small businesses will no longer need to hire extensive teams and expensive professionals. They can use online and offline data tools to do the job on their own. Doing this will reduce the cost of their business maintenance and also be a more sustainable option. Moreover, we will be able to analyze business data from different sources at a time. Advanced tools will help find any hidden patterns in large sets of data. Interactive reports and dashboards will help disseminate important information to the relevant stakeholders. Businesses will be able to react and monitor KPIs according to the changing trends and in real-time.

4. What Are The Different Stages Of Business Intelligence?

Essentially, business intelligence has four stages: 

a. Information Gathering

This step involves preparing data from all of your existing sources like your files and financial database. You can also collect data externally from online surveys, questionnaires, polls, or other people. Once this feedback data is collected, you can move on to the next step.

b. Analysis

Analysis of the data involves turning this raw data into valuable information. There are three significant kinds of analysis: spreadsheet analysis, visualization tools, and software, which allows the user to develop specific data queries.

c. Reporting

Once you’ve analyzed all the data, you need to make a report on it. You can use tools and software to filter and define the information and make it interpretable for the receiver. For example, you can represent the final data in the form of tables, graphs, or diagrams.

d. Monitoring & Prediction

The final part of a business intelligence process takes you back to the first page. You monitor the data that you first collected and notice any changes or ups and downs. Monitoring has three common types: Dashboard, KPIs (key performance indicators), and business performance management. 

Then, you predict. Prediction helps you foresee the future and make appropriate decisions accordingly. The prediction part has two major types in business intelligence: data mining and predictive modeling

Informatica PowerCenter Training

5. What Are The Benefits Of Business Intelligence?

Apart from all the advantages and benefits, we have discussed so far, here are some more benefits of incorporating business intelligence

  1. BI improves data quality. 
  2. It increases its competitive advantage. 
  3. Your rate and ratio of customer satisfaction improve dramatically. 
  4. Your employees are satisfied, and their retention rate improves. 
  5. The customer retention rate also improves significantly. 
  6. You can make better, quicker decisions about your business and organization. 
  7. Your planning, analysis, and reporting will become more accurate by using business intelligence systems. 
  8. Planning, analyzing, and reporting also becomes very fast and needs less time and effort. 
  9. The overall costs of your business development and maintenance reduce dramatically. It is especially beneficial for small startups or companies that are facing a significant financial downfall. 
  10. You will notice a saved headcount by using business intelligence. 
  11. Revenues will significantly increase since the quality of work improves, and the costs go down. 
  12. You will no longer need to depend on someone else or be unaware of what’s going on in your business. 
  13. You can take matters into your own hands and make decisions by fully understanding the context. 
  14. Since you are a significant part of the decision-making process and know what you’re doing, there will be fewer risks of failure and loss.

In Conclusion-Learning about Business Intelligence

If you wish to learn more regarding business intelligence, why it matters, and various examples of it, ExistBi can help. Hop on to the website to know the difference between modern business intelligence and traditional business intelligence, how to choose a BI tool, Business intelligence consulting, and much more.


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Online Data Science Courses During Covid-19 Crisis

The pandemic has made a significant impact on educational institutes everywhere. Colleges, universities, and institutions have resorted to online classes. Data science is one of the many subjects being taught online now. But this digital way to education has brought more perks than just safety.

Now, more and more people understand how the internet works and data scientists are using their knowledge and experience to teach online. Thanks to this wide range of availability, data science courses have become more accessible for interested students.

If you’re looking for such data science courses online, ExistBI has a wealth of experience and a variety of courses available for all ability levels. We’ll discuss what data science curriculums include and how you can learn online.

Data Science Courses

What Is Data Science About?

Let’s first briefly discuss what data science is and what it involves if you’re new to this concept and the big data industry. Data science utilizes systems, algorithms, processes, and scientific methods to extract necessary information from data. This data could be structured or unstructured. Data science revolves around machine learning and data mining.

In simple words, data science is the extraction of meaningful insights from a piece of data through domain expertise, statistics, and mathematics and programming skills.

What Does A Data Science Course Include? 

The entire course is a blend of various subjects, including:

  • Machine learning
  • Algorithms
  • Tools
  • Jameen expertise
  • Coding
  • Twitter analysis
  • Business acumen
  • Mathematics

The curriculum teaches the scientist how to identify and extract meaningful, useful, sometimes hidden, insights from a collection of raw data.

What Does A Data Scientist Do?

The useful information extracted by a data scientist helps businesses make crucial decisions. Analysis and structuring of raw data can help companies make valuable changes to their strategies, understand their profits and losses, and grow financially. Moreover, by sharing and extrapolating such useful insights, these scientists can help businesses come out of financial crises and solve many problems.

What Are the Major Differences between Data Science and Business Analytics

With the growing availability and accessibility to both these programs, people must learn the difference between them.

So, let’s compare them, shall we?

  • Data science is studying data with the help of technology, algorithms, and statistics. In contrast, business analytics involves statistically analyzing business data to gain insights.
  • Data science utilizes both structured and unstructured data, while business analytics mostly involves structured data.
  • Data science incorporates a lot of coding. It is a perfect blend of excellent computer knowledge and traditional analytics. In contrast, business analytics does not include a lot of coding. It is more oriented towards statistics.
  • In data science, scientists use statistics after finalizing the analysis after coding. In business analytics, however, the analyst completes the entire research on the basis of statistical concepts.
  • Data scientists study almost all kinds of trends and patterns, while business analysts study trends and patterns only specific to businesses.
  • Industries like e-commerce, machine learning, manufacturing, and finance are the topmost applications of data science. These industries are telecommunications, supply chain, marketing, retail, health care, and finance for business analytics.

So, as is evident here, both business analytics and data science are entirely different. Both fields incorporate different strategies, and both types of professionals have varying jobs and opportunities. The point of discussing this comparison was that you should know what you’re getting yourself into and what your opportunities look like. You should always know the difference between two similar professions, especially if you’re trying to pursue one of them.

Leveraging Data Science To Combat Covid-19

Amidst the pandemic, it has come to everyone’s attention that data scientists have become more valuable than ever. Indeed, their role in making businesses flourish is undoubtedly significant. However, since the pandemic started, they have also made a significant contribution in helping manage healthcare departments.

Since 2010, we have been growing our knowledge and capabilities in terms of algorithms, identifying patterns, and obtaining insights. However, since the pandemic hit us, the world of data science has seen abilities and potentials beyond what we ever imagined. Data scientists are now using their capabilities to help predict how the disease will affect various businesses and industries. So, is the pandemic opening a new door for data science? You might rightly think so.

Data scientists provide their skills and services for screening, analyzing, predicting, forecasting, tracing contacts, and developing drugs and solutions. If you think this is great, experts speculate that more of such services will come from the data science field very soon if the pandemic continues.

Experts speculate and hope that, soon, machine learning and data science will help categorize and predict which people are prone to getting the disease and which ones are immune and safe from Covid-19. Such categorization will be of immense help in many ways. It will help prioritize those who need the treatments and vaccinations first and who can wait. Plus, it will help reduce the spread of the disease and contain the damage.

Moreover, data science is also helping us keep track of Covid-19’s spread worldwide. Plus, we are also using data science at a macro level to assist what information and results disseminate and where. Such data and information help keep track of where the disease might spread next, where it can do the most damage, and how many waves we can expect.

Apart from these services, data science is also playing a vital role in making industries and businesses run. It’s helping companies make proper arrangements and decisions according to the data they receive and the information they structure out of it. This benefit applies to both private and government industries, and it’s helping many countries stabilize their economy and prevent them from collapsing.

Covid-19 Data Science Urban Epidemic Modelling And Visualization In Python

Since coronavirus spreads from person to person, to keep track of the virus among the population, you must keep track of the people. The disease goes where the people go. Understanding, analyzing, and predicting their movements can help us understand how the disease spreads, where it’ll spread to, and how to stop it effectively. Predicting where a disease virus is saturated and where it will move on to the next can help control the damage and minimize it.

Urban epidemic modeling and visualization in python is precisely this. Python is a coding language you will typically see in data science. Python is the most preferred, most popular programming language among data scientists worldwide. The reason for all this love for the language is how versatile it is. Data scientists use machine learning, special statistics, and complex networks to understand urban communities’ mobility.

Keeping track of the disease’s potential carriers can help you make proper predictions and appropriate measures to stop the disease’s spread. Data scientists use python to build epidemiological models while taking into account urban mobility patterns. Then, they convert the data into understandable, visually pleasing graphics and diagrams. This process includes simple mathematics, statistics, formulations, and special equations. Then, they present the information on a map so that it is easy to understand and interpret.

Online Data Science Courses During Covid-19 Crisis

With the rising demand for data scientists, more and more people are looking up to this profession and adopting it as a career. However, even those who are already certified need to take their game up a notch. The data world is transforming, and if you’re not up-to-date, you’ll soon be far behind the world and what it needs right now.

ExistBI provides valuable data science courses and consultation during the pandemic. If you are a learning data scientist, you can get Big Data training classes on ExistBI. The training classes include:

1. Big Data Training For Beginners

This one-day course aims to provide you with a basic competitive understanding of all the Big Data topics, primarily Hadoop. You’ll learn what Big Data is when we should consider something like Big Data, what a Big Data system architecture looks like, and much more. You’ll learn all about the ecosystem, the key players in the space of Big Data, and whether it’s related to technology and data volume. In the end, you’ll identify whether it can enhance the existing technologies or completely replace them.

What’s more, you don’t require any programming experience to enroll in this training. If you’re someone who needs a complete overview of what Big Data is, its various components, and more about the Hadoop ecosystem, this class is fit for you.

2. Fundamentals Of Big Data And Hadoop 

This three-day course aims to provide a maximum understanding of Big Data, including the basics as well as its usage. You will learn what Big Data is and what its architectural system looks like. Along with this basic knowledge, you’ll learn about the implementation of Hadoop jobs for the extraction of business values from vast and varying data sets. You’ll also learn the development of queries to simplify data analysis using Impala, Cassandra, Pig, and Hive.

3. Big Data Analytics Training

Big Data Analytics is a 3-day course that helps improve and expand your skills. The topics include data visualization, statistics, and data mining. You’ll learn how to analyze a larger, more massive amount of data and use this data for the management of risk. This way, you will help make businesses change their route from collapsing to flourishing. It will help to make crucial business and financial decisions.

This course aims to define Big Data Analytics, exploring big data, and explaining the difference between real-time data processing and batch processing. Moreover, you’ll experience both supervised and unsupervised learning and understand the difference between the two. Mining techniques, handling stream data, and defining strategies Big Data is all a part of this course.

4. Big Data For Advanced Learners

As the name suggests, this particular training program is for advanced learners. However, the good news is, you still don’t require any prior programming experience to take this training. (though if you have prior knowledge, it will certainly be helpful). The training course goes on for four days and includes hands-on exercises. These activities and exercises will help you gain a stronger understanding of the Big Data platform and ecosystem.

The entire course has four models, three of which include lectures with hands-on labs. Module one will introduce the subject, while two, three, and four will be all about the architecture, tools, and analytics.

5. Informatica Developer Tool For Big Data Development

This two-day training has objectives like data extraction from flat file sources and relations, parameterized mappings, development of mapping transformations that are most commonly used, and much more. The course is easily applicable to the 10th software version, and you can learn the Data Integration mechanics through the Developer Tool by Informatica. Through this course, you’ll learn about the key components of development, configuration, and deployment of data integration mappings.

6. Informatica Big Data For Developers

This one is a 3-day course that you can get on-site or virtually. You’ll learn the definition of big data, leverage the Informatica smart executor, and describe the way Informatica reads, writes, and parses data collections by NoSQL. This course has 11 modules in total. Each of them has valuable information on big data basics, data warehouse off-loading, Big Data management architecture, and much more.

7. Integration of Informatica Big Data

This three-day training program aims to describe how to optimize data warehouses in a Hadoop environment. Plus, you will learn the processing of different file types using Hadoop that you cannot process using traditional Data Warehouse settings. You will also learn how to describe optimum methods of map designing while executing Hadoop’s Informatica mappings. You’ll learn all this and much more!

Conclusion

Data science has become the new cool, there’s no doubt about that. This profession and field play a vital role in managing the pandemic and tracking it, and we certainly need more of these scientists. So, if you’re interested in pursuing data as a career, expanding your skill set in your current role, or developing your employee’s knowledge to benefit your business, take the ExistBI training.

Apart from these courses and educational values, Exist also provides you with data science services. With these solutions, you can receive trusted, accurate data throughout the information chain. This way, you can make better and faster decisions for your business. Accurate data and information can optimize your business, identify problems and breakdowns, and help you keep everything under control!


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How Big Data Can Help in Fighting Against Coronavirus (COVID-19)

Day by day new cases of Coronavirus (COVID-19) are growing rapidly at astounding rates worldwide; over 55.1M people have been infected with Coronavirus, among them 35.4M people have recovered worldwide. The World Health Organization (WHO) has already declared this as a pandemic. This instant burst of cases requires organizations like WHO to have access to very important sources of knowledge and information. There’s an immediate need to save and store great quantities of data from these cases using different data storage technology. This data is utilized to undertake development and research concerning the management of the virus and the pandemic. In this blog post, we will be discussing how big data can help in the fight against Coronavirus (COVID-19).

But first, let us define Data and Big data in short…

What’s Data?

Data is the quantities, symbols, and characters on which operations are operated by a computer, which can store and transfer it in the form of electrical signals and recorded on optical, magnetic, or mechanical recording media.

What is Big Data?

Big data is an advanced technology that can digitally store a great number of informations. It can help to computationally examine to show patterns, trends, institutions, and gaps. In addition, it can help in showing insights into the spread and management of the Coronavirus. With comprehensive data shooting capability, large data may be properly used medicinally to lessen the probability of spreading this virus.

Big Data is a phrase used to refer to a group of information that’s substantial in quantity and is continuing to rise exponentially with time. In summary, such information is so big and complicated that none of the conventional data management tools have the ability to keep it or process it economically.

Big Data

How Big Data Can Help in Fighting Against Coronavirus (COVID-19)?

Scientists and medical professionals require unprecedented information sharing and collaboration to understand COVID-19 and produce a proper cure to end the pandemic.

Although fever and cough have been considered as the most common symptoms of Coronavirus. Researchers and medical professionals have published a study that loss of taste and smell were the first symptoms to predict that a person could be infected. That insight came from data shared with millions of people who reported through different phone apps, or any other media. Scientists are extracting a huge amount of data to anticipate Coronavirus outbreaks particularly in communities and to research different risk factors for the illness.

Read More: Online Data Science Courses During Covid-19 Crisis

As well as the researchers, there are many other organizations who are working with the massive amount of health data being generated by this pandemic. Since the pandemic spread throughout the world, scientists have begun to aggregate large datasets that could be parsed with artificial intelligence. Though some groups, like those supporting the symptom tracker apps, have benefited from the aid of the people, others are relying upon collaboration from research associations that may otherwise compete with each other.

How Big data analytics will work as a medium for monitoring, controlling, preventing, and research of COVID-19?

Big data will diversify production, improve vaccine development, and enhance the knowledge of the pattern of Coronavirus with complete understanding. Organized data provides better analysis and insights with the variables resulting in better containment of those infected COVID-19 patients. China suppressed the COVID-19 with the support of information collection and implementing it using AI leading to a minimal rate of spread. There are numerous large data elements to this particular outbreak where AI may play a substantial part such as in biomedical research, natural language processing, social networking, and mining the science expedition.

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In certain areas, big data provides advice to identify the suspected cases of the virus. It can help to give an efficient method to protect against the disease and extract additional invaluable details. In the long run, big data will assist the people, physicians, other health care professionals, and researchers to monitor this virus and also analyze the disease mechanism. Data supplied help to analyze how this disease may be slowed or finally averted and help optimize the allocation of assets and consequently leading to taking timely and appropriate decisions. With the guidance of the digital information storing technology, physicians and scientists may also create a convenient and effective system of COVID-19 testing.

How to Secure Patient Data

Since the data includes information such as places and dates essential to monitor the outbreak, scientists and medical professionals required to develop security plans to protect patient privacy. To begin with, data is put in a safe enclave, meaning it cannot be downloaded or eliminated from its server. In reality, it cannot even be seen directly by the majority of the researchers utilizing it. Rather, they need to program software that could analyze the data and give answers.

Read More: How Big Data Can Help in Fighting Against Coronavirus (COVID-19)

Usage of Mobile App for Contact Tracing

In Europe and America, privacy concerns for people are of larger concern than they’re in China, nevertheless, medical research workers and bioethics specialists understand the ability of technologies to encourage contact tracing in a pandemic. Oxford University’s Big Data Institute worked together with government officials to decipher the advantages of a mobile app that could provide invaluable data to get an integrated Coronavirus management plan. Since almost half of Coronavirus, transmissions happen before symptoms occur, efficacy, and speed to alert individuals that might have been vulnerable are overriding during a pandemic like Coronavirus. A mobile app can accelerate the notification process while preserving ethics to slow down the speed of infection.

Tech innovators had worked on alternatives to efficiently track and monitor the spread of Flu. In the USA, the authorities are in conversations with technology giants like Facebook, Google, and others to determine what is potential and moral in terms of using location data from Americans’ smartphones to monitor movements and comprehend routines.

Dashboards from Officials to Track and Outbreak Analytics

Another tool that’s been useful for private citizens, authority policy-makers, and health care professionals to find the development of contagion and also to develop models of how invasive this virus will be are dashboards from organizations like the World Health Organization that offer real-time statistics. These stats show the data around the world in terms of confirmed cases and deaths from Coronavirus and locations. These dashboard data sets can then be utilized to predict red zones for the pandemic so you can make decisions to stay home and help healthcare systems prepare for a surge of cases.

Outbreak Information carries all available information, including the number of verified cases, deaths, and tracing contacts of infected individuals, population densities, maps, traveler stream, and much more, then processes it via machine learning enabling the user to make models of this illness. These models represent the very best predictions concerning summit infection rates and results.

[Case Study] How Big Data Analytics Succeed in Taiwan

As Coronavirus rapidly spread in China, it had been presumed that Taiwan will be greatly hit in part due to its proximity to China. In addition to the flights which moved from Taiwan to China daily, and the number of Taiwanese citizens works in China. But, Taiwan utilized technology plus a strong pandemic plan created following the 2003 SARS epidemic to minimize the virus effect on its territory.

Part of their approach integrated the federal medical insurance policy database with data from its own immigration and customs database. By centralizing the information in this way, when confronted with Coronavirus, they were able to receive real-time alarms regarding who may be infected according to symptoms and travel history. Along with this, they had QR code scan and internet reporting of traveling and health symptoms that aided them to classify travelers’ infection risks and also a toll-free hotline for citizens to report suspicious symptoms. The authorities took quick action when they got the first reported case of Coronavirus, and Taiwan’s rapid response and application of technologies would be the probable reason they have a lower rate of infection than others regardless of their proximity to China.

Bottom Line

Technology is essential in the struggle against Coronavirus and any other potential pandemics. Along with Big data, machine learning, and other advanced technology, data can quickly and efficiently analyze to assist people on the frontlines to find out the ideal management of the pandemic.

If you want to learn more about technologies like big data, machine learning, AI, and other trendy tools contact our experience Big Data team for further information on available training and consulting services.


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How to Build a Successful Data Migration Strategy

Data migration Services involves transferring data from one application to another application, database, or the cloud. Most people opt for data migration to shift data from one place to another or transfer from one email client to another. It has become a common requirement, you, therefore, need to build a data migration strategy that will help you manage data migration.

What is Data and Data Migration?

The process of moving data from one place to another is known as data migration. This process selects the data that has to be migrated moves it to a designated storage system. It is also referred to as system storage migration. In addition to this, data migration services can help in transferring on-premises infrastructure to cloud-based storage/applications.

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Data Migration Strategy

Benefits of data migration

  • Maintains the integrity of data
  • Advanced ROI reduces the costs of media and storage
  • Reduces unnecessary interruption activities
  • Decreases daily manual effort for business operations
  • Increases the productivity of an organization
  • Sustains the growth of the business

Types of Data Migrations

All data migrations are not conducted from the same sources. Generally, the migration is expected to include storage, database, application, cloud, and business process migration.

Storage Migration

IT teams migrate data at the time of a storage technology restoration. The goals of upgrading technology are faster performance and vibrant scaling, along with better data management features.

Database Migration

Moving a database means migrating data between different platforms, such as from on-premise to the cloud, or transferring the data from one database into a new one.

Application Migration

Application migration means migrating data within an application, such as transferring from on-premises Microsoft Office to Office 365 in the cloud. It can also mean substituting one application with another one, like shifting from one accounting software to a new accounting platform from a different provider.

Cloud Migration

Cloud migration is transferring data from on-premises to a cloud or from one cloud platform to another. This type of data migration is not similar to backing up data in the cloud. Data migration is a separate project that migrates data from the source environment to settle a new one.

Difference between Data Migration, Conversion, and Integration

Data Migration- Transferring data between storage devices, locations, or systems. It includes subsets, such as quality assurance, cleansing, validation, and outlining.

Data Conversion- Converts data from a legacy application to a modernized or new application. ETL (Extract, Transform, and Load) process is used.

Data Integration- Combines stored data existing in different systems to generate a unified view and overall analytics.

Risks and Challenges in Data Migration

People often find data migration as a risky and difficult task and it is definitely not an easy process. It is time-consuming, which needs detailed planning and implementation strategy and there is always some risk included in projects of this scale. Let’s take a look at some key challenges.

Data Loss

During a data migration project, there is a risk that you may suffer data loss. When executing on a small scale, this may not cause any problems e.g. IT can repair files with backup.  However, sizable data loss can have a disastrous business impact. In the case of a temporary connection failure, IT may not even identify that the short-term failure unexpectedly terminated the migration process. The missing data could go unobserved until a user or application searches for it, and it’s not found there.

Compatibility Issues

Compatibility issues can also occur during data transfer, such as changed operating systems and unpredicted file formats; or uncertainty about user access rights between the source and target systems. Although the data is not properly vanished, the businesses are not able to find it in the target system.

Poor Implementation Impacts the Business

Many IT teams choose to do a migration process in-house to save funds, or the management team makes this decision for them. But doing it by yourself is hardly ever a good strategy. Migration is an uncertain business with major business inferences and requires widespread expert attention.

A badly run data migration project causes extensive downtime, loses data, misses deadlines, surpasses budgets, and results in balanced performance.

Planning A Successful Data Migration Strategy

Regardless of the intricacies and risks, IT should ensure a successful process within budgets and time limits. The project will require knowledge, strategic planning, management, and software tools.

A well-functioning data migration plan will include the following steps:

Budget for Expert Help

Many IT organizations aim to be practical and some migration budgets do not allow for expert guidance. However, unless IT already has migration specialists within the team, they can save money and time by hiring consultants who have experience and expertise in data migrations.

Plan the Strategy

Be aware of the design requirements for migrated data together with migration schedules and priorities, backup and duplication settings, capacity planning, and prioritizing by data value. It is the step where the IT team needs to decide on the type of migration execution schedule; it can be a big bang or a more gradual tickle migration.

Let’s take a look at these terms:

Big Bang migration involves the complete transfer within a limited time interval. There is always some downtime during data processing and transfer, but the project is finished rapidly.

Trickle migration executes the project in stages, including operating source and target systems simultaneously. It is more complex than Big Bang and takes more time, but has less downtime and more chances of testing.

Work with Your End Users

Consider the data migration process as an important business process instead of just a set of technical steps and engage your end-users. They will have comprehensible concerns over the success of the migration project. Work with them to know the data rules and definitions, what data is the focus in compliance, and priority data that should move first. Also, realize what they are trying to achieve in the process- Is it for Analytics or better performance? A simple way to subject legal holds?

When you spend time working with the end-users, you will understand more about a successful data migration project in less time and at a lesser cost. 

Audit the Data and Fix any Concern 

Firstly, you need to know how much amount of data you are migrating, target storage capacity, and growth opportunities. Database migrations need auditing the source database for idle fields, outdated records, database logic, and making changes before moving data to a new platform.

Storage migration is easier because you don’t need to update the older storage and plot to the new. However, migrating data between two storage systems is not as simple as just copying data from one secondary system to another. You can use software tools to find out dark data and remove or archive them correctly before the migration.  It is important to erase obsolete files, discarded e-mail accounts, and out-of-date user accounts. Figure out and compress the source data if you are migrating data over the WAN, then transfer and test.

Backup the Source Data before Migration

Even if the worse happens, if you lose any data during the migration, you should be prepared to restore it to the original systems before starting again. It will be best practice to create a backup image that you can instantly restore to the original system if you lose data in the migration.

Move and Validate the Data

Invest in an automated data migration tool that enables you to plan staggered migrations of data subsets, validates data integrity in the target system, and sends reports for troubleshooting and confirmation. Protect databases during dynamic migrations with a software tool that connects the source and target databases in real-time.

Final Test and Shutdown

Once you have transferred all data- then you can test migration using a reflection of the production background. When all the checking is done, carefully go-live, and carry out final tests. After the new environment starts running smoothly, you can shut down the legacy system.

What is the need for Database Migration?

In this competitive world, modern needs have given companies some of the evident reasons to adopt new technologies. It includes the speed of doing things, standardization of overall performance, etc. Now when it is clear what database migration is, then you need to know the reasons for performing database migration. Let’s check out these reasons below:

To Save Expenses

Making use of old databases might increase overhead expenses for the company. It is similar to installing other applications or systems to work in a speedy mode. They will transfer its database to a platform that will serve their purpose in a competent way. It will help in savings on infrastructure, personnel, and expertise required for supporting it.

Upgrade to New Technology 

It is a common reason for migration, where the company would move from either an out-of-date system or a legacy one to a system that is intended for the modern data needs. 

In this age of big data, adopting new and proficient storage techniques is a need. For example, a company might select to shift from a legacy SQL database to a data lake or any other agile system.

To Decrease Redundancy 

Data migration is a vital task for the companies in order to transfer all the company data to a single location. It will help in reducing redundant data. Also, the data saved in one place can be easily accessible by all the departments of the company. 

Sometimes, it happens after acquirement when the systems require to be united. It can also occur when various systems are siloed across a company. 

For example, various departments have different databases, and there is no connection between them. It gets really hard to leverage insights from your data when you have different databases that are contrary.

Security Fixes

According to research, it is understood that databases are one of the most susceptible units to cyber attacks. The reason is that they are the easiest to enter into through networks. Most organizations do not upgrade their databases as often as they perform other systems. It ultimately leaves a broad gap for hackers to penetrate and reveal or steal sensitive data. 

Why Should You Hire Experts for Data Migration Services?

The process of moving data from an old application to a new one or a completely different platform is managed by a team of data migration experts. These data migration experts plans, execute and manage to change forms of data for organizations, particularly streams transferring between different systems.

Data migration professionals generally manage the following responsibilities:

  • Connect with clients or management to identify data migration needs
  • Strategize and plan the complete project, comprising migrating the data and converting content as necessary, while evaluating risks and potential impacts
  • Audit available data systems and deployments and find out errors or areas for improvement
  • Cleanse or convert data so that it can be efficiently migrated between systems, apps, or software
  • Manage the direct migration of data that may require slight adjustments
  • Test the new system once the migration process is completed and check the resulting data to discover errors and points of corruption
  • Document the whole thing from the strategies implemented to the correct migration processes put in place, including documenting any fixes or modifications done
  • Build up and recommend data migration best practices for all present and future projects
  • Ensure compliance with regulatory needs and guidelines for all migrated data

If you are considering migrating your data from one system to another, it’s best to get expert support. Otherwise, it may result in a loss of time and data. You will be provided help with setting up your plan, strategy, and overall compliance to conduct a complete data migration.  ExistBI offer Data Migration services in the United States, United Kingdom, and Europe, contact us to find out more.


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Benefits of Predictive Analytics in Healthcare

Nowadays, organizations are facing tremendous pressure to get better healthcare coordination to provide the best patient care outcomes. To achieve these outcomes, healthcare organizations are turning into predictive analytics. In this blog post, we are going to discuss the key benefits of predictive analytics in healthcare…

What to Know about Predictive Analytics in Healthcare

There are some confusion and erroneous perceptions of predictive analytics in healthcare. The area isn’t all about software tools which are generally tied to predictive analytics located in many different businesses.

There is a report from Rock Health about predictive analytics in healthcare: a business that offers seed funds to startups in digital health, it stated that much of conventional medicine and healthcare work inside predictive analytics. The main difference is that many years back, doctors’ minds were predicting the unknown, based on their experience. Now, software tools are broadening the information collection to encompass more information.

Benefits of Predictive Analytics in Healthcare

Predictive analytics in healthcare utilizes historic data to make predictions about the future, personalizing medical care to each person. An individual’s previous medical history, demographic information, and behaviors may be utilized along with healthcare professionals’ experience and expertise to forecast the future. Software tools do not set predictive analytics in healthcare; they represent the most recent wave of technologies to advance the area.

By all stats, the industry is forecast to thrive. According to Allied Market Research, the worldwide predictive analytics in the health care marketplace garnered $2.20 billion in 2018, and it is expected to rise to $8.46 billion by 2025, almost quadrupling in size. The projected compound annual growth rate during that interval is 21.2 percent.

Listed below are the top 7 benefits of using predictive analytics in healthcare:

1. You Can Pick the Ideal hospital and Clinic

Launching a brand new healthcare facility is a costly investment. Predictive analytics can help you evaluate sites by calling prospective visits, measuring the effect of a new center opening on existing centers, and assessing competition by leveraging competitor insights and information so that you invest in the ideal property and avoid costly mistakes.

2. Manage Staffing Levels and Enhance Business Operations

How many staff members if you intend to have in your new hospitals or healthcare facility? By employing the visits prediction generated with a predictive analytics models, you are able to gauge the volume that the facility will probably manage and will maximize your staffing levels so. For existing facilities, you are able to compare the visits prediction to real performance to identify business opportunities to enhance operations. If the center has high-quality operation but low real visits, maybe you’ve got operational issues that will need to be dealt with.

3. Identify that Families are Most Likely to React to Marketing Messages.

Instead of blanketing a trading place with advertising messages to your healthcare center, you are able to identify which families are most likely to reply to your message using a marketing solution that integrates predictive analytics modeling. Taking a targeted strategy to advertise improves return on response rate and advertising spend.

4. Optimize Existing and New Small Business Markets.

Predictive analytics will be the cornerstone of in-depth marketplace research, which identifies your company’s optimal amount of amenities in a current market, the positioning of these centers, as well as the sequence in which you ought to start the facilities. By attaining the proper balance, you are able to optimize a market’s growth potential and deliver the ideal healthcare services into the locations that need them the most.

5. Help Long Run Tactical Planning Initiatives

Healthcare organizations use many different tools from the long term tactical planning process and predictive analytics are a very helpful resource. Arm your staff with a different source of advice to aid with important choices.

6. Plays a Vital Role in Imaging

In medical imaging, predictive analytics is already making waves in accuracy and speed.

Stanford researchers create an algorithm called CheXNeXt, can display chest X-rays in a matter of seconds. It discovers 14 distinct pathologies having an accuracy rivaling that of radiologists. CheXNeXt researchers expect to have the ability to use the algorithm to aid with the identification of urgent care or emergency patients that come with a cough.

Predictive Analytics in Imaging

Lungren, assistant professor of radiology at the Stanford University Medical Center stated that this algorithm prioritized categories for physicians to review, such as normal, abnormal, or emergency. We will need to be considering just how far we could push these AI versions to enhance the lives of individuals anywhere in the world.

Predictive modeling will fundamentally help oncologists make better-informed decisions concerning patient care. Rather than conducting tissue-destructive evaluations or relying upon genomics, AI algorithms can exploit information from pictures to identify patients with a more aggressive disorder that therefore needs more aggressive therapy. It might also let doctors know which patients have significantly less aggressive cancer and may have the ability to prevent the unwanted effects of chemotherapy.

And though the study in predictive analytics for individual care is still growing. It will become a substantial tool for radiologists and oncologists within their functions treating cancer.

7. Perfection in Identification and Preventative Care and Diagnosis

Predictive analytics utilizes the CheXNeXt algorithm to help doctors make more precise diagnoses of the patients to help resolve problems before they appear.

This is done by assessing data collections from tens of thousands of individuals to acquire a larger comprehension of patient travel.

This helps provide a sign of any problems they may have for diagnosis intentions and then allows physicians to better knowledge of how well a patient is being treated.

Using predictive analytics in this way means healthcare providers or hospitals may intervene sooner and ease patient treatment faster, more accurately, and having an increased chance of a much greater result.

Still Wonder Why Predictive Analytics Matter in Healthcare!

Predictive analytics in healthcare is going to be one of the revolutionary things to happen to healthcare providers this century.

Now, take a close look at some of the revealing industry stats for predictive analytics in healthcare:

Society of Actuaries stated, 93% of healthcare companies agree that predictive analytics is crucial to the future of their businesses.

In 2017, the market size of big data analytics in healthcare in North America was estimated at 9.36 billion USD and projected to increase 34.16 billion USD by 2025. The growth rate is almost 17.7%.

82 percent of Respondents at a CWC survey suggested that the top advantage of analytics execution was enhanced patient care.

It is apparent that there will be significant use of predictive analytics in healthcare in the future just they are using in other industries and it’s thriving. For example, the manufacturing industry is one of the best sectors that constantly benefited from using predictive analytics.

The Upcoming Future of Predictive Analytics in Healthcare

Till now, it seems the advantages of utilizing predictive analytics in healthcare is more significant than other concern. Healthcare organizations agree with the companies investing more money in Artificial intelligence, predictive analytics technologies, and machine learning.

Over one-third of healthcare organization’s executives said they had been investing in Artificial intelligence, predictive analytics technologies, and machine learning since 2018.

As the technologies are mature and information sets that may be used by providers to keep growing, predictive analytics will become an extremely significant aspect to take into consideration when it comes to handling patients.

But you may question that this will be in the future, but now what should companies do to look positive? They have the number of data sets required to satisfy their patients. In 2018, Infosys discovered that half of the respondents in an active survey believed their information wasn’t ready.

However, predictive analytics in healthcare is fast-growing among all the industries that use predictive analytics. This is something of an inevitability for larger organizations, even for smaller service providers.

Conclusion

Predictive analytics includes a strong and healthy place in the future of the healthcare industry. But we must remember that the calculations and models behind predictive analytics aren’t perfect and need Points if appropriate. They also require a clear base to be set which seeks to become ethical and nonbiased in its own program.

ExistBI’s Predictive Analytics consulting team helps healthcare organizations to create a predictive analytical ability using a framework that figures out patterns in their historical information while searching for new opportunities to decrease costs and increase profits. For a free assessment or quote, please fill out the contact form or Call: US/Canada: +1 866 965 6332, and UK/Europe: +44 (0)207 554 8568.


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A Brief Guide to Advanced Cognos Analytics

As businesses are producing more and more data than ever before, organizations are investing in tools with business intelligence (BI) features quickly to help them create insights. These insights are generated from that business data to make better business decisions and learn to find out new opportunities by joining advanced cognos analytics training. Last year, a leading market research firm, Research, and Markets forecasted that the global business intelligence and analytics software market would reach $55.48 billion by 2026, symbolizing a CAGR of 10.4 percent that was $22.79 billion in the market accounted in 2017.

Advanced Cognos Analytics

What is IBM Cognos Analytics?

IBM Cognos Analytics is a self-service analytic tool that combines cognitive computing technology, involving artificial intelligence (AI) and machine learning, initially developed as Watson Analytics. For instance, the platform makes use of cognitive tools to get help in automating data preparation. The system discovers the users’ data and can produce recommendations for data connections and visualizations. It’s proposed as an all-in-one platform, presents analytics features, ranging from building dashboards and data integration to exploration, reporting, and data modeling.

Principles of Advanced Cognos Analytics

This business intelligence tool helps in managing and analyzing data easily. Its self-service features help users to prepare, explore, and share data. It includes predictive, descriptive, and exploratory methods, also recognized as numeric intelligence. Cognos Analytics uses a lot of statistical tests to evaluate your data.

It is significant to appreciate the descriptions of these tests as they implement Cognos Analytics. Numeric algorithms are utilized as a part of the workflow to present features to the user that provides information about the numeric assets and relationships in their data.

Business-oriented

Different than traditional statistical software, where the target audience is a qualified data analyst, the algorithms of Cognos Analytics are intended at users who are well-known to it but, not a specialist in data analysis. It means that when tradeoffs are considered in Cognos Analytics, its effectiveness is chosen over complications.

Trustworthy

Cognos Analytics makes use of algorithms that are powerful and are able to deal with a range of assortments of unusual data. This way, the algorithms that are more fragile are able to get better results than strong algorithms. They require you to ensure that they are appropriate and make correct data transformations for the results to be significant.

A slight fall in accuracy is worth the security that is given by an algorithm, which does not provide wrong results when the data is not as it is expected to be.

Principles of Advanced Data Analytics

Intelligent

Nearly all algorithms need tailored decisions to be made regarding them, which needs the combinations of fields to discover data transformations. Cognos Analytics helps to choose suitable values automatically by evaluating the properties of the data. As a user, you may not be able to discover all the decisions that are made manually.

What’s more?

In Cognos Analytics, the numeric algorithms and methods are intended to generate reliable results automatically. To make the most probable prediction, categorization, or analysis, a specialized statistician analyzes the data by making use of IBM SPSS Statistics or IBM SPSS Modeler.

The objective of Cognos Analytics is to present meaningful insights that can help you to know your data and its connections and to make it achievable for a wide variety of data automatically. Cognos Analytics aspires at offering results like a professional statistician without creating hurdles for the business user.

Unlocking Business Intelligence with Cognos Analytics

The days of providing strategic Business Intelligence (BI) solutions have passed. Now, the market is seeking more tactical projects and insight-driven Analytics, and numerous tools in the market place can no longer provide those features.

Based on a recent survey by MIT Sloan, 85% of CIOs consider artificial intelligence (AI) as a strategic prospect. AI-driven Analytics present actionable insights through their self-service capabilities and enable organizations to attain transformation.

IBM Cognos Analytics

So, how does IBM Cognos Analytics differs from other tools? Here are a few features that make this tool more beneficial for your business.

DASHBOARDS – Almost all BI tools can deliver the capabilities to create dashboards. But Cognos Analytics provides smart features to create dashboards on-the-board based on the data presented, removing the need to have reporting writing knowledge, which opens up the tool to a larger audience across the organization.

STORYBOARDS – It is a unique capability of Cognos that allows users to tell the story of data discovery results with this dynamic approach.

REPORT AUTHORING – It helps in professional report authoring, guided authoring practicing, recommended visualizations, on-demand menus, and subscription-based reporting.

EXPLORATION – To achieve the right value from your data assets, Cognos offers advanced pattern detection to help uncover concealed insights. It provides predictive features that emphasize relationship strengths and key factors. And its AI Assistant helps to direct you in the right direction.

DATA MODELLING – It immediately drives data from any source with a simple drag and drop facility.

MAPPING – The top-class mapping and geospatial functionalities built into the latest version helps to examine certain data in a more powerful way. This feature is available for free, unlike other BI retailers.

COLLABORATION –Users can obtain their visualizations and dashboards and convey them to Slack so teams can give feedback straight for a more flawless approach to information sharing.

MULTIPLE DEPLOYMENT OPTIONS – The choice will always be yours! On-premise, SaaS, or cloud-based, whatever you need for your organization, IBM offers all of them. So choose the best solution that meets your IT strategy needs. Want to know more about the tool? Join ExistBI’s IBM Cognos Training with live virtual training or on-site courses in the USA, UK, and Europe.


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How Predictive Analytics Helps Business In Sales Growth

In this blog post, we are going to discuss how predictive analytics helps business in terms of sales growth.

Analyzing a large volume of data is already a crucial part of the decision-making process for any business, irrespective of its volume. Available big data resolve everyday problems like improving the conversion rate or to attaining customer loyalty for an eCommerce business. But do you know that you can also use this data to forecast events before they actually happen? It adds the value of Predictive Analytics Solutions to predict user behavior based on historical data and act consequently to optimize sales.

For online businesses, occasionally executing predictive analytics is equal to improving your understanding of the customer and classifying changes in the market before they occur. The predictive analytics models take out patterns from past and transactional data to recognize risks and opportunities. Self-learning software will automatically evaluate the existing data and provide tools for future problems. It will enable you to build new sales strategies to adjust according to the changes and increase profit growth.

How Predictive Analytics Helps Business

How Predictive Analytics Helps Business Boost Your Sales?

Let’s take an overview of how specifically predictive analytics solutions can help you to boost your sales:

1. Identify Market Trends

Based on data from previous events, predictive analytics will find out the points of highest and least demand that the company might get throughout the year. It allows eCommerce businesses to respond before their competition by planning a good customer acquisition campaign and having sufficient stock in hand to fulfill demand. They can also build an active pricing strategy to optimize sales.

Similarly, considering it for the prices, dynamic pricing depends on predictive analytics to adjust prices to the requirements of the market. Moreover, there are many tools available in the market, which analyze more than numerous different KPIs automatically to set up the best prices for your products and services while always considering historical data and the outcomes of decisions made in the past.

2. Create Personalized Offers

Predictive analytics enables you to predict which offers will be most efficient based on the definite features of each client. With superior segmentation, you can guess the future behavior and attitudes of each user group based on their activities and behavior in the past and offer them only specific products and services in which they are interested. The key to making this possible can be found in the data analytics about what each client purchased, how much they spent, their location, the channel through which they reached to you, and other key performance indicators.

3. Optimize Sales Resources 

With predictive analytics, you can also forecast the behavior of your clients across the whole sales channel. You can easily detect whether there is any risk of them discarding their professional relationship with the eCommerce business or if they are open to making new purchases in the future. Briefly, you can spot the most profitable customers and those customers who should be given more attention from your side.

Optimize Sales Resources

In spite of its countless benefits, CEOs and marketing managers should never forget that, as it is based on historical data, predictive analytics can’t always track the changes in the behavior of customers or competitors. Therefore, you always need to have the correct past and current data in your systems to predict the results correctly.

Use of Predictive Analytics in Different Industries

Other than eCommerce businesses, any industry can empower predictive analytics to optimize their processes, increase revenue, and lessen risks. Below are the industries that use predictive analytics:

  • Banking and Financial Services

In the finance industry, there is a huge amount of data and money at risk, and leveraging predictive analytics for a long time to detect and reduce fraud, calculate credit risk, capitalize on cross-sell/up-sell opportunities, and retain profitable customers.

  • Retail

A recent and popular study showcased those men who purchase diapers often buy beer at the same time. Retailers across the world are using predictive analytics to find out which products they need to stock, the practical benefits of promotional events, and which offers are most suitable for consumers.

  • Oil, Gas, and Utilities

Whether it is forecasting equipment failures and future supply requirements, evaluating safety and reliability risks, or improving overall performance, the complete energy industry is embracing predictive analytics with confidence.

predictive analytics
  • Governments and Public Sector

Governments have always been a source of encouragement in the progression of computer technologies. Today, governments are making use of predictive analytics like many other industries to make their service and performance better, detect and avoid fraud, and better comprehend consumer behavior. They are also utilizing predictive analytics to improve cybersecurity.

  • Manufacturing

It is really important for manufacturers to classify factors leading to decreased quality and production failures, and also to optimize parts, service resources, and allocation. So the manufacturers coordinated with the sales team can forecast the demand of products and manage their manufacturing units accordingly.

In the Nutshell 

Predictive Analytics provides numerous benefits and helps enterprises make more accurate predictions for business outcomes.  Though every business is different, so they need different tools for disparate areas of analytics. In addition to diversity, many companies also presently come across other complexities when it comes to implementing machine learning and predictive analytics across their businesses.

Developing a successful data-driven business strategy requires participation from all levels within the organization, comprising management and staff throughout the departments. All level contributions will help businesses internally assess their existing business conditions, recognizing the major weaknesses and opportunities for growth to find out if predictive analytics can help to resolve those business challenges and impel growth.

Once business recognizes their exact needs for advanced analytics regarding sales and marketing activities, they can begin by evaluating options to apply Predictive Analytics in their businesses.

As a whole, the integration of big data as a distinguishing factor in decision-making becomes a competitive advantage for those businesses that want to boost their sales. Actually, predictive analytics can provide an edge to every organization, no matter what the size your firm has or on which business model it works.

Are you looking for a tool to set your business ahead in the game by growing more sales? Then, make your business shine by implementing advanced predictive analytics solutions today!


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Why is Data Integrity Important for Better Business Insights

In this blog, we are going to discuss why is data integrity important for better business insights?

So, let’s get into the topic!

Modern businesses don’t work on a single application. They empower numerous IT systems to provide the features to enable operational processes and users to be efficient. To make sure that complex IT environments run efficiently, companies are focusing more on how systems are integrated, and the features required to manage data integration services across the environment. While system integration is a multifaceted challenge, the necessary part is to get right is your data integration strategy for your business.

One of the main concerns that people are experiencing these days is that people are not really completely aware of managing data over the network capably. If you cautiously manage a huge amount of data, it may help in evaluating your performance. And ultimately, boost your productivity.

Presently, there is a vast struggle between marketers in the digital market. Thus, businesses need to have a right check and balance to their massive collection of digital data. For the last few years, as the use of cloud technologies has enlarged, data integration has been made more supple and resourceful.

data integration

What Do You Mean By Data Integration?

Data integration is a combination of data flowing in from various sources to single, unified storage space. Integration starts with the ingestion process and involves steps like data cleansing, ETL mapping, and transformation. Data integration eventually enables analytics tools to fabricate efficient, actionable business intelligence.

The main idea behind is making your data more meaningful and actionable and easy to understand for the users who are accessing it. Technology is progressing extensively with time. The data management techniques that were used previously have been trounced by the new emerging technologies, such as cloud storage and other big data technologies.

There is not any universal approach to data integration. However, data integration tools typically engage some common elements, comprising a network of data sources, a master server, and clients who access data from the master server.

In the typical process of data integration, the client makes a request to the master server for data. Then, the master server drives the required data from internal and external sources. After that, the data is extracted from various sources and then combined into a single, unified data set. It is provided back to the business users for further usage.

How Does Data Integration Work?

It’s vital to understand that data integration is a thorough process, not an individual technique. And a variety of data integration tools are available to serve both the assortment of data being collected and the requirements of individual businesses.

Here let’s overview of a basic data integration process:

  • Data is ingested from two or more databases with heterogeneous organizational structures. Even though two or more databases may store rationally structured data, they would not usually be able to correspond with each other.
  • The varied data is stored in a data warehouse. It is processed through a definite schema or set of rules, and classifications developed to resolve various methods in which the information is referenced between databases.
  • The governing schema enables users to propose queries based upon a generally understood system, meaning that numerous data sources can be discovered in performance.

These steps describe data integration in its simplest form. The demands related to data integration technologies are increasing. Unstructured data like information enclosed in-text comments often needs the intervening of the plan to understand semantic links between various units. It adds a level of intricacy to the process and signifies the revolutionary of data management technology.

Why Is Data Integration Important

Why is Data Integrity Important for Business?

Business intelligence applications utilize a complete set of information provided through data integration to obtain important business insights from the past and current data of the company. Data integration can have a direct great impact by providing executives and managers a deep understanding of current processes, as well as the prospects and risks it faces in the market.

Also, the data integration process is sometimes crucial for work together with external organizations like suppliers, business partners, or governmental oversight agencies.

One significant application of data integration in the modern IT environment is in providing access to data stocked up on legacy systems, such as mainframes. For instance, modern big data analytics environments like Hadoop generally are not natively well-suited with mainframe data. A good data integration tool will fill that gap, making valuable legacy data of the organization accessible for the modern business intelligence tools.

How Is Data Integration Accomplished?

An assortment of methods, both manual and automated, has previously been used for data integration. Most data integration tools today utilize some form of the ETL (Extract, Transform, and Load) method.

As the name refers, ETL works by extracting data from its host ecosystem, converting it into some consistent format, and then loading it into a target system for use by applications performing on that system. This step of transformation generally includes a cleansing process that tries to correct errors and insufficiencies in the data before it is loaded into the target system.

Why is Data Integrity Important

Role of Enterprise Data Integration Strategy

An enterprise data integration strategy is a group of policies, processes, plan rules, and governance measures that you decide to ensure data integration is executed consistently, controlled centrally, and is fully sustained by your IT systems.

These strategies are a range of activities that move data from one system to another, supervise the flow of data, implement security rules, and facilitate business processes. When you evaluate how many disparate data sources your company has, you’ll find why you need a holistic enterprise data integration strategy to make sure these important IT components are well managed.

Features of Data Integration

Do you know why people are so much interested in Data Integration? The answer to this question might be the several powerful features that it has. The following are a few main features of data integration.

Create Assurance On Your Data

What people are expecting these days is that their data must be safe and protected. One of the greatest features that come with approximately all the popular data integration tools is the data security that matches all standards of user satisfaction. Consequently, it enables you to build assurance in your data by keeping it secure.

Real-Time Data Governance

Only uniting data at a common platform is not the only thing. What occurs when you arrange the data available but experiencing the same problem in accessing your data in real-time or your data access is lagging somewhere?

The thing that makes your data more effective is the outcome of data integration. With parallel processing technologies leveraged by almost all trendy platforms, real-time data access is made easy to help you access data with the smallest delay and correctness.

Better Customer Experience

When you run an online business, your users are the most precious assets. Hence, the very significant focus that you set on during data integration is boosting the user’s experience. Replace your traditional tools and outdated software with modern and advanced technologies, having first-class features can be used to make the user experience better.

Benefits of Data Integration Services

There are numerous factors that impact the productivity of your business online. One of the main causes that might be stopping your progress is the lack of Data Integration. When you talk about the benefits and significance of data integration, then it will not be wrong if you say that you cannot run any system or business without data.

Consider this whole scenario as a real-life example.  If things keep on messing and spreading all around, it will be harder to access those things and ultimately making it complicated for you to manage those things. Similarly, data is available in massive volume in any online business, and you must provide your users with the facility to reach that data easily without putting much effort. Here, let’s discuss some of the benefits that you leverage with data integration.

Decision-Making

When your data volume in your organization grows with the increase in the number of data resources, the main concern that you may have to confront is the decision- making in managing the data resources. Data integration helps you by combining your data at a centralized platform, which makes it easier to access real-time data and derive better business insights within no time.

Connectivity

If you have been addressing connection issues for a long time, sometimes, it gets really very horrible as you had to wait for weeks to set up the connections. But, with the use of data integration, establishing connections has been made easier. Moreover, diverse tools available in the market also come with multiple automatic connectors used for connecting various cloud storage, which makes it far better to improve the whole performance of the system.

Integrating Multiple Data Resources

One of the important benefits of using data integration tools is that it integrates multiple resources. It doesn’t matter where your data is coming; it will integrate them as a united data resource, which depends upon the type of tool you are using.

Improves Customer Experience

Once all of your data flowing in from your data resources have been incorporated along with all the connections working accurately, it is absolutely going to make customer’s experience better. When the customers quickly find the right information they are searching for, they will get satisfied.

Better Collaboration

When you work in the network, as more data is transferred over the network, the more will be the number of connections required. Data integration helps you to create better collaboration by providing more connections across a common network.

Data Relationships

Raise Competitiveness

Data integration has many other valuable features and capabilities. It also helps you raise competitiveness with other organizational business owners. You can keep track and monitor all your data access, which helps you to analyze the fields in which you have to impart your efforts to compete with your opponents.

Leverage Data Integration for Strategic Benefits

Data is the fuel that adds to innovation and digital transformation endeavors of any enterprise. By leveraging the capability to produce access, collect, analyze, and interpret data from numerous combined data sources, like HR and ERP systems, digital enterprises can comprehend significant competitive and operational benefits.

The unified data can provide insights into business processes, customers, human capital, sales, and finances. These insights can lead to improvements in business processes or recognition of problem areas and facilitate strategic objectives.

In an IDG survey of top-class IT and business decision-makers in organizations with more than 500 employees, 91% of the respondents agreed the capacity to integrate data from any source is critical to creating strategic goals for their organization.

Addressing Data Challenges in Current Crisis!

The current crisis of COVID-19 is bringing the subject of business stability to the forefront of the business leader’s minds. Businesses are trying to survive, adjust, and stay responsive, changing the business processes, positions, systems, and operations to deliver the correct business results. Today, cost management is at the top of the mind of business owners, as companies revolve around the new normal scenario where business conditions change every day. Having an accurate data integration strategy can help you steer through these times of pandemic and come out healthier and more thriving on the other side.

Leveraging an industry-leading data integration tool enables you to connect anything, anytime, and anywhere. By joining hands with professional partners, you can get help in bringing your enterprise data integration strategy to life. It will provide a centralized set of tools for deploying and managing the data integrations across your organization. Do you want to know more about adopting this approach? Visit the leading data integration services provider and consult experts for more details!


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Tableau Consulting – Why Business Intelligence Matters?

Business intelligence (BI) is includes everything from data mining, business analytics, data visualization, data tools and infrastructure, and best practices that help companies to make decisions based on existing data. Practically, you can say you’ve got modern business intelligence when you can have a complete view of your organization’s data and exploit that data to make changes, remove inefficiencies, and rapidly adapt to market or supply variations. With Tableau Consulting, you can understand the importance of business intelligence and how the top BI tool can help you thrive in the modern competitive market.

It’s significant to note that this is a very contemporary definition of BI, and it has had a restrained history like a buzzword. Traditional Business Intelligence originally evolved in the 1960s as a system of sharing data across organizations. It further grew in the 1980s together with computer models for decision-making and transforming data into insights before becoming specific products by the BI teams with IT-dependent service solutions. Modern BI tools prioritize flexible self-service analysis, trusted data-governance platforms, authorized business users, and agility to view insights.

Why Business Intelligence Matters

Examples of Business Intelligence

The Explain Data feature in Tableau helps rapidly identify competent explanations of outliers and trends in data. Business intelligence is much more than a single process – it is an umbrella that covers all the processes and methods, from collecting, storing, and analyzing data from business functions or activities to manage performance. All of these operations work collectively to produce a complete view of a business to help people in making superior, actionable decisions.

In the last few years, business intelligence has developed to comprise more processes and activities to make performance better. These processes comprise:

Data mining: Making use of databases, statistics, and machine learning to discover trends in large datasets

Reporting: Sharing reports of data analysis to stakeholders so they can depict conclusions and build decisions

Performance metrics and benchmarking: Comparing current performance data to historical data to track performance against goals usually using tailored dashboards

Descriptive analytics: Utilizing preliminary data analysis to determine what happened.

Querying: Asking the data related questions to which BI finds out the answers from the available datasets

Statistical analysis: Driving the results from descriptive analytics and extra discovering the data using statistics like how this trend occurred and why

Data visualization: Transforming data analysis into visual illustrations like charts, graphs, and histograms to more effortlessly understand data

Visual analysis: Discovering data through visual storytelling to converse insights on the board and continue to be in the flow of analysis

Data preparation: Collecting multiple data sources, recognizing the dimensions and measurements, making it ready for data analysis

Importance of Business Intelligence?

Business intelligence can help businesses make advanced decisions by presenting current and historical data within their business state of affairs. Analysts can empower BI to deliver performance and competitor benchmarks to make the organization operate more smoothly and efficiently. Analysts can also easily identify more market trends to boost sales or revenue. If the data is used effectively, it can help with everything from compliance to employing staff.

A few means that business intelligence can help organizations to make smarter, data-based decisions:

  • Find out ways to boost profit
  • Evaluate customer behavior
  • Evaluate data with competitors
  • Track performance
  • Manage operations
  • Forecast success
  • Mark market trends
  • Find out issues or problems
importance of business intelligence

How Does Business Intelligence Work?

Businesses and organizations have queries and goals. To find out the answers to these questions and manage performance against these goals, they collect the essential data, analyze it, and conclude which actions should be taken to attain their goals.

Technically, raw data is collected from all activities of the business. Then the data is processed and stored in data warehouses. Once it’s saved, then the users can access the data, starting the analysis process for responding to business questions.

How BI, Data Analytics, and Business Analytics Work Collectively?

Business intelligence involves data analytics and business analytics but makes their use only as a different part of the overall process. BI helps users conclude from data analysis. Data scientists mine into the particulars of data by using advanced statistics and predictive analytics to identify patterns and predict future patterns. Data analytics helps you to get answers for why did this happen and what can occur next? Business intelligence makes use of such models and algorithms and shatters the results down into actionable words.

According to the IT Glossary of Gartner, business analytics involves data mining, statistics, predictive analytics, and applied analytics. Briefly, organizations carry out business analytics as part of their superior business intelligence strategy. BI is intended to answer exact queries and deliver quick analysis for decisions or planning. However, organizations can use analytical operations to improve follow-up questions and iteration.

Business analytics cannot be a linear process because finding answers to one question will probably lead to follow-up questions and iteration. Otherwise, you can consider the process as a series of data access, discovery, exploration, and information sharing. It is known as the cycle of analytics, a modern phrase explaining how businesses leverage analytics to respond to varying questions and expectations.

Business analytics

Difference Between Traditional BI and Modern BI

Previously, business intelligence solutions were designed based on a traditional business intelligence model. It was a top-down approach where business intelligence was determined by the IT organization, and the majority of analytics questions were answered via static reports. It meant that if somebody had a follow-up question about the report they got, their request would depart to the base of the reporting queue, and they needed to start the process once more.

This process led to slow, annoying reporting cycles, and users weren’t able to empower current data to make decisions. Traditional business intelligence is still a general method used for ordinary reporting and answering fixed queries.

On the other hand, modern business intelligence is interactive and accessible. While IT departments are still a vital part of managing access to data, various levels of users can modify dashboards and generate reports on short notice. With suitable software, users are enabled to visualize data and get answers to their own questions.

How Some Major Industries Use Business Intelligence?

A lot of different industries have implemented Business Intelligence more than before, including healthcare, information technology, and education. All companies can use data to change processes.

Financial firms use business intelligence to take a complete view of all current to realize performance metrics and spot areas of opportunity. Access a centralized business intelligence tool that allows you to take all of their branch data together into one view.

Business Intelligence lets branch managers recognize clients that may vary the number of investment needs. And management can track if a performance within the region is above or below average and check out the branches that are responsible for that region’s performance. It leads to more prospects for optimization, together with better customer service for clients.

How to Choose a Business Intelligence Tool?

A lot of self-service business intelligence tools and platforms align the analytics process. It makes it easier for users to view and understand their data without the technical knowledge of mining into the data themselves. There are numerous BI platforms existing for ad hoc reporting, data visualization, and building customized dashboards for several levels of users.

Here are some recommendations for analyzing modern BI platforms so you can select the right one for your company. One of the more general approaches to provide business intelligence is through data visualizations.

Why Business Intelligence matters

Benefits of Visual Analytics and Data Visualization

As you’re aware, data visualization is the most common way to deliver business intelligence. Humans are visual beings and vary in tune with different patterns or dissimilarities in colors. Data visualizations illustrate data in a way that is more handy and comprehensible.

Visualizations accumulated into dashboards can rapidly tell a story and emphasize on trends or patterns that may not be exposed easily when manually evaluating the raw data. This accessibility also allows additional conversations around the data, leading to wider business impact.

Leveraging Benefits of Business Intelligence with Tableau

The processes included in business intelligence help you manage your data so it can be simply accessed and analyzed. Decision-makers can then mine deeply and find the required information rapidly, allowing them to make well-versed decisions. But better decision making is just one advantage of business intelligence. Let’s take an overview of the most practical benefits of BI and how organizations are utilizing this technology to attain their goals.

Quicker Analysis, Intuitive Dashboards

BI tools are intended to do long-lasting processing of data in the cloud or on the physical servers of your company. BI tools draw in data from several sources into a data warehouse and then examine the data based on the user queries, drag-and-drop reports, and dashboards.

The benefit of BI dashboards is to make data analysis easier and actionable, enabling non-technical staff to tell stories with data without any need to learn to code.

Improved Organizational Efficiency

BI provides leaders the facility to access data and obtain a holistic view of their processes, and the capacity to benchmark results against the superior organization. By having a holistic vision of the organization, leaders can find out parts of opportunity.

When companies spend less time on data analysis and collecting reports, BI provides them extra time to use data to modify new programs and products for their business.

Data-Driven Business Decisions

Having correct data and quicker reporting functionality helps in better business decision-making.  Organizations can use customized mobile dashboards for their sales department so they can see real-time data and predict sales before attending any meeting with potential clients. They can confidently talk about the need of clients as well as of prospects and understand that the data is up-to-date. So the business leaders don’t have to wait longer for reports and tackle the risk of data that may be expired.

Better Customer Experience

Business intelligence can directly influence customer experience and satisfaction. With Tableau, companies can deploy BI systems across various departments, building more than thousands of dashboards for employees. These dashboards withdraw data from different processes and text data from customer support interactions. Using such data, companies can discover opportunities to progress customer service and decrease support calls by 43 percent.

Tableau Consulting

Enhanced Employee Satisfaction

Now, IT teams and analysts spend less time answering to business user queries. Departments that didn’t have access to their own data without consulting analysts or IT can now directly perform data analytics with minute training. BI is intended to be scalable, delivering data solutions to the people who need it and for employees who require data.

Trusted and Governed Data

BI systems enhance data management and analysis. In traditional data analytics, data from various departments are siloed, and users have to access multiple databases to answer their reporting queries. Now, modern BI platforms can unite all of these in-house databases with external data sources like customer data, social data, and even historical climate data into a single data warehouse. Departments throughout the organization can access the same data at a single time.

Increased Competitive Advantage

Businesses can stay more competitive when they understand the market and their performance within the marketplace. They can analyze the data to find out the best possible time to enter and exit the market and place themselves tactically. BI lets businesses to sustain with changes in the industry, examine seasonal changes in the market, and predict customer needs.

Adopt Tableau’s Self-Service Business Intelligence (SSBI)!

Today, many organizations are shifting towards a modern business intelligence model, distinguished as a self-service approach to data. IT supervises the data (security, accuracy, and access), enabling users to act together with their data straight away. Modern analytics platforms like Tableau help organizations tackle every step in the cycle of analytics- data preparation in Tableau Prep, analysis and discovery in Tableau Desktop, and data sharing and governance in Tableau Server or Tableau Online. It means that Tableau Consulting can help you govern data access while empowering more people visually to discover their data and share the insights.


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For BI Analytics, Should You Select An Enterprise Data Warehouse or Data Lake Solutions?

Senior Vice President of Gartner, Peter Sondergaard, said that information is the fuel of the 21st century, and analytics is the engine. Companies have always run by data, and increased usage of the internet resulted in more data being generated than ever before, which evolved the term, Big Data. With data being created at a gigantic scale, you will need a place to stock up all this data. So, here need for data warehouses or data lakes solutions.

Companies have long dependent on BI Analytics to help them shift their businesses ahead in the competition by discovering hidden opportunities from data. A few years ago, converting BI into actionable information needed the assistance of data experts. But today, various technologies support Business Intelligence and analytics that can be used easily by employees of all levels within the organization.

Everything that BI data needs to store it somewhere. The data storage option you choose decides how easily you can access, secure, and use data in different ways. That’s why it is important to understand the basic alternatives, how they’re different, and when you should use them.

Data Analytics

Why Data Warehouse and Data Lakes are Important?

Both data warehouses and data lakes are extensively used for storing big data, but they are not similar terms. A data lake is a huge pool of raw data, and a data warehouse is a central repository for structured, clean data that has already been processed for a definite function.

It is common that people often get confused between two types of data storage, but they are much more different than their similarity. In reality, the only likeness between them is their sophisticated intention of data storage. The dissimilarity is important because they provide different functionalities and need diverse sets of skills to be correctly optimized. While a data lake serves good for one company, a data warehouse can be suitable for another.

What is a Data Warehouse?

A data warehouse is a combination of different technologies and components that allows the strategic exploitation of data. It is a practice for gathering and managing data from wide-ranging sources to deliver meaningful business insights. The electronic storage system saves a large volume of data generated by a business, which is intended for query and analysis instead of processing transactions. Data warehouse performs the process of converting data into information.

Modern enterprise data warehouse (EDW) is a database, or assortment of databases, that unifies a business data from numerous sources and applications, and keeps it ready to access for analytics and operation within the organization. Companies can include EDW in an on-premise server or in the cloud.

The data stored in such a digital warehouse is one of the most valuable assets of a business. It showcases much of what is extraordinary about the business, its people, its customers, its stakeholders, and more. 

Data Warehouse or Data Lake Solutions

Advantages of Data Warehouse:

  • Superior ability to analyze relational data that is flowing through online transaction processing (OLTP) systems and business applications (e.g., ERP, CRM, and HRM systems)
  • High-quality integration with consistent data sources, particularly for relational sources, making it robust for small to medium-sized businesses

Disadvantages of Data Warehouse:

  • Data silos, in which information security maintenance directs to restricted access such that important data isn’t reached by the people who may have profited from it, obstructing efficiency and collaboration
  • Higher prospects of distortion of BI analysis outcomes due to impulsive or wrong data cleansing — since data quality is frequently one-sided, with diverse analysts having various tolerances for what comprises quality

What is Data Lake?

A Data Lake is a repository that can store massive volumes, including structured, semi-structured, and unstructured data. It lets you store every sort of data in its native format with no fixed borders on account size or file. Data Lake supports large data quantity to boost analytic performance and native integration.

Data Lake is similar to a big container, just like real lakes and rivers. As lakes have numerous tributaries flowing in, a data lake contains structured data, unstructured data, logs, machine to machine data streaming in real-time.

Daka lake Raw Data

Advantages of Data Lake:

  • Simple integration with the Internet of Things (IoT), as data like IoT device logs and telemetry, can be gathered and analyzed
  • First-class integration with machine learning (ML), with the schema-less structure and ability to amass large volume of data
  • The flexibility provided by the schema-less structure that assists in evaluating data coming from social networks and mobile devices. Also, it carries large, varied, multiregional, and micro-services ecosystems

Disadvantages of Flexibility in Data Lake

The flexibility offered by data lakes can lead to mistreatment, making shortcomings that create more problems than they solve. For example, Data graveyards are data lakes storing data that is collected in large amounts but never used, and Data Swamps are data lakes with low-quality data.

Data Warehouse vs Data Lake

Key Difference in Data Lake and Data Warehouse

Based on some key factors, let’s see how the two data storage terms differ from each other:

1. Storage

In data lakes, all data is stored regardless of the source and its structure in its raw form. It is only processed when it is all set to be used.

A data warehouse will comprise data that is pulled out from transactional systems or data that includes quantitative metrics with their traits. Then the data is cleaned and transformed or further process.

2. Data Capturing 

A data lake captures every type of data in their original format from real source systems, whether it is structured, semi-structured, or unstructured.  

A data warehouse captures structured information and arranges it in various schemas as classified for data warehouse purposes.

3. Data Timeline

Data lakes can store all data, not only the data that is already in use but also data that it can use in the future. Also, data is saved for all instances, to go back in past data and conduct an analysis.

In the process of data warehouse development, considerable time is spent on evaluating different data sources.

4. Users

Data Lake is perfect for users who like to conduct deep analysis. Such users incorporate data scientists with knowledge of advanced analytical tools to exploit functionalities, such as predictive modeling and statistical analysis.

The data warehouse is suitable for operational users since it is well structured, easy to use, and understand for general employees.

5. Storage Costs

Storing data in big data technologies is comparatively low-priced than storing data in a data warehouse.

In a data warehouse, storing data is expensive and time-consuming.

6. Task

Data lakes can include every data and data types, as it allows users to access data before the process of transformation, cleaning, and structuring.

Data warehouses can deliver insights into pre-definite questions for predefined data types.

7. Processing Time

Data lakes leverage users to use data before it has been converted, cleansed, and structured. Thus, it enables users to obtain their results more rapidly comparing to the traditional data warehouse system.

Data warehouses provide insights into pre-definite queries for pre-defined data forms. So, any changes in the data warehouse required more time.

8. Position of Schema

Generally, the schema is determined after storing the data in data lakes. It offers more agility and easiness of data capture but needs efforts at the end of the process.

In a warehouse, the schema is determined before storing the data. It needs efforts at the beginning of the process but presents good performance, integration, and security.

9. Data Processing

Data Lakes works based on the process of ELT (Extract, Load, and Transform).

Data warehouse works on the basis of traditional ETL (Extract, Transform, and Load) process.

10. Complain

Data is stored in its raw form in data lakes and transformed only when it is ready for use.

The major complaint against data warehouses is its lack of ability to make changes in them.

11. Key Benefits

They incorporate various types of data to generate entirely new questions as these users may not possibly use data warehouses because they want functionalities beyond their potential.

In a data warehouse, most of the operational users only are concerned about reports and key performance evaluations.

Data Lake vs. Data Warehouse

Data Lake vs. Data Warehouse In Different Industries

Sometimes, organizations often require both. The need for Data Lake arrives to connect big data and take advantage of the raw, coarse structured and unstructured data for technologies, such as machine learning, but there is always a need to build data warehouses for analytics for the use of business users.

1. Healthcare: Data Lakes Store Unstructured Information

Data warehouses have been used for a lot of years in the healthcare industry, but it has by no means been immensely successful. Due to the unstructured behavior of most of the data in the health industry, such as physician notes, clinical data, etc. and the requirement for real-time insights, data warehouses are typically not a perfect model.

Data lakes enable you for a mixture of structured and unstructured data that can be a better match for healthcare companies.

2. Education: Data Lakes Present Flexible Solutions

In modern years, the worth of big data in education modification has become extremely apparent. Data about student scores, attendance, and more cannot only help to weaken students revert on track but can truly, help to forecast possible issues before they happen. Flexible big data tools have also assisted educational institutions in modernizing billing, progress fundraising, and many more.

Most of this data is huge and exceedingly raw- so most of the time institutions in the education field, leverage the best benefits from the flexibility of data lakes.

3. Finance: Data Warehouses Pleads to the Masses

In the finance industry and other economic business setups, sometimes a data warehouse serves as the most excellent storage model because they get the ability to access structured data by the whole company not only a data scientist.

Big data has assisted the financial and economics industry take large steps, and data warehouses have been a top performer to help companies take that step. The only cause that can influence financial services company away from such a model is because it is more economical, but not as successful for other functions.

4. Transportation: Data Lakes Help To Make Predictions

A great amount of the profit of data lake insight is its capability to make predictions.

In the transportation business, particularly in supply chain management, the prediction ability that approaches from flexible data in a data lake can have enormous benefits, specifically cost-cutting reimbursements identified by analyzing data from forms within the transport channel.

Which Solution Is Right for Your Business?

When you have collected the whole information, you can conclude which BI data storage solution is ideal for your business efforts — whether you should choose a data lake or a data warehouse? After all, both of the solutions provide good data storage facilities for appropriate use cases. The answer to your question may be one or both based on your specific needs, or the businesses can make use of both solutions at the same time.

In general words, the use of data warehouses can be common for small to medium-sized businesses, while data lake practices are more general for superior enterprises. Deciding on one alternative for your business often depends on your data sources. For example:

  • If you utilize an SQL database or ERP, CRM, and HRM systems, data warehouses will fulfill your enterprise environment perfectly.
  • If your data flows in from different data sources, such as NoSQL, IoT logs and telemetry, mobile, social data, and web analytics, data lakes are possibly a good option.

When you run a business, profit, or loss of your business depends on the decisions you made. So when it comes to making the right choice can be essential to ensure that the tool you choose delivers the optimal value to your business. However, the data you confine can only be valuable if you can transform it into actionable insights. Today, top-most software companies like Informatica, Tableau, IBM, etc. offer data analytics tools that let you make decisions easily concerning your upcoming plans and present actions.

Are you still confused about whether to choose an enterprise data warehouse or data lake solutions? Get expert guidance now!


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Big Data and Knowledge Management for Small Business

Many small businesses don’t understand why they should use big data and knowledge management for their business. They think “they are too small for big data”. Actually, this is not true as small businesses need big data and knowledge management to succeed, just as much as bigger corporations. Data gives businesses with actionable insights required to become more profitable and efficient. In this blog post, we will be discussing how Big Data and Knowledge Management for Small businesses can be beneficial.

Let’s start with Big Data…

What is big data?

We all use smartphones, but have you ever wondered how much data it generates in the form of texts, phone calls, emails, photos, videos, searches, and music? Approximately, 40 exabytes of data gets generated every month by a single smartphone user.

Now imagine, this number is multiplied by 5 Billion smartphone users. That’s a lot for our mind to process, isn’t it? In fact, this amount of data is quite a lot for traditional computing systems to handle, and this massive amount of data is what we term as “Big Data”.

Let’s have a look at data generated per minute on the internet…

Big Data and Knowledge Management for Small Business
  • 2.1 Million Snaps are shared on Snapchat.
  • 3.8 Million Search queries are made on Google.
  • 1 Million People log in to Facebook.
  • 4.5 Million Videos are watched on YouTube.
  • 188 Million Emails are sent.

That’s a lot, right!

Uses of Big Data in the Healthcare Industry

There are significant uses of big data in the healthcare industry. Hospitals and clinics across the world generate a massive volume of data annually. Approximately, 2314 exabytes of data are collected annually in the form of patient records and test results. All of this data is generated at a very high speed, which attributes to the velocity of big data.

How can small businesses employ knowledge management?

Management applications in massive organizations. Afterward, we will examine how small companies can revise and embrace those practices for greater knowledge management in their companies.

Simple thought. When running a company, knowledge, and data are resources. Knowledge loss–for example reduction of any advantage –includes a price. Knowledge management is just the practice of taking actions to prevent knowledge loss.

A knowledge management Procedure is normally composed of three components:

1. Gather and preserve significant business knowledge and information.

2. Make gathered information accessible and simple to retrieve.

3. Update gathered information regularly for continuing accuracy.

Knowledge management is significant since knowledge And information are resources.

Imagine you run a business that makes all its gains From earnings on your site. Your site goes down, and also the individual responsible for handling the website is on holiday. Nobody else understands where the website is hosted, plus they do not have key query passwords or answers. Just how much money will the business lose if the site is down for one hour, a day, or weekly?

Knowledge management reduces profit loss within this Scenario since the information required to repair the website is saved where others may find it.

Knowledge management can also be significant for productivity

When a new employee begins and nobody understands the Wi-Fi password, then that worker can not do some work, and yet another worker wastes time by searching for the password or looking for a media cable.

If client support agents have to fix problems from scratch each time they answer a telephone, these calls will require a great deal more time than when they could discover the answers fast in a database.

In the event the workplace supervisor wins the lottery and never returns to perform –but all her documents are saved locally on her computer–somebody from IT might want to spend days or hours trying to obtain access and recover important details.

Room for keeping significant contracts, or as complicated as artificial intelligence technologies that gathers, stores, and retrieves info such as an intern or personal helper. There are lots of different potential approaches.

Let us Look at how knowledge is handled at three

Knowledge management in Toyota

Dr. Philip Fung states that there are two Kinds of understanding to He uses an instance of a chef to exemplify the gap.

Though a chef may Have the Ability to write the recipe down for her Most renowned dish, she’d likely fight to convey how she developed the recipe. There is a difference between what we understand (explicit knowledge) and we do what we know how to perform (tacit knowledge); or as Fung states,”We could do much more than we could tell.”

Toyota’s approach to understanding management caters to the two Explicit and tacit understanding.

Completed by its workers in a work Education (JI) document. The JI contains three pillars of advice:

1. Important Steps — Includes step-by-step directions for finishing The endeavor.

2. specific step.

3.

To talk about tacit knowledge, Toyota workers spend a few months functioning New workers can follow the directions on the JI record, but they’re also able to pull out of the tacit knowledge they obtained while observing seasoned workers performing the jobs.

And when launching a new mill, Toyota not just sends the New mill workers to a present mill for training, in addition, it sends seasoned workers from a present mill to the factory to operate alongside new employees for a couple of months. This makes consistency in processes and knowledge across all Toyota factories across the world.

The way Microsoft utilizes knowledge management

Management plan for almost two years .

Microsoft built its initial knowledge-collaboration stage in It was basically an intranet designed to gather information regarding customer appointments and create the information available to everybody in the corporation. From 2010, the system hosted 37,000 websites.

Finally, the Business realized it had a more contemporary System for distributing and collecting information. This enabled team members to share, access, and make knowledge resources from anywhere, with any device.

Nowadays, Microsoft’s teams utilize an Assortment of the Organization’s Programs to locate and share information:

Employees save files into the cloud–maybe not anyplace on private computers. This simplifies file sharing and prevents information loss brought on by turnover, personal injury, and even theft.

Together with cloud-hosting, workers can nevertheless produce knowledge databases just like they did back in 2006, but they are also able to make websites for outside jobs and make people available to clients and partners beyond the Microsoft network.

All of intranet websites incorporate with Microsoft’s additional services.

Rather than relying on workers to capture and upgrade data, AI will catch and upgrade knowledge automatically by monitoring workers’ digital footprints.

As a little or midsize Company, You Might Not Be able to Develop your personal suite of cloud-based cooperation software such as Microsoft, or an AI-powered call center like Amazon, and you might not have multiple ports for hands-on coaching of workers like Toyota. But that does not mean you can not embrace their knowledge management methods:

Adopt the If new applications are excessively complicated, nobody will use them.

Find alternatives that have built-in integrations together with the applications and software employees are already utilizing.

Understand a fundamental source of advice is greatest. If knowledge is dispersed across multiple applications, it will continue to be difficult for folks to find.

Require Ideal solutions can automate the procedures of upgrading knowledge or mechanically categorize and label fresh content to make it a lot easier to locate.

Search for resources using machine learning how to improve as information is accumulated. Machine-learning technologies learn how people look for particular forms of information, becoming better over time in helping users locate the specific information they’re searching for.

Document Important procedures:

Utilize Toyota’s JI record as a template, or produce your standard process record. Put aside time once a month to allow workers to make instruction about the jobs they are accountable for.

Save documentation about the cloud or any other shared server so everybody has access to it and also to stop file reduction.

Locate Creative ways for workers to share tacit understanding:

set a mentor program that matches new hires with long-time workers.

Ensure supervisors understand how to execute the most crucial tasks that their groups are accountable for. This may enlarge institutionally knowledge, give a supply of backup when workers take off time, and lower the odds of overall knowledge reduction brought on by abrupt turnover.

Individuals most frequently associate terms such as”large data” and However, the truth is that the technologies to access, store, query, and use knowledge and data aren’t merely readily available to small and midsize companies, but it is less expensive than ever.

In case your firm’s most significant employee won the lottery Tonight and never return to perform, would anybody be able to pickup where He/she left? If the Solution isn’t –or if you are not sure–it is time to Seriously think about the role that knowledge management can play on your business.


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New Predictive Analytics Solutions for Manufacturing Industry

The significance of data and analytics in modern companies has continued to rise. In fact, IDC anticipated that expenses on AI-powered tools like predictive analytics solutions grow from $40.1 billion in 2019 to $95.5 billion by 2022. In this blog, we are going to discuss the use of predictive analytics in manufacturing…

The objective of using predictive analytics is to boost efficiency to understand and analyze complex systems and processes and foresee what will happen next. Technologies like Artificial Intelligence (AI) and machine learning can quickly evaluate a tremendously large volume of data, enabling teams to identify insights at a faster pace. It can benefit an assortment of areas in manufacturing, such as production optimization, quality, maintenance, and waste reduction.

Worldwide market competition, quick innovation and logistics, market instability, and varying regulations need manufacturers to forecast upcoming challenges, conditions, and demands in advance. Predictive analytics gives your manufacturing operations the capacity to derive valuable insight from the compound and varied data you’ve already collected, allowing you to see well beyond the perspective into future opportunities.

Predictive Analytics Solutions for Manufacturing Industry

In this rapidly growing market, manufacturing downtime and the introduction of some inferior products can rapidly damage your reputation and outcomes. Therefore, the manufacturing industries require tools that keep manufacturing processes, infrastructure, and equipment running competently to maximize performance and reduce costs and ad hoc downtime that can disturb production, service, and delivery.

Here you’ll understand what predictive analytics is and why predictive analytics is vital to successful manufacturing.

What is Predictive Analytics?

Predictive analytics exploit the power of historical data with AI and machine learning technology to identify, monitor, manage, and optimize business processes. It also spots and identifies trends, forecast potential concerns, and provides suggestions to improve the process and make performance better. Industrial IoT platforms that empower predictive analytics gather and analyze real-time data to foresee and avoid forthcoming problems at the initial point.

When people talk about manufacturing, the first step to leverage predictive analytics is collecting, storing, and organizing the processed data produced by a variety of machines, devices, and systems within the factory. Generally, factories need almost three to six months of data to use predictive analytics efficiently. Although, this time interval can change depending on the volume of data generated and the targeted issues.

The analytic applications like predictive performance and predictive quality generate data rapidly because production runs on regularly. Sometimes equipment failures can take place, so it can take even months to produce the quantity of data required for specific applications. 

Once accumulated together, the historical data can be used to withdraw insights and make efficient predictions based-on a broad range of variables like line speed, product quality, and more. It includes identifying key relationships between various variables, forecasting variables of interest, and leveraging decision-makers to take early action to lessen waste and boost efficiency.

Today, factories have become ever-more connected, so the predictive analytics technology will turn out as a key part of their digital transformation journey because it can help you become more efficient and competitive and gain more profit.

Predictive Analytics

Why Should Manufacturers Use Predictive Analytics?

It’s obvious that there will be rapid growth in the adoption of predictive technologies in the future. In the manufacturing industry, modern and advanced factories are leveraging predictive analytics to reduce the time to action considerably which saves time, money, material, and speeds up the time of marketing. 

Manufacturers get alerts in advances, such as possible quality failures or unexpected downtime due to machine failure, and enable operators to take corrective action. For example, machine learning can foretell a quality failure that can occur in ten minutes because of dropping line speed and its past consequences, where products do not match quality standards. 

Factories are also using these technologies to identify production trends, resolve issues faster, and handle resources more competently. The capability to recognize potential issues early on with predictive analytics facilitates factories to manage their processes and avoid the costs included material waste, high scrap rates, or downtime.

In the situation of an upcoming skilled labor deficiency, machine learning and predictive analytics technology also have the added benefit of helping manufacturers to attract digital-native staff to engage in their workplace. At a time when many factories find it difficult to employ and preserve talent, the opportunity to work with this cut-throat solution provides a value-added benefit.

How Predictive Analytics Works?

When deploying a predictive analytics solution, firstly collect data from machines and sensors and integrate this data with live operational data, data from MES and ERP systems, and offline quality data. After that, cleaning, merging, formatting, and structuring in the cloud takes place. For example, if one machine notes down the temperature in Fahrenheit and another machine take the temperature in Celsius, then the temperature needs to be converted into a combined metric. 

Based on historical data, machine learning algorithms can find out the behavioral patterns that have earlier lead to problems. If the real-time event starts to pursue one of those problem patterns, then the system can predict the potential result and alert factory managers. Once the operator, engineers, or managers gets alerted, they can rapidly take remedial action and avoid issues from having an important impact. 

Here are the four most important steps that are key components of AI predictive analytics...

Step 1: Access and Explore Existing Data

Step 2: Pre-Process Data With Precision

Step 3: Create and Validate Predictive Models in the Cloud

Step 4: Set up Models and Implement Insights from Predictive Analytics

Predictive Analytics

What Are The Benefits of Predictive Analytics?

As the companies are shifting towards digitalization, manufacturers are under pressure to hold a competitive edge; so many of them often query why they should choose predictive analytics?

Predictive analytics is vital for applications that allow manufacturers to classify problems at their very starting stages, so they resolve them before issues begin to unfold. 

As the return on investment is a key driver of the industry, predictive analytics is competent to deliver insights faster and many factories even estimate measurable cost savings and opportunities for optimization after a few months only. 

Detect Patterns to Calculate Performance 

Predictive analytics go through a large volume of historical data much faster and more accurately than a human. Machine learning technologies are able to spot repeated patterns and further relationship variables. So when you modify these settings, you boost production by 10% without giving up the first-pass yield. 

AI and machine learning can reach to patterns and discover a variety of combinations that help your organization recognize potential efficiency enhancements, forecast issues, and decrease waste. 

Improve Operations in Real-Time

Predictive analytics provides agile real-time insights by evaluating data from past production runs with live production movement. These assessments that convert to predictive and prescriptive analytics both constrain suggestions and alerts to make operations better in real-time. A cloud-native hybrid system joins the power of the cloud with the business stability, allowing factory managers to improve their decision-making process faster.

Trim Down Costs

Quality failures can result in major losses in the product that increases the additional cost of labor and time. Predictive analytics will help factories to find out quality failures and take remedial action quickly to reduce impact and trim down the cost related to waste. Prescriptive analytics can also increase these cost savings by allowing you to repeat your most competent processes more consistently. In addition to this, predictive analytics and situation-based monitoring can help factories decrease unexpected downtime and lost productivity by informing manufactures about probable equipment issues.

Optimize to Precision

Almost all manufacturers are familiar with some Lean Principles that they are following for decades. Sticking to these best practices helps manufacturers attain the utmost production efficiency with the least waste. Predictive Analytics ultimately presents manufacturers with real-world data to help them optimize their operations to reach the precision.

Who In The Manufacturing Industry Can Implement Predictive Analytics?

Predictive analytics can be implemented in the manufacturing industry of approximately every size and any other industry. Some applications might be more appropriate to definite industries than others however since predictive analytics rely on the existing data and models can be used to forecast everything.

Let’s take a look at a few important roles within the factory:

Plant managers – They can benefit from predictive analytics to optimize production and augment contribution boundaries.

Engineers – Predictive Analytics can help engineers to solve problems faster. They can evaluate data faster than ever and utilize analytics-driven procedures and quality recommendations to revise guidelines and processes as well as clear up and root cause problems.

Operators – They obtain alerts about potential failures, so they can take curative action quicker and avoid any downtime related to quality or device failures.

To be more efficient, all you require is the right approach to collect data, like sensors, a place to gather that data and data-skilled staff to recognize what those insights mean.

Predictive analytics in manufacturing

How to Start Using Predictive Analytics?

Meaningful ROI depends on creating the right foundation. To make a predictive analytics solution to be successful in the manufacturing unit, you’ll need the following foundational elements:

A True Source of Data

The data existing within your organization is often complex and disorganized. The different data formats withdrew from ERPs, MES platforms, QMS software, and other basic sources only make it more difficult. If you want to drive real value from your inclusive data, predictive analytics can help you build a single source of certainty. Whether it is operations, quality assurance, or supply chain management, it provides the manufacturing industry a holistic approach to take a dive into your complete data.

Correct and Reliable Data

The correctness and reliability of data impact the capability of any organization to make valuable forecasts. In the manufacturing field, the variety of different data types from an assortment of sources makes data quality management the main concern and that there are apparent relationships throughout your master data. If not, you’ll be incapable of classifying differences or duplicates in your data that can overturn your predictions about all from future demands to employee needs. We can help you to expand dependable quality across your data system to make sure your insights are correct.

A Definite Data Strategy

For predictive analytics and for reporting to present the maximum value, your organization needs a solid data strategy intended around your maximum priorities. Predictive analytics can help to overcome the difference between technology and your business goals, attaining them with the straight route.

predictive analytics Reporting

Centralized Data

With the extent of data available to you, you’ll probably require a centralized data lake for diverse business units to access your collection of data. You are required to consolidate all of the diverse source systems, such as ERPs, MES platforms, etc. into a single reliable source, an achievement you can’t attain without data ingestion.

Accessible Data

When complete data is centralized and validated, your internal BAs and data scientists really require data access. Through custom growth or a cutting-edge solution, you can help to generate dashboards and portals that facilitate your team to inquire questions that authorize them to expect demand, run resources, spot potential risks, and increase your ROI.

Conclusion

There are multiple predictive analytics tools available in the market developed to make Industrial IoT and data analytics more available across the factory ground. Including platforms that enable manufacturers to influence data visualization tools, machine learning, and more. These tools help plant managers, engineers, operators, and quality control managers to discover the most resourceful way to make a product within a robust, secure hybrid cloud-edge environment.

With predictive analytics ability, you’ll be given predictive alerts that permit you to take action quickly to avoid quality and other performance breakdowns. You’ll also make use of interactive dashboards and data discovery that give a picture of real-time performance as well as enable you to examine basic cause analysis to work more efficiently.

If you are interested in making your manufacturing unit more advanced by leveraging the latest technologies like Artificial Intelligence and Machine Learning, implement modern Predictive Analytics Solutions to make it work more efficiently. ExistBI offers consulting services in the United States, United Kingdom, and Europe.


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How BI Analytics Services Supports Your Business?

Today, companies are embracing BI Analytics Services to make their IT solutions more vigorous, easy to access, and efficient. With Cloud-based BI solutions, organizations, irrespective of their size, can raise their standards of competency and worth. In 2018, the global BI software market was valued at $14.3 billion and predicted to rise at 19.1% CAGR to $28.77 billion by 2022.

Business intelligence enables small, medium and large organizations to improve their decision-making by accessing big data. Even small companies that don’t generate and manage a large amount of data can gain substantial benefits from enhanced analytics.

At first, only large businesses could be able to afford the cost of BI analytics due to the software cost and the infrastructure required to process it. However, the latest technological innovations, such as Software as a Service (SaaS) that work on a cloud computing platform, have raised the standards. Today, even startup firms with sales below $100,000 a year can exploit and take benefit from BI.

How BI Analytics services Support your business

Implementing business intelligence and analytics efficiently is a critical point of difference between companies that thrive and companies that sink in the modern environment. That’s because things are continually changing and getting more competitive in every segment of the business, and leveraging the power of BI is key to outshine your competitors.

For example, for marketing, traditional advertising methods of spending huge amounts of money on TV, radio, and print ads without considering ROI are not as effective as they used to be. Consumers have become smart and more resistant to advertisements that aren’t targeted directly at them.

The successful marketing companies in both B2C and B2B use data and research to formulate hyper-specific campaigns that reach out to targeted customers with a customized message. Companies test everything and then they put more money in successful campaigns while the other campaigns remain idle.

Why Is Business Intelligence Analytics So Important?

The main functionality of business intelligence and analytics is to help business teams, managers, top executives, and other employees make better-informed decisions based on accurate data. It will eventually help them identify new business opportunities, trim down costs, and recognize ineffective processes that need to be engineered again.

BI analytics uses software and algorithms to derive valuable insights from a company’s data and direct their strategic decisions. BI users evaluate and represent data in the form of business intelligence dashboards and reports, visualizing compound information in a simpler, more amicable, and logical way. Finally, business intelligence and analytics are much more than the technology used to collect and analyze data.

Top Benefits of BI Analytics

The benefits of business intelligence and analytics are abundant and diverse, but they have one thing in common, they give you the power of knowledge. Whoever they influence, they can convert your organization and the way you handle your business profoundly. Here is an overview of the top six benefits of business intelligence:

  • Understand your customers more efficiently
  • Drive performance and revenue
  • You can rate leads
  • Spot sales trends
  • Easily present tailored service experience
  • Enhance operational effectiveness

How Does Business Intelligence Work?

Business intelligence presents a scale of wide range of analytical applications, comprising collaborative BI, mobile BI, open-source BI, SaaS BI, real-time BI, and operational BI. The technology is not only about collecting intelligence but about forming a sense of data in a way that can be rapidly seized.

It is possible through visualization applications for making infographics and design charts. BI also provides dashboards and also present performance scorecards. In essence, you can understand the key performance indicators and business metrics in a much easier way when the data is displayed in the form of visualizations.

BI Analytics services

How BI Analytics Services Supports Your Business?

Many small businesses are reluctant to implement BI into their practices. It is not just because it is costly and time-consuming to install but, because they are not sure about the profits they can gain by using it. Here are a few reasons why it can reimburse its value:

  • It’s much easier to make well-versed data-driven decisions.
  • It’s a structured way of increasing revenue.
  • It augments the competitive benefit over other leaders in the industry, including bigger organizations.
  • It enhances the competence of its business operations.
  • It improves the quality of their customer service.

These benefits are the key factors that decide the success and prosperity of any business. Making efforts to analyze data without Business Intelligence and Analytics is clumsy. For example, information is often fed into Microsoft Excel spreadsheets, which is time-consuming in aspects of data collection and it’s tedious to put the information together in a way that’s easy to grab, analyze and share.

If you fail to analyze data, it can result in the difference between profit and loss or between a humble profit and offensive success. These are the two major things that can occur when analytics are done properly:

  1. You can discover insights into industry trends and can identify marketing opportunities that you could have otherwise missed.
  2. You get to know what customers want and demand from your company and this information can assist you in redesigning your business to obtain more customers.

Data Literacy in Today’s Digital Age

It’s hard to find a business that isn’t driven by correct data. In fact, data is growing at lightning speed. Regrettably, smart business executives don’t always have sufficiently skilled workers to make sense of the constantly rising data- nor they have the right tools they need to collect this data competently and extract it for insights.

Efficient data-driven operations that run across an organization can present a differentiating factor. It’s not easy to understand risk rapidly because the existing data is often incorrect. As its effect, a company can fail to choose smarter options and improve its bottom line. It’s hypothetically possible that a company can have high data literacy without Business Intelligence that is much harder.

How BI Analytics Services Supports Your Business

Leading to ROI with Business Intelligence Analytics 

Business intelligence is a key to manage business trends, spot significant events, and view the full image of what’s happening within your organization due to data. It is vital to optimizing carious operations, boost operational efficiency, gain new revenue, and make the decision-making process better for the company.

You’re living in the most competitive business market in history. Progression in technologies and a worldwide economy have mutually created a force of competition in the market, with weaker companies being buried in the crowd.

Considering the current situation, an organization can’t thrive without using BI tools. Particularly after examining some case studies, which have shown the unbelievable ROI that is only possible by using them and the endless benefits of business analytics. This ROI gained from business intelligence can come in various forms.

You have to understand what’s going on in the minds of your customers who can be your next best customers and how to collaborate with them in the most efficient ways. You can get answers for all these questions from the available data, which can be processed by implementing BI and analytics tools. However, you need to be aware of any indiscretion and don’t forget to consider some business intelligence best practices and some detrimental practices to stay away from!

How Can You Successfully Implement Business Intelligence?

A highly modified, customer-driven approach has ended up in a modern business approach, which needs a business analysis with definite metrics. Hence, a business intelligence strategy is essential for all organizations today.

If you implement Business intelligence properly, it can provide you a correct analysis that can help you to speed up and develop your business. It can help you to evaluate the customer acquisition cost, customer purchasing patterns, cycles, and help you to make informed decisions based on that analysis.

An appropriate business intelligence execution will not only help you know your customers better but, it will also help you to increase your sales multiple times.

So, what are the steps you should pursue a successful business implementation strategy? Here are a few key steps for deploying business intelligence within your organization.

Training the Staff & Stakeholders

It is human nature to oppose change and the first step to reducing the resistance is through training. You can teach and educate the staff & stakeholders, which requires immense efforts as it would need an exceptional amount of expenses from the stakeholders’ viewpoint and shift to new technology from the staff’s point of view.

Identify the Objectives

The second thing to do for a successful BI Analytics is to clearly identify the objectives you want to achieve through a business intelligence system. Having set up objectives will not only help your partners to recognize the expectations from the tool, but it will also assist you in strategizing the plan of action simply.

Set Up Key Performance Indicators

When you have defined the goals to set up a business intelligence system, the next step is to describe the key performance indicators (KPIs) clearly. They will help you to create helpful decisions to attain your objectives. These indicators should be assessable in line with your objectives and the key to accomplishing your goals.

BI Key Performance Indicators

Create a Team

Next, you have to create a team of people who will carry out tasks such as data cleansing, data input, data processing, and data analytics. It is one of the most crucial steps for a successful implementation of BI analytics, as this team will be the one to execute the ideas.

Discover the Best Software

The next step in the process of implementation is to discover the most suitable software that can perform all tasks within your organization. You also have to find out various options for software available for every task. The variety of tools will change depending on the requirement and budget. But, you need to understand the optimal tool required for all processes.

Develop an Execution Strategy

Once you have gathered your team, resources, and software, you need to concentrate on the implementation strategy for the successful implementation process of BI and analytics. It involves understanding whether you require a Top-Down Approach that is more of a strategic method or a Bottom-Up Approach, which is more of a tactical method.

Identify the Tasks & Allot the Resources

After creating a team, selecting software, and the suitable strategy of execution, you need to describe the tasks which the teams will perform. And then, you need to hand over the tasks to the related teams and assign the resources to complete the task.

Data Analysis Processes

Build the Data Cleansing, Data Processing, and Data Analysis Processes

Now when you have all the tools, strategies, and the team in your place, you have to generate a data cleansing process with the selected tool. There is a vast amount of data that is deficient in the quality to obtain your goals and you need to clean-up this useless database and produce a high-quality database.

You also have to make sure that there are checkpoints to estimate the data quality at set intervals. Having an efficient data cleansing process improves your chances of attaining your goals. Then, you have to integrate the BI analytics tools, such as Microsoft Power BI, Cognos, or Tableau to be aware of the user behavior insights.

Execute the Process as a Proof of Concept 

After completing all this, you have to execute them for a single process as a proof of concept. Once you have enough data to recognize the impact of BI on your business, then this approach will help you evaluate whether you are meeting the KPIs or where areas need to improve.

Implement the Changes to Meet the KPIs

When you have executed the changes based on the insights derived from the Proof of Concept, you can run another PoC to recognize how much difference you have covered between the outcomes of these two PoC.  It must be a regular process, and it requires optimization at every stage. It is recommended to try some proof of concepts and analyze their results.

If you want to stay away from all the hassles you experience in implementing BI by yourself and analytics tools – you can hire professional BI Analytics Services that will do it all for you! ExistBI has offices in the United States, United Kingdom, and Europe.


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Data Management Services are Increasing Value and Importance of Business Data

In this blog post, we will discuss the importance of business data and data management services…

In today’s digital era, data is the real king, counted as one of the most important assets of an organization, impacting business decisions. It means, if the data is correct, complete, organized, and reliable, it will influence the development of the organization. And if it is not, it can become a big liability, leading to harmful decisions because of deficient information.  Therefore, companies need effective Data Management Services that can help them to organize, classify, cleanse, and manage data efficiently.

The quantity of data related to an organization nowadays is on an unparalleled scale, holding multiple challenges regarding data management, this is why it is vital to invest in an efficient data management system. Efficient data management is a vital piece of implementing the IT systems that process business applications and present analytical data to help them direct operational decision-making and strategic planning by business executives, managers, and other end-users.

Data Management Services

The data management process involves a combination of different features that together aspire to ensure that the data in company systems is correct, available, and easy-to-access. Most of the essential work is completed by IT and data management teams however, business users normally also contribute to some components of the process. High-quality data will ensure that the data fulfills company needs and will lead to policy and operational strategy.

What is Data Management?

Data management concerns the complete journey of your data; right from collecting, storing, classifying, protecting, verifying, and processing necessary data and making it accessible to your employees in the organization.

Today, data is seen as a business asset that you can use to make more-informed business decisions, make marketing campaigns better, improve business operations and decrease costs, all with the motive of growing revenue and profits. But a lack of correct data management can burden organizations with ill-assorted data silos, conflicting data sets, and data quality issues that restrict their capability to run business intelligence (BI) and analytics applications or, even worse, it lead to faulty insights.

Importance of Business Data

A well-implemented data management strategy can support companies to gain potential competitive benefits over their business contenders, both by making operational effectiveness better and facilitating improved decision-making. Organizations having data that is well-managed can also turn out to be more agile, identifying market trends easily and moving forward to gain new business opportunities more rapidly.

A valuable data management system can also help companies to escape from data breaches, data privacy concerns, and regulatory compliance issues that could harm their reputation, add unpredicted costs, and put them in legal threats. Eventually, the major advantage of a concrete approach to data management can deliver is enhanced business performance.

Importance of Business Data

Here are a few reasons to have an effective data management system:

Boosts Productivity

If you get data to be used easily, particularly in big companies, your organization will be more prepared and productive. It lessens the time that people waste searching for information and in addition, makes sure that they can increase staff capabilities. Your staff will also be able to comprehend and converse information to others. Additionally, it makes it easy to view past communication and avoid miscommunication due to messages lost in the sales journey.

Smooth Processes

A smooth operating system is a dream for every business and data management makes it a reality. It is one of the influential factors in business success. If someone takes time to respond to their customers or the varying trends around them, they often have improved customer retention and generate new customer interest. A superior data management system will ensure that you respond to the world accordingly and remain ahead in the competition.

Lessen Security Risk

Today, a lot of personal information is available for people to access. When you save anyone’s credit card information, personal address, phone numbers, photos, etc., it is of the utmost importance that this data is confined by the most favorable security. If your data is not managed correctly, it can be accessed by the wrong people. Stolen data will also have strict allegations on the growth of your company; no one wants to give their details to people who cannot keep it protected.

Cost-Effective

 If you have a good data management system at your end, you have to spend less money on fixing issues that shouldn’t have occurred it the first place. It will enable your organization to avoid redundant duplication. By storing and making all data easily accessible within the organization, it makes sure that your employees never conduct the same research, analysis, or task that has already been finished by another employee.

Minimize the Possibility of Lost Data

An effective data management system will minimize the chances of losing significant company information. It also makes sure that your data is backed up and in the case of unexpected errors or system breakdown, any lost data can be recovered easily.

Improved Decision-Making

When all your data is organized and all departments know how to access it, then the quality of your decision-making progress should improve considerably. People have diverse techniques of processing information however, a centralized system makes sure there is a framework to plan, arrange, and allot data. In addition to this, an excellent system will ensure fine feedback, which sequentially will lead to essential updates to the process that will only profit your company in the long term.

To Wrap Up

The future of managing businesses lies in an organization’s capability to use data irrespective of its source, type, or size. When data is managed in the right way, you gain accurate insights through business intelligence and data visualizations. You can choose to get assistance from professional data management companies.

There are many advantages to hiring external assistance with your data management. Firstly, a proficient firm specialized in data management will be more of an expert than your inside staff. They can ensure proficient data security implementation within your organization. Moreover, it is likely to decrease the cost of having an internal staff member do it, as a data expert will require less time and resources to complete the task due to their experience.

If you are looking for specialized Data Management Services, ExistBI has consultants in the United States, United Kingdom, and Europe, contact us today for more information.


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Data Warehouse Consulting Driving You Towards New-Age Solutions

Business demands for information are never-ending, it is determined by performance management, competition stress, industry policies, and the exchange of data with customers, stakeholders, and suppliers. Similarly, data integration becomes inevitable for companies that deal with multiple sources, generating massive amounts of data, and requiring real-time results. Here, the need for a data warehouse arises and companies need to get the right guidance under Data Warehouse Consulting experts to create effective storage solutions for significant volumes of data.

With time, data integration features have expanded through software development and infrastructure enhancements. In software, extract, transform and load (ETL) has evolved as the data integration workhouse having Enterprise Information Integration (EII), Enterprise Application Integration (EAI), and Service-Oriented Architecture (SOA) incorporated into influential data integration suites. With the infrastructure development in multiprocessor central processing units (CPUs), disk input/output (I/O), storage arrays, network bandwidth, and database, it has increased the volume of data to a great extent for businesses processing. But the point of concern is that despite these advancements, companies cannot sustain these business information demands, and some cannot afford it.

businesses need data warehouse

There are two basic traps companies can easily fall into that limits data integration efforts despite how much they have to spend. The following is the main leading concern, known as Silver Bullet.

The Silver Bullet

In the starting period of data warehousing, ETL tools were simply for code generation. Their elevated cost and small functionalities restricted their use. IT firstly custom coded all data integration applications. The best data integration coders had special knowledge of database growth, amendment, and optimization. Databases were never close to the self-tuning and optimization that people take lightly nowadays.

Now, ETL and database optimization are highly developed. Most of the people utilizing data integration today do not encompass the same consideration of data integration and databases and with today’s complex tools, they are not required to. So, when the business requires more information, IT searches for a silver bullet; buy more multifaceted data integration software and infrastructure.

Traditional Methods Are Not Good Any More!

There are two essential principles for designing data architecture and making the most of data throughput:

1. Process the least amount of data that is necessary to keep data updated.

 2. Load the data as fast as possible into the database used for data integration.

In spite of all the enhancements that have been made during the last two decades in data integration technology, infrastructure, and databases, these two principles are applied. However, some people have overlooked the system or maybe, they never understood them in the first place. They depend on their data integration tools and databases for quick data loading, and when they get into trouble and then they procure faster CPUs, extended memory, and speedy disks. But all they actually have to do is pursue these two principles, with far less costly results.

data integration tools and databases

People try to make up for skipping the basics by making larger software and hardware investments, but they cannot match the quantities of plenty of business information.

The most effective method to speed up data throughput is to combine only the lowest amount of data required to update your data warehouse or operational data store (ODS). It is the best approach to execute this through the Change Data Capture (CDC) method, but most of the data warehouses and ODSs are built using absolute data reloads. Several of these processes are surpluses that data warehouses and ODSs created years ago. These data warehouses are now legacy applications left by their complete reloads and IT has been hesitant to rephrase these data warehouses using CDC.

Many companies aren’t just building their legacy data warehouses from the same initial point every time; they also have data marts and cubes they recreate every time they use them. It’s time to think about breaking the cycle and enhancing your data warehouse and business intelligence (BI) load cycle.

In bulk loading, the arcane and unglamorous database loading methods and other old approaches to rupture the data integration still stand to help stay away from purchasing new software and infrastructure. The rule is to extract the data out of your source systems and drive it into your data warehouse environment as soon as possible. Normally, this method is a fast and low-priced method to considerably improve data warehouse loading.

Bulk loading is only applied to your major concerns that are usually fact tables, which are generally about 10% of the tables or files that you are loading. It is interesting to note that even the high-end data integration tools have made space to bulk loaders, supporting the fact that it is indeed a feasible and priceless tool. There are further approaches, methods, or techniques that can also be implemented from the older days, where the laws of database and data integration still apply.

Leveraging New Age Data Warehousing

Data warehousing and business intelligence (BI) have been growing and getting more complicated over the years. As IT engineers, consultants, and analysts get more experience; they share these experiences with colleagues when they join other companies, publish articles, or carry out training. By sharing their understanding, they have helped to advance the overall intelligence of the IT industry. It has directed the formation of conventional knowledge about how to design, build, and implement Data Warehouse and Business Intelligence solutions.

But there are limitations to this conventional wisdom when people consider it like fact. Sometimes, people blindly pursue the general advice without making sure that it actually implements to their specific situation. And there can be occasions where you have to challenge conventional knowledge.

The IT industry is still in a phase of active and sometimes unstable development. It’s not always smart to put extra trust in conventional understanding, particularly when the industry is developing and growing in ways that could help you provide strong performance management, Business Intelligence, and Data Warehouse solutions.

Exposing Conventional Wisdom

Conventional wisdom claims that a Data Warehouse is independent of applications, which is not correct. It is beneficial in financial applications, especially in forecasting, budgeting, and planning. Business users require the elasticity to carry out a number of iterations on a group of numbers before approving a budget, forecast, or plan. They should also be able to scrutinize historical data to make their projections. However, business applications don’t have the ability to do this. And data warehouses can’t fulfill this need because they aren’t made to support applications. So, business users opt for the use of spreadsheets that dissipate their time and efficiency.

The usage of spreadsheets has increased the range of errors and made it unfeasible to document how the numbers were produced. With the present business and regulatory environment, this is not adequate for many CFOs. An effective method is to combine these financial systems with an application that has strong connections to a Data Warehouse. The Data Warehouse works both as a system of distribution that sends the data to every business process or a user needing it and as a recording system, where the business budget, forecast, or plan is stored. Firstly, data flows from source systems to data warehouses, then data marts, cubes, and can finally be utilized by BI applications.

How Data Warehouse Fulfill Benefits

All architectural diagrams display this one-way flow. The sources for the data warehouse environment have prolonged from back-end office operations to include customer-front applications, external data received from suppliers and partners, and many previous workgroup or desktop applications. The data flows from throughout the organization and often beyond. The Data Warehouse ecosystem is now an information hub that shares data across many applications and data stores. Data Warehouse is now the system of distribution for many business processes, applications, or staff that requires this information.

How Data Warehouse Fulfill Benefits Your Business?

As per a recent report by Allied Market research, the worldwide market for data warehousing is predicted to increase by up to $34.7 billion by 2025. It is almost twice its worth of $18.6 billion in 2017.

So what drives investment in enterprise data warehouse growth? Cloud data warehouse technology increased the value of innovative systems and practices that augment efficiency and lessen costs across company operations. Today, different departments such as marketing, finance, and supply channels, take benefit from a modern data warehouse exactly the way engineering and data science teams of the organizations do.

The Requirement to Access and Act on Data in Real-Time

Modern data warehouses make data viewable and actionable in real-time by supporting an extract-load-transform (ELT) method over the omnipresent extract-transform-load (ETL) model. in this model, data is cleansed, transformed, or augmented on an exterior server previous to loading into the data warehouse. With an ELT method, raw data is withdrawn from its source and loaded, moderately untouched, into the data warehouse, making it much quicker to access and analyze.

The Search for a Holistic Vision of the Customer

The assurance of a data lake strategy is that complete company data, whether structured, semi-structured, or raw data, can be quickly and easily mined from one place. Using this approach, an enterprise data warehouse can facilitate a 360-degree view of the customer, helping to advance campaign performance, reduce churn, and finally, raise revenue. An enterprise data warehouse also makes predictive analytics possible, where teams use conditional modeling and data-driven forecasting to notify business and marketing decisions.

Data Warehouse Strategy

Considering Data Lineage to Ensure Regulatory Compliance

A modern data warehouse follows compliance with the EU’s General Data Protection Regulation (GDPR). Without a prepared data warehouse, a company would probably have to set up a complex process to fulfill each GDPR request. It would include numerous functions or business units looking for pertinent PII data. When you have a data warehouse in place, there is basically just one place you have to look at.

Enabling Non-Technical People to Query Data Rapidly and Economically

Building a data warehouse can also profit non-technical employees in various job roles beyond marketing, finance, and the supply channel. For example, architects and store designers can make the customer experience better inside new stores by drumming into data from IoT devices located in existing locations to recognize which division of the retail footprint is most or least engaging. Global amenities managers can support their decision-making on whether to enlarge plants or move product lines on a strong set of information, comprising of hiring and retaining data of employees, in addition to typical metrics such as cost per square foot.

The Need to Bring Data Together into a Single Place

Most of the data sets today are huge to transport and query rapidly and cost-efficiently. To control costs and latency, companies use local clouds. According to research, 81% of companies with a multi-cloud strategy results in data sharing across platforms from contending cloud providers. Removing these roadblocks is the main concern for organizations that struggle to be really data-driven.

Top-class data warehousing technology will enable organizations to store data across various regions and cloud providers, and view insights from a globally combined data set.

Summary

The Modern Data Warehouse provides a large-scale, high-performance, and cost-effective approach to enable your data integration tool to help you find actionable insights. It supports diverse workloads, real-time data, and a huge number of concurrent users to facilitate a new set of analytics features. When you leverage top solutions for your data, it will help you integrate existing Business Intelligence, ETL, data mining, and analytics tools.

If you are also experiencing a problem in managing a diverse range of large data volumes within your organization that is obstructing data integration, there is nothing better than adopting cloud data warehouse technology. Are you interested in learning more about this data solution, get the best expert advice from the Data Warehouse Consulting experts! ExistBI has consultants in the United States, United Kingdom, and Europe, contact them for more information.


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Improving Data Security and Management – Data Lake Security Best Practices

It has become common in the modern business world that big data, which is the large volume of data gathered for analysis by organizations, is a major part of any business strategy. Whether it is operations, sales, marketing, finance, human resources, or any other department, each one of them depends on big data solutions to stay competitive in the market. Although, how organizations handle that big data is vital to the benefits they gain from it. Hence, Data Lake Solutions provides organizations with the tools to improve their Data Security and Management. Also, in this blog post, we are going to discuss the data lake security best practices…

big data solutions

The growth in the amount of unstructured data is a challenge to modern organizations. Over the last decade, there has been rapid growth in data creation and inventive transformations in the way information is processed. The increased number of portable devices represents the development of various data formats such as binary data (images, audio/video) CSV, logs, XML, JSON, or unstructured data (emails, documents) that are challenging for database systems.

Maintenance of data flows of all data access points create issues for commonly used data warehouses based on relational database systems. It is often found that with the quick application development, companies may not even have an idea of how the data will be processed, but they have a strict target to use it at several points. While it’s possible to save unstructured data in the RDBMS system, it can be expensive and complex.

Here, you can enter the world of data lakes. Data lakes are storage houses that can comprise data from numerous sources. Other than data processing for direct analysis, all coming data is stored in its relative format. This model enables data lakes to store massive amounts of data while utilizing the least resources. Data is only processed at the time of usage, while in a data warehouse, all incoming data is processed. Ultimately, it enables data lakes to be a proficient method for storage, resource management, and data preparation.

Do you really require a data lake, particularly if your big data solution already comprising a data warehouse? The answer will be a loud ‘yes’. In a world where huge data volumes are shared across limitless devices continues to grow, a resource-competent means of accessing data is vital for success. Here are the following reasons why the requirement for a data lake is getting more urgent with time;

1. 90% of Data Has Been Produced Since 2016

90% of all data is a lot—or is it? Wi-Fi, smartphones and high-speed data networks have become a part of everyday life for the last twenty years. At the starting of the 2000s, streaming was restricted only to audio, while broadband internet was utilized regularly for web surfing, downloading, and emailing. In that condition, device data was at the least amount and the actual data used was generally about interpersonal communication, particularly because videos and TV hadn’t been part of the process, which encouraged high-quality streaming. When the decade came to an end, smartphones had become commonplace and Netflix had transited its business priority to streaming.

It means the internet has experienced huge growth in smartphone applications, social media, streaming services (audio and video), streaming video game platforms, downloaded software rather than physical media between 2010 and 2020, all creating exponential use of data. Is this period of growth significant to business? Imagine how many businesses have connected apps that are continuously transferring data to and from devices, for controlling appliances, deliver instructions and specifications, or gently convey user metrics in the background.

In 2019, deployment of 5G data networks broadly started, so the bandwidths and speeds only got better. Hence, the quantity of data will only get more as technology lets the world get even more connected. Is your data lake ready for it?

Business Analytics

2. 95% of Businesses Hold Unstructured Data

In today’s digital world, businesses assemble data from all types of sources, and most of them are unstructured. Think about the data collected by a company that sells services and schedule appointments through an app. While several data streams come in predefined structured formats and fields like phone numbers, dates, time stamps, transaction prices, etc. still, the company has to archive and store a large amount of unstructured data. Unstructured data can be any type of data that doesn’t enclose an inbuilt structure or predefined model, which makes it hard to search, sort, and evaluate without additional preparation.

For example, unstructured data comes in a variety of formats. When a user makes an appointment, all the text fields filled make that appointment sum up to the unstructured data. Emails and documents are other types of unstructured data within a company. The social media posts of the company and photos or videos that are taken by employees as notes during the services are also counted as unstructured data. Similarly, any instructional videos or podcasts created by the company as marketing assets are also unstructured.

3. 50% of Businesses Trust Big Data to Improve their Sales and Marketing

Many people believe big data is beneficial in aspects of its technical usage. Undoubtedly, a company that works via a smartphone app or presents a form of streaming uses big data and is providing a service that just wasn’t possible twenty years ago. However, big data is much more than offering streaming content. It can generate a lot of important improvements in sales and marketing. Based on a report by McKinsey, 50% of businesses believe that big data is empowering them to modify their approach in these departments.

All You Need Is A Data Lake!

The above indicates one point that your organization needs a data lake. And if you don’t prioritize data management, it’s obvious that your competitors will overtake you in areas such as operations, sales, marketing, communications, etc. Data is basically a part of life today, providing precise data-driven decisions and unparalleled insights into deep causes. When collaborated with machine learning and artificial intelligence, you can also use this data for predictive modeling to forecast future events.

Data Lake Security Best Practices – How Can You Improve the Security of Data?

Data lakes are a competent and safe way to save all of your incoming data. Worldwide big data is predicted to rise from 2.7 zettabytes to 175 zettabytes by 2025, which means there will be exponential growth, all coming from a growing number of data sources. They are not like data warehouses, where structured and processed data is required. Data lakes work as a single repository for raw data across multiple sources.

Along with a list of benefits, a data lake also has some inbuilt risk of a single point of breakdown. Obviously, it’s uncommon for IT departments to identify an exact single point of failure in today’s IT world. Backups, redundancies, and other typical foolproof techniques are liable to protect company data from correct disastrous failure. It provides double security, so when enterprise data stays in the cloud, data delegated in the cloud rather than the local environment has the additional benefit of trusted vendors creating their own protection systems for your data.

Data Lake Security Best Practices

It doesn’t necessarily mean that your data lake is safe from all threats? As with all technologies, a true evaluation of security risks needs a 360-degree view of the situation. Before you step into a data lake, don’t forget to consider these six ways to keep your configuration safe and protect your data.

Establish Governance: A data lake is constructed to store all data. As a storehouse for raw and unstructured data, it can consume anything from any source. But that doesn’t essentially mean that it has to. The sources you choose for your data lake should be scrutinized for how that data will be processed, managed, and used. The threats of a data swamp are very real and keeping them at bay depends on the quality of numerous things like the sources, the data coming from the sources, and the rules for data ingestion. By setting up governance, it’s possible to recognize things such as ownership, security rules for responsive data, data history, source history, and much more.

Access: One of the major security risks concerned with data lakes is associated with data quality. Rather than a macro-scale issue like a whole dataset coming from a single source, risk can come from specific files within the dataset, either when ingesting or after due to hacker access. For example, malware can cover within an apparently gentle raw file, waiting for implementation. Another probable vulnerability arises due to user access if sensitive data is not correctly confined, it’s possible for corrupt users to access those records, perhaps even adjust them.

By building strategic and strict rules for function-based access, it’s possible to reduce the risks to data, especially sensitive data or raw data that has yet to be inspected and processed. Generally, the broadest access should be for data that has been established to be clean, correct, and ready to use, thus restraining the possibility of accessing a potentially harmful file or gaining unsuitable access to susceptible data.

Data Security

Use Machine Learning: Some data lake platforms come with integral machine learning (ML) functionalities. The usage of ML can considerably reduce security risks by increasing the speed of raw data processing and classification, mainly if used in combination with a data cataloging tool. By this level of automation, a large quantity of data can be processed for common use while also spotting red flags in raw data for added security exploration.

Partitions and Hierarchy: When data is ingested into a data lake, it’s vital to save it in an appropriate place. The common harmony is that data lakes need numerous standard zones to hold data based on how reliable it is and how ready-to-access it is. The various zones are:

  • Temporal: Where transient data like copies and streaming reels remains before deletion.
  • Raw: Where raw data stays before processing. Data in this zone can also be further encrypted if it encompasses sensitive data.
  • Trusted: Where data that has been confirmed as reliable stays for trouble-free access by data analysts, scientists, and other end users.
  • Refined: Where enhanced and influenced data stays, generally as final outputs from tools.

You can create a hierarchy by using zones like these, when joined with role-based access, can help lessen the prospect of the wrong people using potentially sensitive or malevolent data. 

Data Lifecycle Management: Which data is continuously in use across your organization? Which data hasn’t been touched for years? Data lifecycle management is the process of recognizing and segmenting stale data. In a data lake ecosystem, older stale data can be shifted to a definite tier designed for competent storage, making sure that it is still available whenever needed but not captivating the required resources. A data lake driven by ML can even utilize automation to recognize and process stale data to make the best use of overall efficiency. While this should not impact directly on security issues, a competent and well-supervised data lake enables it to work like a well-oiled machine rather than failing under the burden of its own data.

Data Encryption: The proposal of encryption is very important to data security is not anything new, and most data lake platforms bring their own methods for data encryption. Of course, it is critical to know how your organization implements. In spite of which platform you utilize or what you choose between on-premises vs. cloud, a powerful data encryption strategy that works with your current infrastructure is completely vital to protect all of your data, whether it is in motion or at rest.

Let’s Create Your Data Lake!

What’s the most suitable method to make a secure data lake? By selecting the best range of products, you can create a data lake in just a few steps. With cutting-edge data lake solutions, you get advanced capabilities to integrate it with best-in-class analytics tools.  Are you considering creating a data lake? Contact leading service providers to get answers to your major concerns!


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How Data Science Consulting Can Empower Your Business?

Data is one of the most important assets that every organization has because it helps business managers to make fact-based decisions, statistics, and trends. Data Science Consulting for businesses has emerged as a multidisciplinary field due to this rising scope of data. It utilizes scientific approaches, procedures, algorithms, and framework to take out the information and insights from a massive amount of data, which can be either structured or unstructured.

Data science is a concept to carry ideas together, examine data, Machine Learning, and their connected strategies to understand and analyze authentic phenomena with data. It is an extension of different data analysis categories like data mining, statistics, predictive analysis, and so on. Various techniques used in Data Science include machine learning, visualization, pattern recognition, probability model, data engineering, signal processing, etc.

The development of an abundance of data has given huge importance to many features of data science, especially big data. However, data science is not restricted to big data only as big data solutions focused more on organizing and preparing data instead of analyzing them. Also, due to Artificial Intelligence and Machine Learning, the significance and growth of data science have been enhanced.

Data Science

Importance of Data Science

With the help of professionals, you can use their expertise to turn advanced technology into actionable insights and make the right use of Big Data. Today, a great number of organizations are unlocking their doors to big data and utilizing its power, which is growing the worth of a data scientist who understands how to withdraw actionable insights out of gigabytes of data.

It is getting clearer by the day that there is huge value in data processing and analysis and exactly where the need for a data scientist is. Executives understand how data science is a vast field and how data scientists are like modern superheroes, but many are still uninformed of the value a data scientist can provide in an organization. Let’s have a look at its benefits.

  • With the right guidance under Data Science, the companies can identify their client in a better and more informed way. Clients are the base of any product in an organization and play the most important role in their victory or failure. Data Science allows companies to connect with their customers in a tailored manner, and thus, proves the superior quality and supremacy of the product.
  • Data Science allows products to convey their story strongly and attractively. When products and organizations use this data collaboratively, they can share their story with their audience, which forms enhanced product connections.
  • One of the imperative features of Data Science is that the results it shows can be implemented to almost every type of industry, such as travel, healthcare, education, and many more. With the help of Data Science, the industries can evaluate forthcoming challenges easily, and can also confront them efficiently.
  • At present, data science exists in almost all the fields and there is a diverse range of data available in the world today. If used appropriately, it can direct the product to the way of success or failure. The data, when used properly will hold significance for attaining goals for the product in the future.
  • Big data is constantly budding and increasing. Using different tools that are developed frequently, big data helps the organization to determine complex concerns related to IT, human resources, and resource management competently and effectively.
  • Data science is gaining immense value in every business and hence playing an important role in the performance and growth of any product. Therefore, the necessity of data scientists is also increasing as they have to execute the important job of managing data and providing solutions for definite problems.
Data Science

What is the result of including data science in your business?

  • Alleviating risk and fraud

Data scientists are qualified to recognize data that stands out in a definite way. They generate statistical, network, and big data methodologies for predictive fraud susceptibility models and use them to produce alerts that help make timely responses when abnormal data is recognized.

  • Delivering the right products

One of the benefits of data science that organizations can exploit is they can discover when and where their products sell best. It can help you deliver the relevant products at the right time and develop new products to fulfill the customers’ needs.

  • Customized customer experiences

One of the most popular advantages of data science is its capability to recognize their audience on a very coarse level, for sales and marketing teams. With this information, an organization can generate the best possible experiences for their customers.

Data science consulting for businesses

Future of Data Science in Modern Businesses

The impact of Data science has impacted areas and industries differently. Its influence can be seen in multiple sectors like the retail industry, healthcare, and education. In the healthcare business, new medicines and techniques are being exposed constantly, and there is a requirement to improve care for patients. By including data science techniques in healthcare, you can find a solution that can assist in taking care of patients.

Education is another sector where you can notice the benefits of data science clearly. The most recent technologies, such as smartphones and laptops have now become an imperative part of the education system. By facilitating data science, better opportunities are formed for the students, which allows them to improve their knowledge.

Business Intelligence To Make Smarter Decisions

Traditional Business Intelligence has more expressive and static behavior. However, by associating data science in BI, it has modified itself to develop into a more dynamic field. Data Science has made Business Intelligence integrate into a wide range of business operations. With the enormous increase in the quantity of data, businesses require data scientists to examine and obtain meaningful insights from the data.

The meaningful insights will help the data science consultants to evaluate information at a big scale and grow essential decision-making strategies. The process of decision making involves the assessment and estimation of various factors included within it. The four-step process decision making involves:

  1. Understanding the context and nature of the issue that you need to solve.
  2. Discovering and measuring the quality of the data.
  3. Executing the right algorithm and tools for concluding a solution to the definite problem.
  4. Using story-telling to interpret your insights for a better understanding of teams.

This is how businesses require data science to facilitate their decision-making process.

Creating Better Products

Companies should draw customers’ attention to their products. They need to create products that meet the needs of customers and present guaranteed satisfaction to them. Therefore, industries need data to make their product in the best way possible. The process includes the analysis of customer reviews to come across the best fit for the products. This analysis is executed with the help of the most advanced analytical tools of Data Science.

In addition, industries make use of the current market trends to plan a product for multiple audiences. These market trends present businesses with hints about the existing need for the product. Businesses develop with innovation. With the expansion in data, industries are able to execute not only newer products but also different innovative strategies.

Data Science

Managing Businesses Efficiently

Nowadays, businesses are data-rich. They hold an overabundance of data that allows them to obtain insights through a suitable analysis of the data. Data Science platforms uncover the unseen patterns that are existing inside the data and help to make consequential analysis and prediction of events. Data Science helps businesses to manage themselves more effectively. Both large and small scale businesses can benefit from data science to grow further.

Data Scientists help companies to analyze the wellbeing of the businesses. So the companies can forecast the success rate of their decided strategies. Data Scientists are accountable for transforming raw data into meaningful information. It helps in abbreviating the performance of the company and the health of the product. Data Science identifies key metrics that are essential for calculating business performance. According to this, the business can take important measures to calculate and access its performance and take suitable management steps. It can also assist the managers in analyzing and finding the potential candidates for the business.

Predictive Analytics to Forecast Results

Predictive analytics is the most vital element of modern businesses. With the arrival of highly developed predictive tools and technologies, companies have extended their potential to deal with varied forms of data. In technical terms, predictive analytics is the statistical analysis of data that encompasses several machine learning algorithms to forecast future results using the historical data. There are various predictive analytics tools such as SAS, IBM SPSS, SAP HANA, etc.

There are different applications of predictive analytics for businesses like customer segmentation, sales forecasting, risk assessment, and market analysis. Predictive analytics provides businesses with an edge over others as they can forecast future events and take suitable measures regarding these. It has its own definite implementation based on the category of industries. However, despite that, it shares a common function in foreseeing upcoming events.

Utilizing Data for Business Decisions

As explained in the previous section, data science is playing an imperative role in forecasting the future. These predictions are needed for businesses to be aware of their future outcomes. Based on these results, businesses make important decisions that are data-driven. Previously, many businesses would make poor decisions due to the lack of research and surveys or self-confidence on gut feelings only, which would result in some devastating decisions leading to the loss of millions.

However, with the existence of an excess of data and essential data tools, it is now achievable for the data industries to make thoughtful data-driven decisions. Additionally, business decisions can be made with the help of influential tools that can not only do faster data processing but also present accurate results.

Data Science Data Tools

Automation of Recruitment Processes

Data Science has performed a key role in driving automation into various industries. It has taken away the common and recurring jobs. Resume screening is one such job. Companies have to deal with a crowd of candidate’s resumes daily. Many major businesses draw the attention of thousands of candidates for a position. To making sense of all of these resumes and choose the right candidate, businesses exploit the power of data science.

The data science technologies such as image recognition can transfer the visual information from the resume into a digital format. Then, it processes the data using a variety of analytical algorithms like clustering and classification to find the right candidate for the job. Moreover, businesses learn the right trends and analyze the best possible applicants for the job, which allows them to reach candidates and have a profound insight into recruitment and job websites.

Conclusion

Data science is one of the developing fields in businesses today. It has become an essential part of almost all sectors irrespective of its size and type. It helps them to find the best solutions that meet the needs of challenges for an ever-increasing demand and sustainable future.  As the significance of data science is growing day by day, the need for a data scientist is also increasing. Therefore, a data scientist should be competent to provide great solutions that fulfill the needs of all the fields. To make this happen, they should have appropriate resources and systems to help them achieve those goals easily.

Data science can sum up to the value of any business that can use their data conveniently. From statistics and insights across all business processes and selecting new candidates, to assisting senior staff in making better fact-based decisions, data science is important to any company. Now you have an understanding of how data science plays a vital role in businesses for business intelligence, for making better products, for escalating the management capabilities of companies, and for predictive analytics. Therefore, it is recommended you discuss your data with Data Science Consulting experts to unlock your potential.


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Choose Automated and Smart, Cloud Based Data Integration Service

Today, organizations are increasingly investing in new cloud-based platforms, processes, and environments to exploit benefits such as scalability, flexibility, agility, and cost-efficiency. Concurrently, organizations also acknowledge that data management is the initial step to successful digital transformation. With a professional Cloud based Data Integration Service, you gain the ability to unite your data sources and drive important insights quickly.

Cloud-Based Data Integration Services

When you put these trends together, IT departments are employed to help the business become cloud-ready, to modernize analytics. Enterprises are modernizing or adopting new data warehouses and data lakes in the cloud environment. In one cloud data platform, you have a mutual solution for both historical and predictive analytics.

However, when it is a matter of managing the data to speed up the value and bring ROI with an investment in cloud data warehouses, lakehouses, and data lakes, the usual approach that IT departments tend to choose, can have major implications like increased cost, project overruns and maintenance intricacy removing any benefits of modernizing analytics in the cloud.

Challenges in a Multi-Cloud and Hybrid World for Data Management

As IT companies begin sustaining cloud and analytics or AI projects, the inducement is to accuse their technical developers of designing, developing, and deploying the right solution. However, they hurriedly get into data challenges if they fall to the hand-coding path. In a lot of cases, these complexities exploit on-premises data warehouses and data lakes:

Varied and siloed data:

Many organizations have different types of data available in many dissimilar systems and storage formats, either on-premises or in the cloud. The data is every so often distributed throughout siloed data warehouses, data lakes, cloud applications, or third-party assets. Though, more data is created from online transaction systems and communications like web and machine log files and social media. For instance, in a retail firm, data is dispersed across numerous different systems. These systems include point of sale (POS) systems, including in-store transaction data, customer data in a CRM and MDM system, social and web click-stream data accumulated in a cloud data lake, and more. ( Cloud adoption )

Lack of data governance and quality:

Varied and siloed data often changes the values of data quality and governance. Policies are hardly ever enforced constantly. Data is discarded into data lakes creating swamps where data is hard to search, understand, manage, and defend. Even inferior is soiled data approaching a cloud data warehouse, where multiple business analysts and other data users rely on it for decision making, predictive analytics, and AI.

A Lot of Emerging and Changing Technologies:

As the amount of data is increasing, new vendors, technologies, and open source projects are coming into effect that changes the IT environment. There are traditional, new, and evolving technologies available for computing, storage, databases, applications, analytics, and even new AI and machine learning. Developers may efforts to stay on top of this varying environment, making it complicated to standardize or execute a methodology.

Why some organizations still using hand-coding?

There are still some organizations that choose hand-coding, supposing that it’s an easier approach than deploying a data integration tool, which may require some level of skills and knowledge. In addition to this, developers may think that integration tools can limit their creativity for a custom use case and practice. In many cases, these are some short-sighted doubts about a smart and automatic data solution. However, hand-coding may be suitable for faster proofs-of-concept (POC) with a low-priced entry.

Data Integration

Disadvantages of Hand Coding in IT

Initially, IT departments may find hand-coded data integrations as a fast, economical way to construct data pipelines. But there are important disadvantages to consider.

Hand Coding Is Costly

In due course, hand-coding is costly to execute, operate, and maintain production. Hand coding needs to be edited and optimized from growth to consumption. And with large IT budgets in operations and maintenance processes, the cost of hand-coding increases with time.

Hand Coding Is Not Long-Term

With new and emerging technologies, developers have to re-structure and recode every time when there is a technology change, an upgrade, or even a modification to the primary processing engine.

Hand Coding Lacks Automation

Hand-coding doesn’t extend for data-driven organizations and can’t maintain speed with enterprise requirements. There are basically too many requirements for data integration pipelines for IT users to contain. The only way to range the delivery of data integration projects is through automation, which needs AI and machine learning.

Hand Coding Lacks Enterprise Width

It took many years for data integration hand coders to understand how important and essential data quality and governance are to make sure the business has reliable data. It is even more significant for data-driven companies for the development of AI and machine learning. Hand coding can’t present enterprise width for data integration, metadata management, and data quality.

Disadvantages of Hand-Coding for Businesses

The limitations of hand-coding aren’t limited to IT only. Eventually, hand-coding influences overall business outcomes. Here are the following key areas where hand-coding can have a harmful business impact:

  • Higher Cost
  • More Risks
  • Slower Time to Value
Data Integration

Create that Illuminating Moment with Cloud Data Management

After struggling for months in the initial modernization project, Informatica realized the need to re-evaluate their cloud data management strategy. By reconsidering the drawbacks of hand-coding, they improved their strategy to decrease manual work and make efficiency better through automation and scaling. Businesses require a cloud data management solution that comprises:

  1. The facility for both business and IT users to recognize the data ecosystem, through an ordinary enterprise metadata establishment that presents end-to-end lineage and visibility throughout all environments
  2. The capacity to reuse business logic and data conversion, which increases developer productivity and allows business stability as it encourages integrity and uniformity of reuse
  3. The capability to conceptualize the data transformation logic from the primary data processing engine, which will make it long-lasting under the quickly changing cloud environment
  4. The capability to connect to an assortment of sources, targets, and endpoints without any requirement for specialized code connectivity
  5. The ability to process data competently with an extremely performant, scalable, and dispersed server-less data processing engine or the capacity to control cloud data warehouse pushdown optimization
  6. The ability to work and continue data pipelines with the least interruptions and cost

Components of Smart, Automatic Cloud Lakehouse Data Management

As the organizations are joining and modernizing their on-premises data lakes and warehouses in the cloud or build up new ones in the cloud, it has become more important than ever to escape from the drawbacks of hand-coding. Especially, today, with the evolution of lakehouse is presenting the best of data warehouses and data lakes that come with cloud agility and flexibility. So it’s important to adopt metadata-driven intelligence and automation to create efficient data pipelines.

Automatic Cloud Lakehouse Data Management

While many IT departments only focus on data integration, a more enhanced solution is required to solve today’s enterprise needs across the complete lifecycle of data management.  Here are four main components required in the data management strategy:

Data Integration

A best-in-class intelligent, automated data integration solution is necessary to manage cloud data warehouses and data lakes. The below are a few functionalities that allow you to rapidly and competently build data pipelines to send into your cloud storages:

  1. Codeless integration with templates, suggested by AI for next-best transformations
  2. Group ingestion of files, databases, changed data, and streaming
  3. Pushdown optimization of databases, cloud data warehouses, and PaaS lakehouses
  4. Serverless and expandable scaling
  5. Spark-based functions in the cloud
  6. Large and native connectivity
  7. Stream processing
  8. AI and machine learning growth to handle schema drift and complicated file parsing
  9. Support for data and machine learning processes (DataOps and MLOps)

Data Quality

Nowadays, with the development of cloud lakehouses, it’s not sufficient to encompass top-class data integration. You also require best-in-class data quality. The smart, automated data quality features ensure that data is cleansed, consistent, trusted, and standardized across the enterprise. Here’s what you should look for:

  1. Data profiling integrated with data governance
  2. Data quality policies and automated rule creation
  3. Data dictionaries to manage lists of values
  4. Cleansing, parsing, verification, standardization, and de-duplication processes
  5. Integration with your data integration tool
  6. Data analytics for quality
  7. Spark-based functioning in the cloud

Metadata Management

A general enterprise metadata establishment allows smart, automated, point-to-point visibility, and extraction across your environment. Wider metadata connectivity throughout different data types and sources make sure that you have visibility into it and can use data kept protected in varied transactional applications, data stores and systems, SaaS applications, and custom legacy systems. An ordinary enterprise metadata structure enables smart, automated:

  • Data discovery
  • End-to-end lineage
  • Value tagging and data curation
  • Perception of technical, business, functional and traditional metadata
  • Connectivity through on-premises and cloud for various databases, apps, ETL, BI tools, and other systems

Cloud-Native Features Built on a Base of AI and Machine Learning

This component is foundational and performs under the other three. The components of data integration, data quality, and metadata management need to be developed on the basis of AI and machine learning to manage the exponential growth in organizational data. Always pick up the cloud-native solution that is multi-cloud, API-driven, and microservices-based and also look for the following features in it:

  1. AI/ML-driven automation, like next-best transformation suggestions, data pipeline resemblance, operational notifications and auto-tuning
  2. Containerization
  3. Server-less architecture
  4. Minimum install and setup
  5. Auto-upgrades
  6. Usage-based rates
  7. Trust certifications
  8. Integrated full-stack high accessibility and superior security
AI/ML Data

Take a Comprehensive Approach to Smart, Automatic, and Modern Cloud Data Management

Many organizations require data to understand, process, and grow their business effectively, but data complexity is an obstruction. IT companies are searching for an intelligent, automatic data management solution that fills the space between on-premises and cloud deployments without requiring rebuilding everything from the start before they can garner the benefits of successful execution.

Without a united and wide-ranging data platform, organizations are required to exploit different point solutions together that were never intended to work together initially. It takes immense time to integrate these systems, which is also very expensive, risky, and inflexible to be amended later. If there is any change in one point of the solution, then you have to repeat and retest all integrations in the system.

You don’t need a big bang implementation to take an enterprise method. One of the major benefits of having intelligent and automated data management is that companies can compress the use of general methodologies, processes, and technologies increasingly, starting with one or two projects initially.

By choosing an enterprise data management platform for high productivity, IT teams can speed up start-up projects to bring instant business value. As the IT companies implement supplementary projects, it can exploit and reuse available assets, considerably decreasing the cost and time to bring new capabilities to the business and making consistency and control better.

With the leading metadata-driven cloud data management solutions in the industry, you get the power to leverage the complete features of your cloud data warehouse and data lake across a multi-cloud, hybrid ecosystem. You can boost the efficiency, ensure more savings, and can start small initially, and scale with top-in-class data integration tools for the cloud, on an AI-driven, intelligent data management platform.

Summary

As you know, data is a valuable asset for businesses. So when you run a business on a large scale, hand-coding can bring a lot of manual errors. The IT department cannot suitably take care of your data management, quality, governance, security, and derive insights quickly that also needs to be actionable. Therefore, an automated data management solution is a smart option to start managing your data intelligently.

Are you worried about bringing value to your business’s most important asset, data? Rise above the manual coding and choose an automated approach with professional Data Integration Services that will help you to exploit cloud capabilities for your databases. ExistBI has consulting teams in the United States, United Kingdom, and Europe.


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SAP BI 4.3 On the Way- Join SAP Business Objects Training to Learn More!

Change is on the way for SAP BusinessObjects users in the form of SAP BI 4.3, which could be available in the next few months. This update is the first major 4.x release after 4.2 in early 2016. After a long gap of four years in software development, there will be some huge opportunities to grab and a few challenges to confront. Taking part in SAP Business Objects Training will help you to understand these upcoming changes in a better way.

SAP BI 4.3

What Are The Major Changes?

Here are some of the major changes that are expected to cover in BI 4.3:

  • Elimination of the old Launchpad /InfoView interface that will be replaced by a new tile-based design
  • Better integration with SAC
  • Redesigned Web Intelligence interface
  • New Web Intelligence Capabilities
  • New data-modeling roles for Web Intelligence report builders
  • Explorer and Dashboards functionality deleted

Some of these changes will impact customers more than others, and those who have exceedingly invested in deploring tools like Dashboards and Explorer will require thinking deeply about their next steps.

The New Look Front End

Many of the end-users access BusinessObjects through its web-portal known as the Launchpad/ InfoView as it was called in XIR2 and 3. This portal lets the people log in, explore their reports and documents, interact with them, schedule them, probably produce new Web Intelligence reports or edit existing ones. This process still won’t change, but the web-pages will appear very different and several work-flows will change.

SAP named its web-design environment as ‘Fiori’, and it has slowly turned around Fiori-style front-ends throughout its product range. A Fiori-style Launchpad is already obtainable as an option in BI 4.2, from SP4 onwards, so you can view if you have that version or superior installed. Modify your normal URL from http://:/BOE/BI to http://:/BOE/BILaunchpad instead, and you can see it.

Fiori

Fiori BI Launchpad in BI 4.2 SP6

This new-interface means that users of other SAP products like SAC or their CRM/ERP products will feel more native in BOBJ. But for those with BOBJ only, it is a big change. In Sap BI 4.3, as the single user interface will be available, it will bring new functions along with the requirement to learn its shortcomings and work-flows.

Removal of Explorer and Dashboards

These tools are based on Adobe Flash, and therefore will not be supported by many tech companies by the end of 2020. Even if you don’t upgrade to BI 4.3, it will be harder for your IT teams to keep these tools running. This new upgrade to BI 4.3 eliminates all support for these tools from BOBJ, and an in-situ upgrade will possibly delete the actually installed software too. 

Re-design of the Web Intelligence Interface

The arrival of 4.0 back in 2011 was carried in the ribbon menus in Web Intelligence, the last large redesign of the tool. For users shifting from XIR2 or XI3.1 to BI4.x, the main task has always been for discovering the buttons that you are aware of stays there but are just buried down in the tabbed-structure of those ribbon menus.

SAP BI 4.3 changed the interface once again, but not only in menu styles. The query panel, universe object icons, input controls, locations of navigation and object panels, filter bars; all are changing.

SAP has committed functional parity for Web Intelligence reporting between late stage 4.2 and the release of 4.3, so the buttons will remain in there someplace. However, it will be a challenge to locate them in unknown surroundings.

SAP BI 4.3

New Data-Modeling Concept/Role for Web Intelligence Report Builders

Along with new features and behaviors of Web Intelligence, there is one more new concept being added in BI 4.3 that is WebI as a data-modeling tool.

Presently the end-users who build the attractive, informative elements of a report also need to understand how to make the technical data aspects of a report, including the possible complex merging of multiple queries and the formation of complex variables and calculations.

In 4.3, these two tasks can be more easily separated. Technical authorized-users can build datasets in Web Intelligence from multiple universes, multiple queries, including spreadsheets or CSV-based information, write freehand SQL, merge the data, write variables and calculations and then publish all of that as one precise package. These can then be utilized by other users as the data source for their reporting.

Greater Integration with SAC

SAP Analytics Cloud is SAP’s main representation in the analytics environment. It is where the greater part of their development and investment resides, and it is their goal to help people to use it and gain the benefits from its powerful capabilities.

In BI 4.3, the interoperability between SAC and BOBJ is advanced a few steps ahead. Businesses having licenses for both tools will be enabled it to integrate users more easily, and there will be links to your SAC occupancy from the BOBJ Launchpad.SAC will now consume data through the new Webi data models, which could unlock great opportunities for easier dashboard design.

The front-ends of both systems will be more homogenized with Fiori designed structure, so users will be feeling more content while switching between them: and the redesign of Webi is in part planned to imitate work-flows from SAC story design.

How to Prepare for These Changes?

This change is predictable and inevitable in the long-run, so how can you be better to get ready for it? Before you push yourself and update your production system to BI 4.3, there are numerous things you could do to mitigate the impact and train your users on the new opportunities it will provide:

  • Ensure that your live system is updated to BI 4.2. Service pack 7 is the newest version, but SP8 will be available soon. Upgrading now gives you the best ever lead-time available and makes sure you have the most recent bug fixes and patches, as well as providing you access to the latest version of the optional Fiori Launchpad – to practically use it before it becomes the only option left.
  • If you have bought your BOBJ licenses after 1st July 2009, you can create development BOBJ systems for no extra license cost. Create a new environment to test the BI 4.3 water, install new software, and then migrate content to it for testing. Once it trains your users or reveals issues, you can conclude when to upgrade your live environment.

Want to practice new functionalities of BI 4.3? Join SAP Business Objects Training today! ExistBI offers on-site or online training with live instructors in the United States, United Kingdom, and Europe.


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Keep Your Platform Future Ready with Data Integration Consultants

Make your Integration Platform Ready to Embrace the Latest Technology Trends

Some stimulating technology trends are rising that are predicted to strike the conventional systems over the next few years that will have a major impact on your data management systems. Is your system future ready with data integration? Will your integration platform be all set to sustain these new trends? If not, this is the time to seek advice from Data Integration Consultants so you will be ready to support a new age of business functionalities that your company will need to thrive.

Choosing the Right Data Integration Platform

If you think your traditional data integration platform is not sufficient to embrace the latest technology trends, changing over to a new platform can be a smart choice. It can be costly, but will surely bring good ROI if implemented correctly.

Data Integration Consultants

Selecting a data integration platform can be difficult, especially when your needs are complex. There’s a wide range of service providers to choose from, and not all will be suitable for your needs. You have to find answers to a few essential questions to help you through the decision-making process of choosing the right data integration solution for your business.

  • What is the use of data integration software in your organization?
  • Where does your data stay?
  • What are your prospected data needs?
  • Who’s going to work with your data integration software?
  • What is your budget?
  • Have you had a trial and demo with different data integration software retailers?
  • How to identify suitable data integration retailers?
  • How will the implementation and ongoing process take place?

Finding the answers for every question on your own can be difficult for you without any technical guidance, so you can hire data integration consultants for helping you out in each stage of choosing and implementing the right software. But if you want to hold using your legacy system, there are some circumstances that can interrupt the adoption of new technology trends in your organization.

Emerging Technology Trends Hovering to Interrupt the Conventional Systems

The IT industry is varying rapidly, and the needs of every business are also changing drastically with their growth. Hence, there are four key emerging technology trends that data management and IT experts need to monitor closely.

Cloud-Native Architectures

Companies are speedily changing from home-grown systems to both cloud services, whether it is a platform or SaaS. These cloud services consume cloud-native architectures that are distributed exceedingly on a regular basis, utilize parallel processing, engage non-relational data models, and can be turned on or shut down in just a few seconds. Integrating data from these systems can be difficult for traditional data integration systems that need the manual configuration of every data connection.

Future Ready with Data Integration

Your integration platform should have to be able to distinguish and become accustomed to these cloud-native architectures, which facilitate your business and IT teams to make regular changes to the application environment while preserving the integrity and security of existing enterprise data assets.

Event-Driven Applications

Legacy IT applications were developed on structured workflows that were well defined, much like a novel. And modern activity-driven applications are more like an adventure book, where the continuous transaction flow may not be pre-defined at all. Events and data are analyzed, heading towards dynamic workflows developing based upon the needs of the individual transaction. A great number of cloud-based container apps and functions are being used to set up capabilities in this way.

The challenge event-driven applications cause to data management is that they require the data context that conventional application workflows present. Context is the result of the series of events and actions that directed to the current position in time. Your integration platform should need to recognize and be able to carry the exclusive gradations of these event-driven applications and contextualize the data they create in a different way.

API Led Integration

Similar to applications based on the events, API led integration is a new model for carrying out IT capabilities collectively. Applications are imagined as pseudo-black boxes, and the thing that is managed in a structured way is the interfaces that lie between them.

From a data management point of view, this scenario lifts the need to manage data that is in motion and flowing between apps over APIs, and also for data at rest within every application. Your integration platform will need to comprehend the differences between these two types of data and should be able to ingest, convert, and load them together in your data warehouse for additional processing.

Data Integration

Streaming Data

The organization of all leading industries are now being flooded with streaming data approaching from a variety of data sources, such as IoT, Mobile apps, deployed sensors, cloud services, and digital subscriptions, etc. The data generated by these systems is significant, and even in a small organization, the number of data sources can be increasingly more. When you multiply large data streams through many data sources, there will be a massive amount of streaming data that a company needs to manage.

Most traditional integration platforms were considered for batch data processing, not for the scale of issues that arise due to streaming data. Cloud-based integration platforms are often well-matched to tackle streaming data challenges than on-premise systems because of the original capacity of the cloud environments where they work.

Is Your Integration Platform Future-Ready?

If you aren’t confident whether your integration platform is up to the mark to support these emerging technologies, then most likely it isn’t.  You can use a modern hybrid integration platform that provides cloud-scale and performance to distribute the functionalities that you need to connect anything, anytime, anywhere, and integrate it into your enterprise data environment.

Taking a decision regarding data in an organization is a critical task, and all users don’t have the right knowledge and skills to make a graceful and profitable decision. Whether you are selecting a new platform or optimizing the traditional one to make it adaptable to embrace new technology trends, you would require professional help of Data Integration Consultants to make it more consistent and successful.


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Role of Data Integration Consultants to Guarantee Project Success

Many of you will agree that businesses work better and attain more of their goals when they can utilize their data strategically. However, there are several forms and sources in which data exists in enterprises, such as CRMs, ERPs, mobile apps, etc., and combining and making use of that information is not as easy as it seems. Here, Data Integration Consultants come to your rescue and helps you make the most out of all your data. Let’s get to know the role of data integration consultants to guarantee project success.

For many years, companies were dependent on data warehouses with definite schemas for a specific use or application in the business. For example, marketing teams make use of data for better understanding the success of a specific campaign, get a clearer view of the buyer’s journey, or to plan the types and quantity of content they’ll require in the future.

As you all know, data is the most important asset, so suitably utilizing it can enable you to make intelligent business decisions, drive growth, and boost profitability. However, as per Experian, 66% of companies fail to get a centralized approach to data, where data siloes have been one of the most common issues. With the growing amount of information available throughout the variety of sources, businesses are adopting a partial approach to data.

Luckily, automated data integration processes can collect structured, unstructured, or semi-structured data from virtually different sources into a single place. Combining data into a central repository facilitates teams across the enterprise to make performance measurement efficiently, get meaningful insights and actionable intelligence, and make more well-versed decisions to sustain organizational objectives.

Role of Data Integration Consultants

What Is Data Integration?

According to IBM, data integration is a combination of technical and business processes used to connect data from different sources to extract meaningful and valuable information. In general, data integration creates a single, combined view of organizational data that is used by the business intelligence application to create actionable insights based on the completeness of the data assets, without concern about the original source or format. The huge amount of information generated by the data integration process is sometimes collected into a data warehouse.

A Combination of Theory and Practice

If it seems that this is something only for the enterprises that have huge data flows, you might be amazed to learn just how fascinating data integration is across different industries and sectors. In 2016, Capgemini surveyed that 65% of business executives said they fear to get inappropriate or uncompetitive if they fail to make use of big data. After the years of the survey, this percentage is continuously rising as executives across the world have realized the harmful impact of not including a data strategy and solution in place, which will affect every aspect of their business operations.

Today, staying competitive, work more capably, reducing costs and growing revenues means finding ways to collect, evaluate and optimize data to the fullest extent of its value. Data should not be treated as someday goals down the road, but as today’s driving initiative.

Data integration works throughout your organization to carry out numerous types of queries, from the coarsest questions to the overarching concepts. You can apply data integration to many detailed use cases that impact all teams and departments of your business, including:

Business intelligence – Business intelligence (BI) comprises everything from reporting to predictive analytics to operations, management, and finance. In addition, it depends on data existing in the whole organization to discover inefficiencies, gaps in processes, missed profitable prospects, and much more. Data integration provides you with the right BI tools and technologies that your company might need to make further strategic decisions.

Customer data analytics – Understanding who your customers are, what behaviors they show, and how they are expected to remain loyal or look somewhere else is vital to good business. Data integration allows you to extract information together from all your individual customer profiles into a unified view. From there, you can discover what the complete trends are and complement your existing customer retention strategies with real-time world insight.

Data enrichment – Fight against data decay by constantly updating contact lists like names, phone numbers, and emails. Merge this information with definite sets of exclusive information about every customer to create a much richer and more precise image of your buying audience.

Data quality – It is a challenge to manage the quality of data, it is important to ensure that your data requirements are reliable, that you understand how the data is generated and the tolerance for errors your organization is willing to accept. However, making the data integration process automatic eliminates many risks that are not conforming to your company’s data governance policies, growing both the accuracy and the value of the data available to teams across the organization.

Real-time data delivery – Businesses cannot wait days to provide actual numbers or insights; they have a few hours or sometimes minutes only. That’s why real-time data delivery is important for many businesses to adapt to customers, markets, vendors, and even general and compliance changes faster. Data integration allows you to check data from any point in the collection process anytime to find minute-by-minute insights into processes, workloads, and communications.

Data Integration

How Data Integration Consultants Plan Successful Projects?

Integrating various systems involves integrating different existing subsystems and then producing distinctive and new value for the customers or end-users. To make your efforts for integration planning successful, you must include a wide scope to make sure that the plan meets all specific business needs. A business analyst should start and direct every integration effort of systems to boost the success rate and reduce recurring tasks.

The process of integrating all data existing in different internal and external sources has become more complex in the last few years – typically because of a continuously growing massive volume of data handled by companies. And this process does not get any easier as new potential data sources continue to appear. The success of a data integration project does not only depend on the available systems, but also the third-party products you choose. Here are the most vital criteria to make your data integration successful...

Ensure that Data is of Good Quality

With the evaluation of Big Data, data quality has become a major concern in data-driven organizations. Any data integration task can be negotiated by bad quality data. Keeping it straight, if you keep the trash at one end, you will get nothing but trash at the other end also. Data integration projects without a company-broad strategy on data quality before, during, and after the data integration implementation process will certainly fail. 

Good data quality is the only thing that will guarantee user-adoption and accordingly, the success of your data integration project. If you provide your users with poor quality data, they will begin to doubt the data existing in the system and will start using the old, idle processes. A successful data integration project should always have a dedicated data quality range.

Consider the Impact of System Customization

Even though, today, many systems and applications bring you an array of custom functionalities, many implementation projects contain additional customization and development practices to support enterprise-level, departmental or user-specific working processes and behavior. This process can result in numerous custom modules or capabilities, but it is also quite a challenge when it comes to integrating different systems.

Data Integration services

Opt For a Consolidated Approach

When you adopt a data integration approach as a multitude of end-to-end custom integration scripts, without a general direction, then your data integration plan is considered to fail in delivering the required critical unified view of business data. Data must be coordinated in an automated and dependable manner across multiple platforms for a company to get a single version of the truth. Errors created by inconsistent data and manual data entry can prove to be very expensive for the organizations and interrupt business activities.

Take Future Upgrades into Considerations

Many ERP or CRM providers have developed an onetime integration between the systems for their consumers. Some organizations have already implemented this process for themselves. Although this might appear like a great idea initially as they have a good understanding of the complete processes and data models in the company, it can prove to be an error in the long term. Why? Because these integration solutions are not actually developed as a complete long-run project with future considerations.

So, what will the result be of upgrading the integrated systems? What will happen if you want to expand the use of your integration tools and integrate with other systems? When you select your data integration solution, always ensure that it is long-lasting, and you can keep using it when the integration collection changes. Personalized interfaces typically require development, which reduces the flexibility of the upgrades and makes maintenance more expensive.

Choose Top Management Support

Data management can be a challenging concern, some departments might consider that they own the data in their system and therefore be hesitant to allow other systems to access what they think to be their important information. Here is where wide executive support will help you. Although IT plays the most important part of your data integration project, it would be a big mistake if you do not involve more of your managers and executives.

Executive-level drives bring cooperation between data owners, user adoption, and are actually very important. Why? Because the data integration project you are implementing will not only affect your IT team but also have a broader impact on your overall organization. Don’t forget that a data integration project is all about sharing data and automating various processes. The best CRM-ERP integration projects cannot only be successful if they involve a CIO or IT director, but it also needs to include CEO-level support and participation of top management from the Sales and Marketing teams.

Data Integration

How is data integration implemented?

A diverse number of methods, manual and automated both, have been used for data integration earlier. A lot of data integration tools today utilize some form of the ETL (extract, transform and load) method. As the name suggests, ETL works by taking out the data from its host environment, converting it into some consistent format and then loading it into a target system to be used by applications operating on that system. The step of transformation generally includes a cleansing process that is executed to correct errors and insufficiencies in the data prior to its loading into the target system. 

Various types of data integration tools are available out there, comprising master data management, data governance, data cleansing, data catalogs, data modeling and other tools that have a number of data integration features. Here are some of the generally used data solutions that businesses need to understand:

ETL Tools- As explained above, these tools extract data from one application or system, transform it into a fresh format, and then load it into the new application.

APIsIt refers to Application Programming Interface, which provides a programmatic approach to one application for sharing data with another. 

Data Integration Platforms- It includes a broad range of diverse features, like ETL, ELT, data governance, data quality, data security, etc. These tools can incorporate data from an extensive variety of different sources and are appropriate for use by business users.

Integration Platform as a Service (iPaaS) It offers cloud-based tools for the data integration process. They generally provide very effective ease of use features and the ability to integrate data from cloud-based sources, such as software as a service (SaaS).

Data Migration Services – They tend to migrate data from one place to another and may provide some limited features for data transformations as well. Most of the major cloud service providers present migration services for shifting data to the cloud.

Want more? There is so much more you need to know as a business user. As you have read, handling a data integration project is not as easy as it seems, so you will always require the guidance of data specialists who are experienced in handling such projects easily. ExistBI have experienced Data Integration Consultants based in the United States, United Kingdom and Europe.  Contact us today to support your data integration project.


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6 Top Challenges in Data Integration Services You Shouldn’t Ignore

Data integration presents a wide range of important information that allows your business to deploy new modern services, but data integration services won’t be successful without overcoming several challenges. Your data can hinder your business intelligence, analytics, and modernization techniques if you don’t work with the right attitude, tools, or strategy. So what will be the result? An inactive organization that lags behind its competitors and fails to satisfy the client’s demands. Let’s discuss the top 6 challenges in data integration services:

Challenges in Data Integration Services

Well, firstly, you need to understand what data integration challenges are and what steps you should follow to avoid them.

What Challenges in Data Integration Services Are There?

A data integration challenge can be anything preventing you from attaining control over the processes and outcome of your data integration. It’s one of the major obstacles in your way of receiving a single, unified vision of your data.

Data integration includes capturing data from different sources and combining it to generate a single, unified view of complete data. This merged data makes it easier to depict insights from your existing data is processed to deliver faster, more impactful business growth.

Trying to ignore challenges in data integration services can cost you significantly; therefore, overcoming the top data integration challenges is imperative when you’re processing data integration at scale and maturing your data strategy.

What are the Top Data Integration Challenges You Shouldn’t Ignore?

When you have got a complete overview of what data integration challenges can be ahead, it’s time to explore some more specific common examples. Below are the six challenges your business can encounter while implementing data integration services alongside, possible solutions.

Incorrect Data Availability

You want to keep your data at one centralized place but, you are putting effort into its execution. This data integration challenge is generally an outcome of relying on human resources alone. It takes developers time to collect data from various sources and combine them manually. Here, your organization has to spend time evaluating data insights and execute valuable business best practices.

So, it will be better if you trim down the middleman and consider the assistance of a smart data integration tool and accelerate your innovation objectives. In this way, most of the heavy work is managed for you. Opting for an automatic data integration platform is a great approach to solve your data integration concerns.

Latency in Data Collection

A few processes need immediate and real-time data collection. For example, if you’re a retailer owning an e-commerce business site, it would be preferable to display customized, targeted ads to every customer based on their search history.

But, if your data isn’t gathered at the required time, you won’t be able to meet these demands. Regrettably, depending on your team to assemble data manually in real-time, is clearly impossible. Probably, you don’t have the right resources or manual power to embark on such a hectic task. If you want to drive real-time data ingestion, your only effective approach is the acquisition of a proficient data integration tool.

Data Integration services

Incorrect Data Format

Irregular data sets that are jumbled or do not exist in the correct format will not be actionable and therefore will lose their value. While manual formatting, validating, and correcting data is common, it requires a lot of your developer’s precious time. Data transformation tools remove these concerns by analyzing the original basic language, finding out the correct formatting language, and automatically building the change. This process eliminates the stress out of data integration and restricts the number of errors, particularly when your data team can identify and examine code at any end in the transformation pipeline.

Poor Data Quality

Poor quality of data in your organization can lead to a loss in revenue, missing important insights, and damage to the reputation. That’s why data quality management is a necessary component for empowering modernization, keeping compliance, and driving more precise business decisions. And it is not as tough as you might be thinking.

You can restrict and reduce the amount of bad data flowing into your systems by dynamically validating your data as early as it’s ingested. Above this, you can also examine your data pipelines for outliers and identify errors automatically before they create bigger issues.

Data Duplication

It’s estimated that more than 92 % of businesses have duplicate data in their systems. At first, the existence of duplicates in systems may seem harmless, but they can create severe long time concerns. So, the greater number of duplicates you have, the bigger the risk to your business.

Usually, these duplicates are the outcome of a ‘silo mentality’ issue. The duplication and unnecessary variations become normal in the data integration pipelines if the employees don’t share data and correspond with each other effectively. To restrict the creation of duplicates and eliminate data silos:

  • Create a data-sharing culture in your organization and spend time in training colleagues
  • Standardize data after validation and make sure that everyone understands it
  • Invest in technology that helps in team collaboration
  • Keep regulatory reports that encourage transparency and keep an eye on data lineage
Data Integration Services

Lack of Understanding

The communication between technical and business teams regarding data sharing plays an important role in data integration. But setting up a general vocabulary of data definitions and permissions is equally essential.

You can create a common understanding of data among the users through:

Data governance- This process focuses on the procedures, rules, and regulations that are covering your data strategy.

Data stewardship- A data steward is the person who supervises and coordinates your strategies, executes policies, and aligns the IT department with the business strategists.

Without a managed executing plan and clear ownership of your data, you will continuously struggle during the integration processes.

Defeating the Data Integration Challenges!

Today, the quantity of data generated by businesses each day is growing rapidly and has a critical impact on organizational success. However, until you overcome these six top data integration challenges, you won’t be able to make the most out of your applications, activities, and processes. With professional Data Integration Services, you can get things right by empowering an automatic data integration platform to use data as a keystone for accelerating the business transformation and ensuring the growth and development of your organization.

ExistBI offers Data Integration Service throughout the United States, United Kingdom, and Europe.


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5 Reasons to Hire Data Integration Consultants for Business Success

The IT industry has become enormous and companies have transformed the way they handle and manage their data. Now, data integration is not done by using specific tools optimized to tackle particular data. But the organizations are deploying a lot of tools from different vendors to access and recover business intelligence spread across a number of databases and applications. As data integration plays an important role in fueling business growth, it is vital to understand the reasons to hire Data Integration Consultants to manage their data needs.

reasons to hire data integration consultants


Merging of data flowing from different data sources is done to form it into meaningful and actionable insights that can be used for technical and business processes, this is known as data integration. Data integration is now considered as a significant approach to improve the accessibility, suitability, and quality of mission-critical data in an organization. Consultants help you through the complete implementation process, from understanding, selecting, implementing, cleansing, monitoring, transforming, to delivering consistently reliable data, and governed information in real-time.

What are the steps involved in a data integration project?

There are three steps involved in a data integration project:

i. Accessing Data

The data is accessed from all sources and places, whether it is available on-premises, provided by related partners, in the cloud or a mixture of both.

ii. Integrating Data

The accessed data is integrated so that information from one data source can be withdrawn into another. This sort of data preparation is necessary to make analytics or other applications able to utilize data successfully.

iii. Delivering Data

The integrated data is delivered to the business as and when it is required. Data can be delivered in batches, close to real-time or in real-time.

Data Integration Consultants

Why should you choose data integration for your business?

1. EASY AND FAST CONNECTIONS

Generally, building connections have been a hectic job that could take months. End-to-end manually coded integrations are not only prolonged but also unsafe. When you try to make more than three connections, it can be difficult to adjust them. Even applying small changes in one of the integrations can develop errors or a virus in the others.

In iPaas architecture of modern businesses, data access is easy and fast due to in-built adapters and connectors that can be imitated easily.

2. AVAILABILITY OF THE DATA

It is not easy to manage data siloes and batch processes. You must keep the existing data of all the right stakeholders in one place and it should be made available in real-time. That’s why it’s necessary to join all the data sources quickly to get the necessary information in a single location faster.

While it is enough for some industries to transfer data once or twice times a day, many industries are faster than that, and you must be able to make data available in real-time.

Data Integration Consultants

3. INTEGRATE DATA FROM MULTIPLE SOURCES

In an organization, you use a number of applications, systems, and data warehouses. As long as these data sources are distinct and siloed, it is hard to make existing data meaningful. For better cooperation, it is important to join all the dissimilar data sources with one another to make out the value of insights. When the right information is made available on a single location in real-time for all the stakeholders, you can utilize the information to improve the processes and provide superior customer service.

So, here is the need for data integration becomes very important. Generally, B2B integrations are very complicated and need integration experts to manage data. Sometimes, hundreds of data sources need to be integrated. The thing that is more difficult is that some data sources are on-premise and others are in the cloud, so there can be several firewalls, protocols, and data formats.

With an efficient data integration tool under the assistance of the right consultant will help you combine all data sources proficiently.

4. BETTER INSIGHTS BRING IMPROVEMENTS

Once you get the all data in one place, you can finally make use of the available information. You can utilize the raw information, or your data analysts can formulate insights from the existing information. Whether you can use these insights or some data tools that need to be deployed on the information and the result will be positive. There will be better intelligence on your processes and customers and you can make better decisions according to the available data and directly improve your processes.

Generally, there is a hidden value in every sort of data. The businesses that realize it early and unlock the unseen information in the data can considerably take advantage as compared to other competitions.

5. BETTER COLLABORATION

When you need to make internal collaboration better with your trading associates, you can only make this possible by integrating data. Fortunately, you can get amazing benefits of automating the flow of information on the way how you handle your business.

It’s critical to provide relevant data to significant stakeholders. This way, they can view better insights, and implementing data integration can automate a few processes that have been handled manually before. Whether you do internal or external integration, your employees and partners can make better collaborate, because they will get more information at their end.

Closing Words

As you are already aware, data is a valuable asset in any business, either you can make the most out of it by handling the data safely and securely or you may lose an important deal by disclosing important details. Therefore, it is vital to combine all data to form actionable and meaningful insights. More than these benefits, you get improved data quality that increases your competitiveness in the market.

Build a data integration strategy for your business and plan what measures you should take to improve the accessibility of data both in external and internal processes. The overall objective is to create more profit by utilizing the power of data. One of the most important parts of the business is attracting customers, so offering superior services than your competitors shouldn’t be ignored. If you are planning to strategize your business processes, contact Data Integration Consultants.  ExistBI have experienced teams within the United States, United Kingdom, and Europe.


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How Organizations Can Ensure Success with Data Governance Consulting?

The quantity of data existing in the world today is truly remarkable. According to the estimation of the World Economic Forum, 463 exabytes of data will be generated worldwide daily by the year 2025.  Data is no longer considered as just a result of running an organization—now it has become the lifeblood of every operation of any business. Today, businesses depend on high-quality data to function and be successful. The right Data Governance Consulting team will help you achieve high-quality data from multiple sources in various structures to be analyzed to cultivate success. In this blog, let’s discuss how organizations can ensure success with data governance consulting…

A powerful CRM (Custom Relationship Management) comprising of clean, correct data enables companies to create stronger customer relationships, provide smooth customer experiences, and formulate more efficient sales and marketing campaigns. It can assist you in discovering important insights and encourage the growth of new products and services. But the most important factor that leads to success is to make certain the data is available to the people who need it, at the right time, in a format that is accessible. That is exactly where the need for data governance arises.

Data quality vs Data Governance

What is Data Governance?

Data Governance includes all the people, processes, and technology that have been deployed by an organization to manage its own data. You need to establish the standards for data that fulfill the custom needs of the organization and its processes. Although security and governance are two different scenarios, still a comprehensive data governance policy should conduct a security check. This type of security checks ensures that the correct people have suitable access to the right data that is compliant with rules and storage.

A Data Governance policy needs to fulfill numerous grounds to become efficient and successful. For each data set, define the following:

  1. Where is the data residing?
  2. To whom is the data available?
  3. What is the structure of the data?
  4. How accurately the important terms and articles within the data are defined?
  5. What are the expectations of data quality in the organization?
  6. What is the usage of that data in the organization?
  7. What process should be followed by the data to meet these objectives?

Actually, finding answers to these questions is not a simple task. So, what’s the option for the organizations to set up and deploy an efficient governance policy? Here are a few vital points you should consider while implementing a data governance strategy.

Success With Data Governance Consulting

Engage the Right People

At first look, people often think that Data Governance is a job only for IT. But actually, Data Governance reaches far beyond the capacities of an IT team. The IT team is important for fulfilling all the technical concerns of data management, however, the whole company must work together to identify the day-to-day processes to build a broad and effective strategy for delivering data to the people who need it.

A data governance strategy should be agreed throughout the organizational structure, including sales, marketing, tech support, product development, legal, management, compliance, finance, and also the IT people. This way, you can ensure that multiple perspectives and priorities within a large enterprise are reasonably characterized and make Data Governance policies and procedures to fulfill the needs of all departments across the company.

Don’t Just Focus on Processes

Particularly, the ultimate objective of Data Management and Governance should be to make data access and use easier as much as possible by generating rational, meaningful standards and processes that the data users can follow easily. Processes need control, and control is entirely significant when it is a matter of data.

The practice of generating those standards, processes, and policies can be pretty complicated. You need to all members of the organization to understand that Data Governance is a tool to make more effective business decisions, not to create hurdles in the workflow. Stay away from going deeper into a lengthy process-building and follow the below tips to ensure your Data Governance efforts give the desired outcome:

  1. Firstly focus on the key objectives for the business and then move further to create more defined goals.
  2. Choose the right people for the task, then generate the right processes and define the most appropriate technology needs.
  3. Create clear objectives and evaluate your progress.  Ensure they are measurable.
  4. Characterize roles and responsibilities, so everybody is aware of why they are included in the process and what is expected from them.
  5. Make the processes simpler and automate wherever possible.
  6. Keep in mind that effective Data Governance is a continuous process.
Data Governance Consulting

Customize the Technology to Suit Your Needs

The exploration for suitable technology solutions gets less intimidating when you have a clear vision of goals that you need to extract from your data and how the established team is going to interact with this data. Once the set up is done, your Data Governance policy will serve as a roadmap to find the right technology.

When you start analyzing some prospective solutions, you might notice that some components of your data policies and processes need to be modified. Modifying the technologies to meet your need is normal today. Start tailoring your processes and technologies to interrelate with each other to meet your stated goals.

It is doubtful that you’ll find a tool that matches all of your needs therefore, your policy should include the strategic implementation of a centralized solution that can be integrated with third-party tools. There are a few key functionalities you should consider in governance plan:

  • Data Import
  • Data Verification
  • Deduplication
  • Data Reporting and Analytics
  • Data Operations
  • Data Maintenance
  • Data Security

Closing Words

Similar to the emerging technologies like automatic vehicles that rely on exact data for optimal performance, the success of operations in the businesses also depends on the quality data and effective Data Governance. Once organizations identify this inbuilt value of the existing data, they can start taking the steps necessary for making the most out of their data investments. With data Governance Consulting, you can get help in employing the right people, processes, and technologies in place to ensure that the right data assets are available at the right time. ExistBI offers experienced Data Governance consulting in the United States, United Kingdom, and Europe.


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Understanding Use Cases and Benefits of Data Migration Services

What’s the first thing that you consider when planning a data migration project? It should be the design of a successful strategy for migration. The various stipulations presented available from data migration service have made it possible for users to easily upgrade their operations. Here are a few important things that are proving helpful for users and also for web hosts – benefits of data migration services:

Data Migration Services

What are the Benefits of Data Migration Services?

The transfer of databases to all new environments is a trending process in the world of big data and data analytics, however, the solution is not appropriate or possible for everyone. Making decisions quickly will not be to your advantage, as firstly you have to know all the potential approaches to take full advantage. Here are the following benefits of data migration;

  1. It upgrades existing applications and services dealing with data within your organization.
  2. It helps you to scale your resources to meet the growing needs of increasing business data.
  3. It boosts competence and efficiency while maintaining overall IT operating costs as low as possible.
  4. The professional services offer a pay-as-you-go model.

Data migration projects have three major categories that are;

  1. Host-based software, which is suitable for replication (Copying files or any other upgrades of platform)
  2. Array-based software, which deals with the data that is migrating between two similar systems
  3. Network-based appliances, which helps in transferring the computer volumes, files, or blocks of data

When you start migrating the database, ensure you analyze the complete concept, and find out what’s right for you.

What use cases you should look for within data migration services?

Issue Description

Several companies, especially the larger organizations, have devoted a great amount of time and investment in recent years to discontinue explosion in their IT environments and update their environments simultaneously. Therefore, centralization and standardization are the key main factors here.

Consequences

For migrating multiple diverse legacy systems to a new modernized and centralized environment, you should seriously consider data migration services. The related workload for larger organizations rapidly scales up to maximum numbers.

Impact

Other than the elevated costs and long-term projects, these types of broad data migration projects also have other drawbacks to the loss of the companies’ policies and innovation. At the same time, they can put at risk conformity with important limits.

Solution

Data migration is perfect when it reduces the quantity of data to be moved and provides it in the best quality for use. This is made possible with a contemporary, centralized, and neutral platform for managing the information. The solution to these types of challenges is system-based independent management for the complete life cycle of legacy data and documents.

Data Staging Area

Data and its quality can be evaluated and optimized at the data staging area with the use of duplicate cleansing, enhancement from others, business-applicable sources, and management. This area is essentially important not for migration projects only, but also for every rapid business use case such as mergers and acquisitions, digital transformation, and the resultant digital business models and operations.

Technical Assistance

The professional services allow you to manage the data and documents that are no longer required for various operations. It includes their business strategies across their complete life cycle, from their movement, from the production systems to legally appropriate storage and closing points.

The most important part of assistance, however, is that past data residual will always be accessible, which enables the business users of the company to migrate that specific part of the data from the target systems only when they need to in their daily business activities and operations, such as post orders.

Benefits of Data Migration Services

Identify

Once the complete database has been moved from the legacy systems to the new environment, you should start analysis to find which data the customers should need, which they don’t need and why this data is important. A specific criterion for Data Reduction Potential Analysis (DRPA) would comprise of various units in the organization, different master data, and types of transaction data or some definite business objects. The result of DRPA takes a form of reports for management, whitelists or blacklists, which states the specific areas and fields in tables that are moved and no longer required there.

Design

After clarifying the operational and historical information of the organization, the next step to be followed is detailed planning for data selection and migration in the design stage. The selection criteria from the identification stage are then additionally refined and tested so that the alternations in the data store should be done by software automatically.

Transform

After the design phase, the service providers should set up accurate and predefined filter rules such as blacklists and whitelists, which enables the customers to choose the tools which they want to utilize for transformation and migrating information. On the other hand, the provider can transfer the comprehensive data package to their own Extraction, Transformation, and Loading (ETL) solutions.

Conclusion

Modern generations of software are instruments and drivers of digital transformation in various organizations. However, their success depends importantly on the quality of the business data available there. The Analysis made on the basis of incorrect or incomplete data directs to inaccurate results and ultimately to wrong strategies and actions being taken. On the other hand, the business that is using digitization to cleanse and constantly keep its data maintained, staying ahead of the competition.

Web hosts must ensure that their migration service serves as an enabling point, not an obstacle for all the customers. They should promote service efficiencies like convenient demands and make sure to execute the migration quickly. There are different approaches available out there to proceed further towards the data transfer, including planning, analyzing, and strategizing the project. However, you would need an ideal infrastructure that can endorse the seamless functioning of various processes.

Therefore, if the service provider plans to proceed with its data migration services with all the essential features and use-cases, it can let more users drive into the process while also develop you as an impending competitor. Contact ExistBI for data migration support, with specialists in the United States, United Kingdom, and Europe.


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4 Facts You Need to Know About Data Governance

Data governance is the framework that guides the policies and processes involving data and its use in the organization. It is a fairly new term that gained popularity through the government implementation of data privacy laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), both enacted in 2018.

From the term governance, it highlights taking control of data and how it moves and is used within the organization. It is about taking control and ownership of your data as an asset and how it can be used to attain corporate goals. So let’s get into the 4 facts you need to know about data governance

About Data Governance

1. Difference between Data Governance and Data Management

The concept of Data Governance and Data Management may sound similar but they refer to different things. You can think about Data Management as the umbrella term that refers to the specific detailed programs that an organization handles the data it produces, receives, and stores. It is mostly focused on handling data from the Information Technology (IT) perspective and practice.

On the other hand, This is the blueprint that lays these programs down and ensures that they are in line with regulations within the organization and the law and considered a part of the broader data management strategy. It is focused on handling data from the Corporate Management perspective, as a business strategy.

Both are very important concepts for today’s organizations, as it sets the tone for the critical management of data in today’s economy. It is not just an issue for the IT department but should be a concern across the organization – whether you’re in Sales, Marketing, Human Resources, or an Executive, you need to know about how data is being handled in your organization.

Data Governance and Data Management

2. Benefits of Data Governance

Business Intelligence is a huge market. According to a report by Grand View Research, the industry was valued at US $24.9 Billion in 2018 and will have an expected annual growth rate of 10.1% from 2019 to 2025. Many companies have made the strategic implementation of data governance in their day-to-day operations.

As a result of these investments, plus getting the right data governance consulting as they roll out changes, companies have been able to get the following benefits:

  • Ease of compliance with internal and external regulations

Many companies have created the positions of Chief Data Officer or Chief Information Officer as a response to the growing focus on data. Since GDPR was rolled out, any companies doing business with Europe needed to have this representative. Dedicating a person in this role can help manage the task of maintaining data governance in the company.

  • Protecting data from breaches

With so many large corporations becoming vulnerable to data breaches, such as leaking information about their employees and customers, securing the system is critical. This is a hit towards reputation and in turn, revenue. Putting up the proper security measures is a key activity.

  • Standardizing the data architecture across your organization

Connected to the protection of data, having all employees from top to bottom understand the importance of data security and having a standardized process to receive, store, and process data will help support the initiative.

  • Ensuring data quality and accessibility for everyone in the company

Helping the whole company understand the data infrastructure will make them invest their efforts to maintain the data integrity for their own activities because they can see how it affects their colleagues and the company’s bottom line.

  • Improving transparent about data within and outside the company

Outlining the sources and destinations of relevant data helps you trace where any aberrations and mistakes may have occurred, allowing you to address and mitigate any possible breaches.

  • Creating smoother analysis and reporting processes

As part of business intelligence strategies, implementing dashboards and enterprise-wide software that can analyze data without the need for specialists. This returns quantitative results that can help explain any inconsistencies and point your company to the direction of better strategies.

  • Cutting costs and increasing revenue through higher efficiency

Proper data governance helps you use the information more efficiently. Because you invested in the right tools, your reports and data analysis are more reliable and help you make better decisions with your business.

3. Data Governance Challenges

While Data Governance has been around for some time, businesses still find it a challenge to implement these frameworks fully.

  • Lack of understanding about the importance of data

The concept of data has been understood as the domain and responsibility of the IT department. While this is no longer the case, you may have to explain to your other departments that keeping data integrity is in everyone’s best interest.

  • Investment and rolling out enterprise-wide tools

There are a lot of business intelligence tools out in the market, and there are popular options and support systems that you can rely on to have a smooth user experience. Of course, this still requires money and effort to install.

4. Does My Business Need Data Governance Consulting?

Given the challenges that surround the implementation of these frameworks, it can be difficult to get things running for your organization. However, getting around to these changes is not a question of if, but when. Soon, to comply with regulation and survive in a technology-reliant ecosystem, you will need these systems in place.

Data Governance Consulting

All businesses can benefit from a sound data governance strategy, but of course, your needs may be at a different scale from other companies. This is where data governance consulting can help you. If you aren’t sure where establishing authority over your data starts, there are several experts on the field that can help guide you through the process. Your company will be equipped with the right technology that your business needs without the danger of over-investing.

Final Thoughts

Investing in Data Governance consulting is going to ease the transition. You will not only address the technical questions but the human challenges that will arise in the process as well. Choosing this direction will move you forward as a progressive organization, ready to exist and thrive in the new generation of business.


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Planning to Migrate? Avoid Common Pitfalls with Our Data Migration Tips

Data migration is the process of transporting data from one place to another. While it is easy to understand the concept of data migration, it is a tough task to implement the process of data migration. In fact, data migration services are one of the most complex tasks in the field of data engineering. Please check our Data Migration Tips below…

Data Migration

What are the most common use cases of data migration?

Before getting into Data migration tips, let’s know three common use cases for data migration:

(a ) Application migration

(b) Storage migration

(c) Cloud migration

Application Migration:

It is the transfer of an application from one storage or server location to another one. Either you can migrate an application from an onsite server to a cloud-based server, a cloud-based server to another cloud-based server, or shift data from one application to a new application, which only acknowledges data in a particular format.

Storage Migration:

It is the migration of data from legacy storage systems that are isolated and have become walled-off into data silos to storage systems that allow improved integration throughout all the information systems belonging to a business. Transferring data into a more integrated data warehousing system provides considerably better processing, flexibility, and economical scaling. It might also offer advanced data management capabilities such as snapshots, cloning, disaster recovery, backups, and more.

Cloud Migration:

Cloud migration is the process of moving data from onsite servers or on-premises servers to a cloud-based data warehouse. This is the most important element for large organizational data systems right now. According to Forbes reports, 83% of businesses will be transferring their data systems to the cloud by 2020.

Data Migration Tips

Concerns That Can Lead to Delay of Data Migration

There are various steps an organization can take to finish a data migration process effectively. 

Conduct a migration impact assessment to analyze the levels of data quality and the probable cost of project delays. It defines the approach to be used for migration, creating a timeline and evaluating each level of the process. Additionally, it is essential to understand how to solve some of the most common challenges in data migration.

All data migration processes are different and regardless of the fact that projects will differ according to their scope, time limit, type of migrating database, and other significant circumstances; there are three major concerns that can delay the process of migration.

Insufficient Planning for Data Preparation

Data migration is not the same as copying information, so transferring data to a particular cloud storage solution requires good preparation. The time allowed for it must be measured in the data migration plan and the budget as well. If you ignore this step, you may lose the chance to filter out redundant data, like backups, old versions, or draft files that are often available in data sets that would not be required in the cloud workflow. The key is to discover an automated approach to choose what data will be moved and then save the important records without overlooking that various cloud workflows may need the data in a diverse format or enterprise than on-premises applications.

Lack of Data Integrity Assessment and Protection

Data validation is a vital step and also the easiest to however, it should not be assumed on the basis of thoughts and opinions but on confirmed facts. There is a valid concern that unauthorized access can occur during data transfer. It is the preparation and transfer of the data where the information is most vulnerable to lose or hacking.

Underestimating Cloud Scaling

Once the data reaches its target location in the cloud, the process of data migration is in the intermediate stage, the project is only halfway there. You have to make sure that the data transferred is true to the existing data source. Verifying that it can make storage cache layers complex. After the sent data has been verified, it is essential to extract, reformat, and dispense it so that it becomes ready to use by cloud-based applications and services.

Data Migration consulting

Approaches to a Potential Journey to the Cloud

As organizations are adopting data analytics services and applications in the Cloud, the intricacy of their data management grows. As the Cloud itself has numerous environments and applications within its hybrid ecosystem.

Types of Hybrid Approaches

To compete in this multi-cloud environment, organizations require an end-to-end hybrid data management platform, enabling them to provide business data rapidly and safely in the cloud, hybrid platform, and on-premises ecosystems. It can be made possible based on various approaches:

 • Simple hybrid integration- The most suitable for companies searching for a platform that supports them with integrating all of their cloud-based SaaS applications with all available local data to get a holistic view. For simple hybrid integration, an Integration Platform as a Service (iPaaS) would fulfill the needs to integrate applications, data, and processes in the cloud, hybrid and on-premises environments. The approach works well when companies are starting with a similar approach to integrate cloud applications and data sources.

 • Advanced hybrid approach– As the organization grows, the intricacy around data management also increases, not only from the data sources or data volume but also related to new use cases. It needs a more developed platform to manage growing complexity, an advanced hybrid integration form. Businesses longing to see their requirements fulfilled should shift to next-generation iPaaS, which are exclusive, modular, metadata-based platforms, integrating big data, cloud, and on-premises systems. It even conducts advanced integration use cases, such as the Internet of Things and other compound data management solutions for business and IT users both.

Conclusion

The latest cloud, big data, and IoT technologies can be overwhelming. To take advantage of all these technologies, we must learn, modify our processes, and adapt our approach to data. This way, you can manage to master the complicacy and leverage the benefits. Migrating to the cloud is not only a matter of data but also of processes, this is vitally important when planning to conduct any data migration project.

If you want to avoid challenges, to ensure the final cloud infrastructure supports the required workflow, follow these data migration tips, if possible hire professional Data Migration Services to get expert assistance to complete your project. ExistBI has specialist teams in the United States, the United Kingdom, and Europe.


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5 Business Requirements to Fulfill with Right Data Warehouse Consulting

According to a recent report by Allied Market Research, the global market for data warehousing is expected to rise to $34.7 billion by 2025, which is almost double its worth of $18.6 billion in 2017. What fulfills the needs from investment in data warehouse development in an organization?

The role of innovative applications and practices in an enterprise has increased the need for Cloud data warehouse technology, which boosts the efficiency and lessen the costs across company functions. Today, various departments like marketing, finance, and supply chain operations take advantage of modern Data Warehouse Consulting as much as the engineering and data science teams do.

Data Warehouse Development

Types of Data Warehouse

There are three main types of data warehouses that the users have been using worldwide, which are:

  1. Enterprise Data Warehouse
  2. Operational Data Store
  3. Data Mart

Why is Data Warehouse Development Necessary?

Here, explore the list of five business needs that can be fulfilled with bigger investments in modern enterprise data warehouse development.

1. Need to Access and Act on Data in Real-Time

Nowadays, businesses can do data processing and detecting signals in real-time that had much higher latency in traditional systems. Identifying the stock levels at retail stores, for example, lets a retailer respond to customer trends and solve key concerns before they negatively impact the business. Superior yet, by merging a real-time vision of supply chain data and weather, the retailer can restock stores running vacant before it goes empty.

Modern data warehouses make data visibly understandable, meaningful, and actionable in real-time by implementing an extract-load-transform (ELT) method over the single omnipresent extract-transform-load (ETL) model, in which the cleaning, transformation, or enrichment of data on an external server before loading is done into the data warehouse. With an ELT approach to working, raw data is explored, drawn and analyzed from its source and loaded, comparatively unchanged, into the data warehouse, enabling it to be a lot faster to use and analyze.

data warehouse ELT approach

2. Search for a Holistic View of the Customer

In the past, the information existing in a company about its customers was collected in siloes. The data from one source would be stored in a data silo, and data from another source is saved in a data lake or stored in an on-premises traditional system. Without a simpler approach to connecting the dots, it was complicated to make sure that high-value customers were getting the best experience that’s possible.

The assurance of a data lake strategy is that complete information of your company, whether it is structured, semi-structured, or similar to raw data, can be rapidly and easily queried from a single place. With this approach, a data warehouse for an enterprise can facilitate an absolute view of the customer, supporting to improve campaign performance, reduce churn, and eventually, to develop revenue. An enterprise data warehouse also enables predictive analytics, where teams use situation modeling and data-driven predictions to inform marketing and other business decisions.

3. Recognizing Data Lineage to Ensure Regulatory Compliance

In huge organizations, it becomes tough to discover the origin of specific data. This situation can give rise to problems, particularly for the finance and accounting department, when they conduct audits. Traditionally, the only recourse they have to file is a support request that can be expensive and slow. A modern enterprise data warehouse allows its data customers to audit and examine data sources directly and locate errors rapidly.

You can also implement compliance through the General Data Protection Regulation (GDPR) presented by the EU with the use of a modern data warehouse. When you don’t have a data warehouse in your system, it is likely that your company would have to set up a difficult process to fulfill every GDPR request. This process would engross various functions or business components, looking for relevant PII data. Therefore, you’ll essentially have to search in only one place with a data warehouse.

Data Warehouse Consulting

4. Allowing Non-Technical People to Query Data Rapidly and Cheaply

Developing a data warehouse can also profit your non-technical personnel in job operations beyond finance, marketing and the supply chain. For example, architects and store designers can improve the experience of the customer within new stores by digging deep into data from IoT devices located in available locations to recognize which parts of the retail stores are most or least engaging. Global service managers can support their decision-making on whether to extend retailing outlets or move product lines on a powerful set of information that includes data regarding hiring and retention of employees, in addition to typical metrics like cost per square foot.

5. Need to Join Data Together into a Single Location

Nowadays, many data sets are simply too huge to move and query faster and in a cost-effective manner. To restrain expenses and latency, some companies use regional clouds. According to research, for companies that use a multi-cloud strategy, 81 percent of them end up with data spread across multiple platforms from contending cloud providers. Getting rid of these obstacles is the main concern for organizations that struggle to be data-driven.

Wrapping Up

With a modern data warehouse, users can integrate various data sources, applications, and departments together to combine all information in a single location, allowing authorized users access anywhere, anytime. Therefore, managers can leave the worries of maintaining the data on their own and making it available to all business users in real-time.

Data Warehouse technology has made things easier and allowed the organization to create a single storehouse for their data and providing a unified view of data to its users. In this way, the security and privacy of data are also ensured as only the legal users are allowed to access sensitive information, including important credentials, which are kept safe from hackers.

Top-class Data Warehousing Consulting will help you to understand how companies can store data across various regions and cloud providers and query it as an inclusive unified data set. Thinking of developing your enterprise data warehouse? Get advice from the data experts and make your data easily manageable and accessible! ExistBI offers Data Warehouse consulting services in the United States, the United Kingdom, and Europe.


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Connect to Your Partners with Informatica Cloud B2B Gateway

Digital transformation is now involved in every part of the business. Demands for round-the-clock and self-service have extended beyond consumers and impact business-to-business (B2B) companies as well. That’s why; the consumers who make the purchasing decisions have become familiar with the ease of digital interactions.

Whether they are at home or in the office, consumers want a good experience each and every time they connect to your business. People opt for Informatica Consulting to discover in-depth information about connecting to digital transformation with Informatica Cloud B2B Gateway.

Informatica Cloud B2B Gateway

Companies who work within traditional B2B models have previously focused purely on the price or products and have ignored the customers, missing out on the powerful human contact at various points of interaction. As per B2B Marketing, 96 percent of B2B buyers mold their decision to buy again based on the experience they had. And 83 percent of people who have had a good experience with your business will refer you to a friend. B2B companies have to make important connections at each touchpoint to improve and personalize the customer experience (CX).

Here are the basic five strategies that can help you to provide a better experience to your customers:

  • Focus on Customer Life Cycle
  • Be Good at Data
  • Broad Segmentation
  • Understand Your Customers
  • Anticipate, Predict and Act

Get Inspired to Make a Big Impact

B2B marketers who have the aim to be more customer-centric should rethink how they can effectively obtain new customers, cultivate relationships with existing customers, and create long-lasting loyalty. Start with implementing the above five strategies and adopt Informatica solutions for more effective results.

Ensure Savings with Informatica Cloud B2B Gateway

Moving further from customer experience, another vital aspect is managing your partner community. There is no doubt that customer experience will increase your sales, but managing your trading-partner community is also extremely important for your business. Simplifying integration with your trading partners can accelerate partner on-boarding, speed up data exchange, and decrease operational costs for all parties.

Integration with trading partners

Businesses looking for B2B solutions find trading-partner community management to be challenging, protracted and costly. B2B business users have the questions that:

  • How can I accelerate the partner on-boarding process?                                                    
  • How can I present additional control to my partners and enlarge my association with them?
  • How can I meet various service-level agreements (SLAs) for partners and owners?
  • How can I enlarge my community of partners to embrace smaller businesses?

The more you focus on these needs and challenges with customers, the more you’ll understand that meeting these objectives requires collaborating partners in a self-service mode. So how can you do that? With Informatica’s B2B partner portal!

Onboarding a new trading partner is an essential step in the partner-management life cycle, and businesses exert immense efforts and resources into modifying, simplifying and accelerating the process. A self-service-based, joint onboarding portal can considerably cut down that step and reduce operational costs.

When you get your trading partner onboard, you can then start your ongoing daily interactions. You can exchange multiple hundreds of files and messages, which you want to ensure, are processed successfully as per agreed-upon SLAs.

A B2B portal is a strategic tool that can help you find the answers for your needs and develop a smooth partner relationship.

Advanced Partner Community Management

This new self-service Informatica B2B Partners Portal allows you to simplify and accelerate the partner-on-boarding process. Informatica Cloud B2B Gateway, a component of partner community management service, helps to improve integration and collaboration among all trading partners.

Once you have set up the partner portal and given access to your partners, they can log in to track and verify the status of data exchanges by using the B2B event-tracking and monitoring system. Partners obtain a sophisticated view of the status through a dashboard. They can further explore the status of particular files and messages with the events screen. The administrator of your organization has the authority to control the portal and can brand it to your organization’s needs.

Informatica's Partners Portal
Informatica's Partners Portal

What’s next?

Making yourself able to connect with your partner community is a crucial task, which should always be put on high priority. But, what can you do when your partners do not have a suitable EDI or file-transfer solution at their services? Then how will you exchange files and messages with them? The upcoming release of Informatica’s Cloud B2B Gateway will come up with a solution for you, enabling your trading partners to use Partners Portal to send and receive files to and from your organization for their reference.

With a simple online login, they can access the Partners Portal, and feed the data and files for processing, and can also download the files that are waiting for them to collect. The process of sending and receiving files takes place on the basis of HTTPs for better security and to maintain the files on your premises’ domain. Informatica file servers take care of the management and set up of the HTTPs server.

Drummond AS2-Interoperability Certification

The new Informatica B2B Gateway Cloud AS2 was tested and certified by Drummond as a Suitable AS2 solution for its great compliance, security, and interoperability.

To Sum Up

Providing the best customer experience (CX), data integration, and collaborating with partners is the key to success in the B2B market. And Informatica has always put the best efforts to provide the best solutions for changing and improving the way you handle your business. When it comes to online business solutions, Informatica Cloud B2B Gateway is a single solution that will help you manage all the aspects of a B2B business. It helps you to modernize your data integration processes, enhance customer experience, and manage community partners.

Informatica’s Next-Generation Cloud B2B Gateway provides Informatica Intelligent Cloud Services that help the customers to expand their organization integration platform to the integration with all of their external business community partners.

For leveraging a software solution for your company, you must need thorough guidance and directions to help you identify and understand the needs of your business in the first place. Then you can select what and how to implement it in your business. Contact leading Informatica Consulting partner for comprehensive guidance throughout your digital transformation journey. ExistBI offers Informatica services in the United States, the United Kingdom, and Europe.


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3 Data Governance Strategies to Become Data-Driven: Tableau Bootcamp

Do you know which data governance strategies best fit your organization? One of the big decisions of your organization that you will make on your transformation to become data-driven is what type of governance you should put into practice for your data and content. Preferably, the governance model you choose should be secure, while also allowing your employees to utilize the available data to make improved decisions. In Tableau Bootcamp, you’ll come across three governance models that the Tableau blueprint represents.

Tableau Bootcamp

Here are the three types of governance models that are in real-time practice in different industries:

  • Centralized
  • Delegated
  • Self-Governing

Centralized

Generally, the IT department is only one central department that holds your entire data and controls the analytics environment. They categorize the data sources, collect data and reports, and make them accessible to your analysts and other business users across the organization. You can opt for a centralized data governance model because of the following reasons:

  1. Users can handle the project with fewer data literacy or skills in the entire organization
  2. The data needs are enormously sensitive and require profound control and monitoring over the users who have access
  3. You have an existing traditional top-down IT or data strategy that isn’t varying anytime presently

Here are some of the possible drawbacks of a centralized strategy:

  1. The owners get loaded up with access requests from throughout the business that results in lengthy procedures and time-consumption for business decisions or sometimes gets made without the correct information.
  2. The word never gets out to the rest of the business that the reports or data exist for business owners to use because they haven’t been engaged in preparing it. Your investment is never fully utilized.
  3. You can never embark upon the data and analytics skills-gap existing in your organization.
Data Governance Strategies

Delegated

In a delegated model, the possession and liability of the data are given to personnel outside of the central IT team, which hold positions of Site Managers or Project Leaders in Tableau Server for changing permissions. Delegated requires wide-ranging processes in a position to authenticate and verify data that is published. In a number of delegation models, it may come under the centralized team to verify finished content by the delegates.

There are the following reasons to have a delegated model of data governance in your organization:

  1. Data literacy is required in several areas, but still needs enhancements in other areas.
  2. Some of the data is sensitive and is still required to be handled responsibly by a central team only.
  3. Your organization is conducting a gradual transition towards self-governing or independence.
  4. You have to verify the user’s content earlier than certifying it as data expertise is still being constructed.
  5. Reporting and data requests are surpassing a centralized team’s ability to produce.

Here are some latent difficulties of a delegated strategy:

  1. You would need an inclusive method of certifying and validating data and content that is marked by users saying they understand the process.
  2. A special training scheme for users is necessary to allow them to generate good content. A little or no training will result in the generation of poor content or a combination of poor and good content, without any appropriate data literacy.
  3. Site Administrators or Project Leaders need to take training to make sure that they understand the tones of the roles in Tableau Server.
Tableau Bootcamp

Self-Governing

The various employees of the organization produce content and data regularly, either by the creators on the desktop or by explorers in web edit. Every user, including viewers, has some level of data-literacy. Ad-hoc or sandbox content vs. certified content is distinguished, and the procedure of endorsement to certification is apparent and definite. Analytical skills should be good throughout the organization among all business users.

There are the following reasons to have a self-governing model in your organization:

  1. Data literacy is good throughout the organization, and users need to be able to find answers to their own questions by using data.
  2. Require fast reporting to exceed the supply by a centralized team.
  3. Your company holds an open data policy in your organization where all employees are allowed to view most of the data sources, excluding sensitive data.

Here are a few shortcomings of a self-governing strategy:

  1. It is required to separately monitor your Tableau Server environment and its ability to scale up
  2. Generating custom admin reports on the basis of the Tableau Server data to track who has accessed what may be essential for directive requirements
  3. Needs suitable and frequent training for all users at all stages, either at a Creator, Explorer, Viewer, or Admin level

Conclusion

It is really important to understand the value and role of data that exists in your organization, and moreover, it is required to keep the sensitive data safe and protected from the hands of the viewers and users who can misuse the role of this data. Therefore, you should always startup by building a suitable data governance strategy for your organization that fits perfectly with the needs of your business.

For doing so, you need to have a complete understanding of your existing data and business needs. Then only you can evaluate and examine different types of models to implement in your organization. The three above explained strategies are mostly used by companies to tackle their data needs and manage them efficiently. Evaluate your needs and find the most suitable one for you.

Tableau puts tremendous effort into helping people to change the way they use data and provide a unified view of data on a single screen with a different range of dashboards. If you are not aware of all the policies and regulations that are associated with various governance models, you have to understand it’s the usage and practical implementation in a real-time world.

Join Tableau Bootcamp and get complete training to understand and analyze data effectively with a wide range of data dashboards. ExistBI offers a unique Tableau Bootcamps in the United States, the United Kingdom, and Europe.


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3 Common Challenges You Can Resolve with Data Integration Consultants

Challenges are inevitable, how you face them is your choice. In big data projects, you can rarely avoid a few challenges and obstacles to success. However, it is not impossible to tackle them, having an awareness of such obstructions as soon as possible enables you to defeat them. 

Similarly, in the world of data integration, a few challenges will inevitably come up along the way.  Having Data Integration Consultants on your team will help you to identify and understand what those barriers to success are and how you can overcome them to achieve results.

Data integration

Challenge 1 – Defining Data Integration

One of the major challenges of data integration is defining its significance. Data integration is often confused with business integration and system integration, however, they are very much separate entireties. Typically, data integration is a collection and integration of complete data sources from internal and external systems and devices in a separate data structure for the purpose of cleansing, managing, and analyzing the data. Data integration can take place in a data warehouse and requires dedicated software to handle large data repositories from internal and external sources. 

The software extracts data, merge various data assets, and then delivers the information in a unified form during this process. When you understand the right words for defining this process, you will find yourself one step closer to getting the results you want.

Challenge 2: Data Diversity

Another difficulty that can arise during the data integration process is that the information in the data sources is available in different forms. Traditional legacy systems hold data in different forms; however, a single data integration platform cannot deal with diversity. Therefore, it must all be in a similar form for data analysis.

You need to be aware of the heterogeneity of data formats at the initial stages to overcome this challenge. When you start your project, firstly evaluate your information to identify the variety of information you have. Then, convert the whole information into a similar format, so that the data integration platform can analyze it.

Challenge 3: Extracting Valuable Insights

A common hindrance that everyone confronts about data integration is its complexity to extract value from your data after the integration of a variety of other sources. It is not as simple as you think because there is a huge diversity in the data available out there and it is getting more complex and sizeable with the growth of the industry that captures information via sensors, mobile devices, and social media. Your data analytics tool must be efficient at connecting with the data integration platform seamlessly. 

Always remember one thing, when you are going to exploit data integration software; check whether it connects to your analytics tool or not. So if you make the right tech solution decisions, you can escape from various hindrances that can make your data useless.

Data integration can face several challenges during the implementation process if you don’t approach it the right way. You require knowledge and thorough planning for successful data integration. To guarantee success let the experts do their work and hire Data Integration Consultants to handle your data integration projects.

For more information contact ExistBI’s Data Integrations Consultants in your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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It’s Time to Clean Up Your Cognos Analytics Content Store with Cognos Training

Do you know what is available in the content store of Cognos Analytics? When planning to upgrade your Cognos Analytics, you should check whether your content store is performing well or not. The content store holds a massive amount of important information regarding Cognos report specifications, packages, report outputs, scheduling, data source connections, etc. Through your Cognos Training, you will discover how to cleanup Cognos Content Store is like a garage that continually receives increasing amounts of items that eventually need clearing out. 

Reason for Content Store Bloat

One of the biggest reasons for the overstuffing of the content store is the bloating of custom folders accessed by end-users. Self-service was a hallucination from a  few years back, and maximum end users didn’t produce their own content, but only ran specific reports. Today, IBM Cognos has become more advanced and user-friendly. However, the end-users continue to fail to utilize it thoroughly and create multiple versions of their own content, which results in content store bloat. So, if the size of the Cognos Analytics content store increases, it’s performance will also slow down and create unsteadiness.

Cognos enables full control of users over their content in a personal and public environment. If content retention rules are not followed, there is a huge possibility that content will stay un-used and start collecting in the Content Store. With the passage of time, this content will get congested and become unorganized to preserve a managed environment.

Here are the few side effects of the overflowed content store:

  • Slow performance
  • Degradation
  • Locking of the Content Store
  • Corrupt files, etc.

Clean Your Cognos Analytics Content Store

All Cognos admins should follow these few steps to avoid performance issues in Cognos Analytics Content Store:

  1. Turn on Cognos Analytics Auditing that will help you to know what data is in use constantly and what is not used for a long time.
  2. Identify orphaned content with thorough and routine checking of your website. This type of content stays in the system when a system user with a particular folder leaves the company. Then the user is eliminated from the security, but the existing content remains there and gets orphaned. So, for handling such concerns, either delete the content or transfer the data to another user. 
  3. For the various version created, produce definite guidelines. The results of any saved reports always remain in the database, so it is advised to keep minimum copies. You can ensure this by adjusting the report output version’s setting in its properties.
  4. To boost up the performance and trim down the size of the content store, IBM Cognos Content Archival enables you to save archived data in external storage.
  5. Cognos Admins get a Content Removal option to delete output versions in the Public Folder or Custom Folders.

Summing Up

Cognos Analytics has now come up with an automatic Content Store Cleanup Solution. Therefore, by using this solution, you can identify large and outdated content through various aspects, such as by pre-defined size, data that is not used for days or months, data that is not having any user, etc. So, if you require a smooth upgrade to the latest version of Cognos Analytics, shift to Cloud or following these best practices specified above. Cleaning of the Content Store is a routine task that you must execute on time to avoid performance issues.

 Join our Cognos Training to know more tips and tricks about keeping your content store clean and handy features in the latest upgrades of Cognos Analytics.

For more information about ExistBI’s IBM Cognos Training and Consulting Services, call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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5 Lightning Navigation Tips by Salesforce Consulting Partner

Salesforce lighting has empowered data managers in terms of providing a customized layout and experience suitable to match the specific business profile. Managers are able to change the layout with standard Lightning components, which imparts a unique Org experience and Salesforce Consulting Partner provides the users with multiple features to get a customized layout. In this blog, we are going to discuss 5 Salesforce lightning navigation tips by Salesforce consulting partner.

Let’s check out these 5 features of any Salesforce Lightning that makes it more dynamic.

Lightning UI Changes

These features can be used in lightning Org quickly. You can also create a combination comprising two or more features to create an experience for your specific needs. 

1. Favorites

Similar to the browser, you get a star icon at the top right of your screen that permits you to add your Favorite pages you want to access quickly. This feature not only allows adding documents and records but also implements Favorite Reports, Dashboards, List views, etc. When you need to work daily on any page and would need to access it frequently, press the star icon and add the page to your Favorites list. The Edit Favorites button can also be used to revise, delete or rename your favorites list.

2. Pinning List Views

You may have observed that Salesforce sets the default according to the ‘Recently Viewed’ list view. But with new lightning features, there is a solution. There is a new addition to every object in Salesforce that is a pin icon, right next to the list view.  To skip the list view, click on this pin icon whenever you visit an object’s home page.

3. Customized Navigation Bar

Lightning has also introduced a comprehensive customized navigation bar, with which you can monitor 

  • Sequence of tabs
  • Insert extra tabs
  • Add documents, records, dashboards, list Views, Reports, etc. 
  • Editing navigational items

With all these features, you have the ability to entirely modify the typical view of Salesforce you will use every day.

4. Density Settings

Salesforce has added a new feature last year, which lets you adjust the amount of data that you can view on your screen. So, if you have an Organization that generates a massive amount of data, which requires frequent scrutinizing, then this feature will improve efficiency. It comes with two options, Comfy or Compact. Compact provides a Classic experience having immense information available on your screen view.

5. Kanban View

Kanban’s view on the Opportunities object is a game-changer for Sales users, which enables them to get a logical view of their opportunities, not only in a simple data list view. This view is also applicable to other records influenced by any back-processes, such as Leads or Cases. Not just offering a different layout, a Kanban view also presents the following functions: 

  • Allows viewing the total amount of data in each step
  • Let’s drag and drop Opportunities to various stages
  • Notifies about the appropriate details you should know, like a number of stages completed, pending forms or task completion, etc.

Here you got an overview of features of Lightning that will help you get a more unique and customized experience with Salesforce. To get the most out of the software and access more tools, contact ExistBI Salesforce Consulting Partner today.

For more information about ExistBI’s Salesforce Training and Salesforce Consulting call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Tips for Integration of Informatica Data Quality (IDQ) with MDM

For any MDM (Master Data Management) task, data cleansing and standardization is an essential part. Informatica MDM’s Multi-Domain Edition, MDE, offers an extensive amount of really out-of-the-box cleansing functionalities. But sometimes these out-of-the-box features are not sufficient, and it requires a complete function to accomplish the process of data cleansing and standardization, like address validation and sequence generation. Here, Informatica Data Quality (IDQ) presents a broad range of cleansing and standardization functions that can be used easily with Informatica MDM. In this blog, we are going to discuss the integration of informatica data quality tips (IDQ) with MDM.

Let’s check out assorted options for integrating Informatica MDM with IDQ, with the pros and cons of each alternative, and highlight the best options. 

Options Available- Informatica MDM-IDQ Integration 

Integration of Informatica IDQ with MDM can be achieved by using these three options:

  1. Informatica Platform staging
  2. IDQ Cleanse Library
  3. Informatica MDM as target

Informatica Platform Staging

Informatica released new functionality in MDM’s Multi-Domain Edition (MDE) version 10.x that is Informatica Platform Staging used to do the integration with the developer tool, IDQ. This function allows staging and cleansing directly with IDQ mappings to Stage tables in MDM, avoiding the landing tables. 

Advantages

  • Quick Availability in Developer tool after synchronization 
  • Automatically highlights the changes in the structures into the Developer tool 
  • Load data into MDM’s staging tables by sidestepping the landing tables

Disadvantages

  • Not easy to maintain because of its connectivity with each base object 
  • Hub Stages like hard delete detection, Delta detection, and audit trails are not available
  • Manual Broadcasting for system-generated columns required
  • Invalid search records are not discarded when loading data to stage
informatica data quality tips

IDQ Cleanse Library

IDQ enables to develop functions for operation mappings and consuming them as a web service to transfer it for Informatica MDM hub usage, creating a new cleansing library called IDQ cleanse library. This function permits the use of received IDQ to cleanse functions as with other out-of-the-box cleansing functions. Informatica MDM Hub operates as a Web service consumer application, utilizing IDQ’s web services.

Advantages

  • Ability to easily build transformations compared to complicated java functions.
  • Hub Stage processing options like hard delete detection, delta detection, audit trail are available.

Disadvantages

  • Require manual creation or updating of physical data objects for each staging table.
  • Only synchronizes the web service citations

Informatica MDM as Target

To load data into the landing tables Informatica MDM, IDQ can be used as an ETL tool, or these IDQ mappings can also be used to load data directly into the staging tables.

Advantages

  • Standardizing data in the Hub Stage Process is not required
  • Hub Stage processing options are available
  • Ability to process on a lower version of Informatica MDM 

Disadvantages

  • Need to create and update physical data objects manually 
  • Hub Stage options are not available.
  • Need to produce System columns manually
  • Invalid search records are not rejected when loading data into stage 

Conclusion

According to the client requirements, you can now have a multitude of options that are available for integrating Informatica Data Quality (IDQ) with Informatica MDM. You can select the ideal options after analyzing the needs, pros, and cons of each option. Join ExistBI’s Informatica Online Training to understand in greater detail data cleansing and standardization. 

For more information about ExistBI’s Informatica Training and Consulting Service call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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4 Elements of a Data-Driven Decision-Making Framework with Business Intelligence Consulting

A company’s main objective is to establish well-versed data driven decision making framework, a Business Intelligence solution processes challenging company decisions in a fast, intelligent, and efficient manner. Companies that ignore a single element of the data analytics process will see a significant impact on performance and profitability. Business Intelligence Consulting has developed a BI framework that creates a data-driven culture and covers all the functionalities of each BI solution so that their customers get the greatest return of income from their investment.

Let’s check out below four key components of a BI Framework:

1. Planning

The planning component includes trend recognition, forecasts generation, determining business performance, and scrutinizing plans.

Trend Analysis

 In Trend Analysis, BI users are able to categorize various models in past statistics and recognize the opportunities the company can successfully attain alongside, identifying the challenges that the company could face in the future. For example, business users can check the behavior with which the company’s profit is growing in a specific duration.

Forecasting

Forecasting enables the users to predict future insights that help in setting profitable targets by analyzing the complete historical data of the last few years. Forecasting would be processed by the data analyst within the company.

Performance Analysis

Performance Analysis comprises of internal and external standards analyses the overall sales reports and finds out the highest-selling products that are imparting the greatest profit. Then the company can acquire more valuable insights into the production and profit in the industry.

Plan Analysis

The plan is being analyzed from different perceptions by evaluating the records related to the service like various targeted marketplaces, different rates, and existing similar products, etc. Then, the final plan is distributed in various departments for execution.

Data Driven Decision Making Framework

2. Plan Execution

The plan execution component is used to evaluate the success goals of a plan against the actual achievements. It allows you to conduct root cause analysis to recognize and analyze the drawbacks in your executed plan that affected your target profit.

3. Change analysis

In change analysis, you can identify the actual and actionable points requiring adaption in your plan. You can process various scenarios by commencing new products or services to understand the impact they will have on your business. Based on analysis with BI solution, you can better understand your business needs.

4. Optimization

The purpose of the optimization component is to put the data of BI solution into practice to optimize the internal processing of the business. So that you can utilize the insights to enhance marketing after evaluating the changes in the plan for launching a new product in the industry and getting better sales in the areas which are under-performing.

Conclusion

It is a fact of this business generation that a company with a BI strategy and solution in operation are far more likely to succeed than one without. The BI approach plays a foremost function in the organizing, implementing, and enduring execution of BI solutions. Business intelligence Consulting provides a blueprint that facilitates businesses to evaluate their performance, discover opportunities, and apply data reports and statistics to navigate business success.

So what type of strategies are you implementing? If you are not using BI solutions yet, you need to develop a BI strategy for your business to ensure your growth. It doesn’t matter, which type of industry, product or service you have; digital transformation is the core need of this competitive business environment.

For more information about ExistBI’s Training and Consulting Service call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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MicroStrategy Introduce HyperIntelligence in their Latest Update

5 New Capabilities in MicroStrategy 2019 Update 3

This summer has seen the release of MicroStrategy’s Update 3 which has the first-ever HyperIntelligence component to support users with advanced functionalities. This upgrade is openly available through Amazon Web Services and Microsoft Azure. The revolutionary data intelligence accessible within the platform will have powerful benefits for business users, analysts, administrators, and developers.  Today, we’re going to summarize what is new and what you can expect from this award-winning upgrade.

What’s new is MicroStrategy 2019 Upgrade 3

1. Firstly, users can expect advanced analytics and data intelligence within their daily calendar.  The update provides the user with all past, present, and future information to ensure they are fully prepared for every meeting or event.  This is also accessible within the HyperMobile app and the iOS calendar.

2. MicroStrategy has also enhanced the workings of Mircosoft Office, web browsers, business intelligence tools, and even Salesforce. With new HyperIntelligence technology emails can be enriched effortlessly.  With what MicroStrategy calls ‘Zero-click analytics‘ users can have access to data analytics that can improve business productivity and growth. The user hovers the cursor over words and HyperWeb produces data related to this term. This contextual insight can be voice-activated using natural language to provide the answers needed. This HyperIntelligence functionality is truly next-generational. As previously mentioned, these benefits are not isolated to your desktop, the advantages of HyperIntelligence have been integrated into their mobile applications. This, therefore, provides accessible, fast and relevant information on-the-go.

HyperIntelligence

3. The update also provides the designer with enhanced flexibility when designing their workstation cards.  There are now multiple options available to customize the cards built to meet the requirements of the business user.  For those who are new to MicroStrategy, there is still the pre-built template cards offering drag and drop metrics and attributes.  However, customized cards have a variety of widgets to provide control and freedom.

4. MicroStrategy can now be seamlessly connected to several other platforms providing improving performance, scale, and security.  These platforms include; Tableau, Qlik, Power BI, and Office.  This upgrade enables these platforms to be prompted to generate reports resulting in potential federated analytics.

5. The update supports data integration with Teradata assets.  Teradata has recently announced the launch of the Teradata Vantage platform.  The Teradata platform allows users across an organization to use their preferred analytics tool across a large-scale data source.

Ready to update?  It is also good to know that you will not need a metadata update to do so, neither will you need a full platform installment. For more support with MicroStrategy training or data integration consulting contact our expert team.

For more information about ExistBI’s MicroStrategy Training and Data Integration Consulting Service call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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9 Useful Tools You Must Try in SAP Business Objects Training

SAP BusinessObjects BI.4 is an invention of SAP that produces reporting applications and SAP business objects tools, using data from SAP BW and SAP HANA for data analytics. The tool kit of SAP BusinessObjects includes the tools for reporting and dashboards that business owners can use by consuming the data available in the form of tables or data structures in SAP HANA. 

Check out the list below and explore the tools of Business Objects BI.4 that you would learn about in your SAP Business Objects Training;

SAP Business Objects tools

1.  SAP Lumira

SAP Lumira helps businesses to create visualizations, stories, reports, transform data, and to do ad-hoc dashboards. It is a self-service data visualization tool that connects to the SAP HANA database directly. BICS connection driver and SQLDBC language are used for forming the connection to the SAP HANA database.

2.  SAP Crystal Reports

This window-based tool is used to generate reports using an in-built data structure for printing and publishing, for example, generating sales invoices, purchase orders, work orders, client reports, etc. JDBC/ODBC connectors and SQL language are used as the interactive language for the connection. It provides a crystal clear and high-pixel visual print, that’s why it is named Crystal reports.

3.  SAP Design Studio

It is an advanced-level designing tool that helps business users to design influential reporting applications and dashboards. SAP Design Studio also uses BICS connection drivers to interact through the SQLDBC language. This tool has the capability to do server-side programming, requiring full compatibility and sustainability with SAP NetWeaver BW and SAP HANA platforms.

4. MS Excel

MS Excel is the most popular Microsoft tool that is used by non-expert users; it facilitates business owners to explore data in analytical and calculation view (hierarchical data and data in cube models) in SAP HANA. Direct OLAP connection with the help of ODBO connector using MDX language connects directly to the SAP HANA database. 

5.  Analysis Office 

This is also a self-service analysis tool and leverages multi-dimensional data analysis. BICS connector through SQLDB language is used to form OLAP type connection with SAP HANA database or SAP BW. Business users can access and integrate the information available in the OLAP data sources.

6.  Explorer

Users of the whole organization can use this discovery-tool for searching or exploring fresh information and can access it from anywhere. The Explorer tool forms the connection with the SAP HANA database using an OLAP connection with a JDBC connector and SQL language.

7. Universe Designer

When indirect connections are formed with reporting tools like WebI (Web Intelligence) and Dashboard Designer, Universal Designer builds a transitional layer above the SAP HANA database. This helps to transform the relational and OLAP non-SAP data sources into important business information. For connecting to the database through SQL, IDT or UDT connects uses JDBC or ODBC connections.

8. Web Intelligence

This advanced reporting tool has the ability to ad-hoc and detailed reporting, utilizing query panels, etc. Web Intelligence uses the data available in semantic layers created by the IDT tool by using universes. Multiple resources can be accessed with IDT, while UDT allows accessing only one data source at a time.

9. Dashboard Designer

This Reporting Tool in the SAP BusinessObjects BI4 package is used for creating dashboards. It offers pre-designed dashboard templates to business users, which they can use to create powerful static and dynamic charts and visualization.

As already specified above, all the tools use separate connection drivers and database languages to connect with the data source platform. These tools are very beneficial to minimize the working operations of your business and to prepare better reports and documents.

Did you Find These Tools useful?

So why don’t you join SAP Business Objects Training to discover the more specific usages of these tools to leverage your business needs.  

Our team of SAP BusinessObjects consultants offers official or fit for purpose training, onsite or via virtual live classrooms.  Call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Learn to Build a Shared Hyper Cluster at Tableau Bootcamp

The biggest reason behind people’s obsession with Tableau analytics is its amazing engine, Tableau Hyper cluster Database (“Extract”). By enhancing its multi-node capacity, separating and then distributing the query process, Hyper can act like original business MPP data, which can run on modern infrastructure like Kubernetes. Here, we will discuss how to build an MPP database with Hyper. If you are new to this technology, join Tableau Bootcamp to practically implement these aspects.

Fundamentals and Background Tableau’s Hyper database was built from Scratch with standard functionalities (LLVM Code generation, Columnar data store, in-built capabilities, etc.), SQL dialect compatibilities, and Postgres network. It is a fast, neat, and convenient database. You can assign a full set of Postgres SQL statements with the new Extract API including copying and moving data in huge amounts.  

It’s great that we can approach the Hyper database with just minor adjustments to the Libpq based apps like PostgresODBC and PSQL by accessing the core potential of the engine.

Hyper Cluster

MPP- (Shared-nothing Use Case)

Commonly MPP (Massive Parallel Processing) architecture provides a database to run multiple worker nodes, partial datasets and aggregators combining the results from processing nodes. But if the horizontal scalability is missing, it won’t be able to leverage multi-server nodes to accelerate single queries.

By adding twice as many nodes you then see a two-fold increase in the performance level. Take an example of a webshop, where you store all your transactions in a single extract file. For evaluating the overall customer value, firstly you need to process the transaction for a specific customer, and then you can view the output in your Tableau report for all customers. And, if you have multiple computers to calculate the customer value, which is located in the same node, the algorithm will independently work on separate servers for each customer’s transactions. Multiple nodes will get more performance.

Converting Hyper Database to MPP Architecture

So how would you do this? Let’s check out a few things to make this conversion successful:

1. Build independent worker nodes on generic hyper Database

  • Create a docker image from Hyper Database that can be monitored from different sources 
  • To manage it’s flexibility, deploy it on Kubernetes as a Service 

2. Build an aggregator that will be the master node. In Postgres 11, there is a link-like facility that diverts the queries to other databases (Hyper also acts like a Postgres). So firstly deploy the Postgres 11 on Kubernetes, and set-up foreign-data wrapper with the help of Hyper. Then, import and synchronize metadata across master nodes.

3. Finally, the aggregation will be done on shared-nothing data, and then you can validate it easily.

In Summary

After a thorough study and practically using it’s various aspects, you will be able to build a distributed Hyper MPP database cluster that supports horizontal and vertical scaling, ingestions and allotting queries between servers in a Kubernetes cluster. There is a single limitation of custom SQL based data source as Postgres that it drives back on partition tables. 

So, if you want to leverage more benefits for your business by using tableau software, study and solve more practical use-cases. Join ExistBI’s Tableau Bootcamp to gain knowledge and hands-on experience to fulfill your company’s needs.

For more information about ExistBI’s Tableau Training and Tableau Consulting services, call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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How SAP BusinessObjects Training is Inspiring Gender Diversity in Data Science

SAP has joined forces with the supermodel and superpower Karlie Kloss in support of women in the technology industry. Entrepreneur Karlie Kloss has established a non-profit company called STEAM (science, technology, engineering, arts, and math). Her mission is to support the next generation of innovative women entering the workplace. SAP wants to help young women access Karlie’s ‘Kode with Kloss’ project that provides training and experience within the technology used in businesses today. They plan to raise awareness of the benefits of SAP BusinessObjects training within the STEAM community.

SAP BusinessObjects Training is Inspiring Gender Diversity

With some studies quoting only 13% of the data developers being female, there is a common goal within the profession to improve this. You don’t have to look far to see this innovative being adopted to improve the industry’s diversity.  Stanford University has held an annual Women in Data Science (WiDS) conference since 2014.

This conference includes a Datathon to challenge and showcase skills. WiDS now supports over 150 regional events worldwide.  The project has also recently started an exciting podcast with talks from leading women in the big data and data analytics profession. Similarly, in the United Kingdom, there are a Women in Data organization run by a former Managing Director in Strategic Analytics for Barclaycard Europe.  She too holds an annual conference, with workshops being fully booked within thirty minutes of release.

With the rapid growth of the technology industry, there is a pleather of jobs available to both men and women. Yet statistics show an actual decline in the number of women in the data science environment.  Some, have attributed this to lack of early exposure to the subject, workplace environment culture, inherited bias and sense of isolation. 

If you would like to encourage your female employees to purpose the data science or if you yourself would like to know more, contact our enthusiastic team of expert trainers. Below I have also included some helpful and inspiring resources;

Women in Data Science (WiDS) – Stanford University offers an annual conference and much more on the latest trends and networks.

Women in Big Data Forum – A LinkedIn forum offering support and mentorship from leaders in the field. 

Girls who code – a non-profit organization encouraging young girls in the school environment to take an interest in technology and data science.

National Center for Women & Information Technology (NCMIT) – This is an impressive non-profit organization that brings together; universities, businesses, government, and non-profit organizations with the aim to increase women’s involvement in the technology industry. 

Our team of SAP BusinessObjects consultants offer official or fit for purpose training, onsite or via virtual live classrooms.  Call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Take ExistBI’s Tableau Bootcamp to Differentiate Yourself in the competitive job market

If you’re in the market for a new job, you know how competitive today’s job search can be. Not only is it extremely competitive when applying for roles, but job openings also seem to outnumber qualified applicants. According to Forbes, “the existing talent shortage will reach its worst levels in 2030 when an expected 85.2 million job openings will go unfilled worldwide.

It’s a race to see who can apply the fastest, set themself apart from other job seekers, and wow recruiters with a CV, LinkedIn profile, or portfolio. 

One cause of the talent shortage can be attributed to the need for technical skills like data analytics. Harvard Business School reports the business and society potential created by big data is “disrupting a wide range of roles, from engineering to functional analysts to executives.” Across many organizations, functions, and industries, people will need to develop their data skills.

What do US campus recruiters want to see? 

We asked the campus recruiter at Tableau about her point of view on skills that current or returning students should be acquiring to boost their professional profiles.

“We view data skills as more of a mindset than anything. Regardless of the information you’re analysing, we see someone with this skill set as naturally curious and passionate about solving problems. Whether you’re looking to solve a critical issue or you’re more interested in personal data, data analysis skills are extremely transferrable.”

Data skills are important for anyone starting their career. When LinkedIn reported hard and soft skills companies to need most in 2019, “Analytical Reasoning” was ranked number 3 under hard skills. Digging deeper into this conversation, I asked Kari what makes a strong candidate and she listed a few examples of skills that demonstrate competency:

  1. Transferrable skills like data skills! Recruiters like to see projects that demonstrate leadership, flexibility, and humility. Show us how you’ve applied data to make decisions.
  2. The ability to code in Java, C, C#, C++, Ruby, and other languages, as well as showing proven success and ability to meet deadlines with a project.
  3. Experience working with customers. Seeing that you have past success in customer-facing roles, possess technical aptitude with tools, and are goal-oriented tells us you’re an applicant to consider.

Whether you’re still in school or a recent graduate, it’s never too late to pick up data skills, learn how to code, or gain experience working with customers – all things that make you a more compelling candidate in the job search.

Hear from students – the Data Generation

One new Miami University graduate, Buchi Okafor, had a passion for sports and landed a finance internship at Under Armour where he first learned Tableau. Buchi quickly learned that “whatever job you’re doing you’ll be looking at data. The people that separate themselves from the pack are those who can gain insights from data pretty quickly and share their insights with others in a way their business partners can understand.” When a new analytics team at Under Armour formed, Buchi decided to focus on his love for data and now works as an analyst for the global pricing strategy and analytics team.

Another new grad, Harpreet Ghuman, began his data skills journey unconventionally. He saw a Game of Thrones visualization in Tableau Public which intrigued him, leading him to teach himself Tableau. At the time, Harpreet was in a master’s program at the University of Maryland for business administration and management. 

Harpreet said, “Once I started making visualizations with Tableau, it didn’t matter to people that I didn’t have a background in data. Visualization, like curiosity, is a skill you can translate.” With his newfound love of data analytics, Harpreet decided to also pursue a master of science degree in marketing analytics. Now, he has his dream job as a senior consultant in data analytics at EY, putting his Tableau skills to use with his job’s focus on data visualization.

Check out more about Tableau Bootcamp training in the US for University students here


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5 Tips for System Administrators to Managing Application Testing

Systems administrators have a complex job today, as companies adopt multiple business intelligence platforms and cloud solutions.  The admin staff has to manage upgrades, new content, patches, fixes, and security developments.  This includes the impact of these changes on existing data, dashboards, and reports. This requires a significant amount of time, skills, and manpower.

Any undetected effects on the data can lead to the poor performance of the software, inaccuracy of the data, and reliability of data-guided solutions throughout the organization.  This will all reflect negatively on the systems administrators.

Tips for System Administrators

Typically, the systems administrator would review data for inconsistencies following fixes manually. This involves selecting a subset of data and reports and checking their accuracy.  However, this would not be realistic on large-scale upgrades therefore, automation is critical to ensure the long-term success of the applications.  Here are our tips to support the automated maintenance process;

  1. Ensure you’re testing realistic use of the application.  It is important to test the application following an update in the way the customer or user would require it to function to meet their everyday needs.
  2. Replicate the user’s environment.  It is important not to affect the current live working environment.  Therefore, the tests need to be run a replica of the company’s live system.  This step is easier with a cloud platform, as a replica environment is easier to develop.
  3. When creating an automated testing system we would recommend using a platform approach rather than disjointed separate scripts.  This will allow you to test multiple scenarios at various scales within the application.
  4. Examine your test results to a granular level.  These tests should not purely result in a pass or fail, all detailed statistics and data should be gathered and examined to help predict future potential issues and to improve the automated testing practice.
  5. Store results in an optimized format, such as a data warehouse.  This allows easy analysis, enables the performance of the application to be reviewed and historical analysis to be monitored.

This is all time consuming and challenging task for the systems administrator, who is already in high demand.  MicroStrategy has developed a testing platform that can be used to analyze over 20 different customer applications.  They even use this single platform to test their systems before releasing updates, in an attempt to minimize disruption to the data.  For more information on managing application upgrades and developments contact our team of experienced business intelligence consultants.

For more information about ExistBI’s MircoStrategy training and MicroStrategy consulting call your nearest office: US/Canada: +1 866 965 6332 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Data Conference Highlights Importance of Staff Integration with Data Integration

This May the annual Data Conference was held in Boston.  This conference is unique as it’s developed by professionals for professionals, there are no sales pitches, booths or paid advertising.  The mission of the conference is to allow data science practitioners to interact as scientists.  This year saw speakers like; Alessandro Panella, Data Science Manager at Facebook, David Harkness, Data Science and Predictive analytics from National Geographic to Yoni Halpern, Software Engineer at Google.  Subjects discussed ranged from the demanding forecast for the future of data analytics to automated index management.  

Robert Grossman, Analytic Strategy Partners and Jim and Karen Frank Director, Center for Translational Data Science from University of Chicago gave an essential talk covering the management of data analytics projects in the work environment.  This addressed the management and integration of a new analytics model throughout different departments, to then be integrated into services and operations. This is an aspect of all our training and consulting programs that we ensure we cover.  With a consistent approach to data management throughout the company you are guaranteed to get the most from your business intelligence investment. 

Data Science Conference

As with Grossman’s talk we would like to use a case study to illustrate the importance of this point.  In a recent Salesforce consulting project, we were engaged to not only manage workflow but also, staff enablement and integration.  This was a challenging assignment for our certified Salesforce consultants who had to first assess the current landscape of the Salesforce platform integration by the in-house team.  In this initial assessment gaps in knowledge, strategy and implementation were identified.  This required careful but concise management by our team of experience professionals to ensure the client’s CRM initiative was a success.  The client also benefited from our data warehouse consulting services, involving the design and architecture development for new data warehouse capabilities using Microsoft Power BI.  Our team of data specialist worked seamlessly with all members of the company from data analyst to CIO.  This open and clear communication ensured everyone is now working with a common goal and the outcome has seen a benefit to customer service and increase in sales.

It is important to recognize the importance of ensuring a universal company ethos of data driven decision-making.  This should start from adequate training of your chosen platform, supported integration and frequent report analysis throughout the company.  These are some of the key points raised in Boston at the Data Science conference, and we couldn’t agree more.  To discuss your company’s data analytics requirements, challenges and staff integration needs contact our team.

For more information about ExistBI’s Salesforce Training and Salesforce Consulting call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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7 Big Data Podcasts You Should Know About

Last month we saw the launch of the SAP Partner big data podcasts, ‘From Coffee To Cloud’.  The first episode was a relaxed twenty-five minute chat with guests Ekaterina Kruse, the Technical Partner Manager, and Henning Heitkoetter, a Product Owner. The main topic discussed was the SAP Cloud SDK. Ekaterina explained how SAP Cloud SDK provides developers with the tools to develop extensions with more ease.

This Cloud application communicates with other SAP solutions, allowing them to build additional functionality. This differs from previous approaches from SAP as it makes communication more consistent throughout applications. Future posts promise to cover general company updates, tips and tricks, and open discussions with guests on the latest hot topics in the industry. You can hear the whole podcast here.

Big Data podcasts

Podcasts are a great way to stay up to date and inspired, here are six podcasts that we would recommend you check out;

  • IBM Big Data & Analytics Hub– This weekly podcast covers a broad range of topics related to data and analytics, making it relevant to all in the Business Intelligence services regardless of your company size.
  • BIFocal – Is hosted by two Microsoft MVPs and offers monthly updates on the business intelligence industry, hosting regular guest speakers, as well as handy tips and tricks.
  • The 10 Minute Business Analytics Podcast – This weekly podcast knows it’s audience well, in the business world we’re all short on time.  They offer around three podcasts a month, lasting around 10 minutes.  They update the listener on the business intelligence industry, big data, analytics applications, etc.
  • Data Stories – This podcast is focused on data visualizations, with an impressive library it posts once a month. Through this, you will gain insight into the best way to tell a story with your data.
  • Liner Digressions – This weekly podcast covers topics such as; data science, machine learning, and artificial intelligence. The host helps answer common queries and give insight into future technology.
  • The Digital Analytics Power Hour – This informal podcast posts once a month.  With three hosts and the occasional guest they tackle the hottest topics in digital analytics over a few drinks.

For more information about ExistBI’s SAP BusinessObjects Training and Consulting services, call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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What’s New in Tableau’s Latest Software Update?

Since last year’s Tableau conference in New Orleans, we have been looking forward to the launch of their ‘Ask Data’tool and it has now arrived in Tableau’s latest software 2019.1 updates.  This task-based tool is a union of natural language processing and expert data visualization. This new tool expands the accessibility of this platform, allowing any user regardless of their background in data analytics to ask questions of the data to create insightful visualization reports. The tool is navigated via keyboard and mouse however, the company state that they hope to release voice-activated control in the near future.

The great news is, if you already have Tableau, this will automatically update to include the Ask Data tools. This means that all your current database will be natural language enabled.  In addition to the natural language functionality, the upgrade introduces; advanced data preparation, the ability to export data visualizations into PowerPoint files, user alert customization and a complete redesign of the mobile interface. The mobile app is available to iOS and Android users.  Changes to the app include; improved search, favorites experience and interactive previews offline.

There is an additional cost for a Data Management Add-On for those using Tableau server.  This provides access to Prep Conductor, allowing the data prep workflow to be scheduled and monitored.  Tableau announced that later this year this Data Management Add-On will expand it’s capabilities to include new cataloging functions to enable the user to search for data across all data sets from a single point.

If you are new to Tableau or want to get more from your current software, contact our team of certified Tableau training experts.  We offer a range of Tableau classes including our unique Tableau Bootcamps. 

For more information about ExistBI’s Tableau Training and Tableau Consulting services, call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Is Multi-Cloud the Future of Business Operations

Since its introduction cloud-computing has fast become part of not only businesses every day but, everyone’s day today.  So, it really should be no surprise that we are now discussing multi-cloud operations. The question we’re asking today is will these complex operations help or hinder business?

The benefits to multi-cloud strategies are simple; performance optimization, budget-saving, reduce the risk of DDoS attacks and avoiding vendor lockdown, just to name a few. Not every department or business function has the same requirements, therefore, using different cloud platforms allow their various data and application needs to be met. However, if you asked CIO to provide details on internal and external cloud layers used by the company and answer can vary significantly.

For those smaller operations, they may only use two or three cloud providers. Such as Google for its users in the United States and Azure for European users. For larger companies, however, the cloud operations are a complex web of data and services. With interlinking data flow, some even from cloud to cloud.

Alongside, the clear benefits mentioned above there are equivalent challenges.  Such as; cost, expertise, resources, complex management, just to name a few.  Managing and monitoring these ecosystems is a real challenge for CIOs today. Due to this, we have seen an increase in the adoption of management fabric systems. These management programs span multiple cloud systems to give the user a detailed understanding of their data architecture.

Examples of this are; MapR’s Global Data Fabric and Pivotal Cloud Foundry. I don’t need to tell you what an expense this multi-cloud operations amounts to, let alone the need for a management program to run them all.  It is, therefore, important to choose the right cloud providers for your business to generate revenue.  

Multi-cloud

It is clear to see the advantages of this multi-cloud approach however, there must be a disaster recovery plan in place to protect the company. When planning out your cloud operations we suggest visualizing it as a tree, each additional branch being another cloud system. The reason for this is, you must have a contingency plan for the interlinking cloud services that often rely on one cloud source. Everyone had to learn from the Amazon outage incident a couple of years ago, which really impacted on their SaaS services.

With the right fabric management and the right cloud services, many companies have seen a significant return of investment. Companies must also ensure they choose the most efficient app for their multi-cloud environment. Traditional apps can be inflexible and difficult to manage and scale. By utilizing the most appropriate cloud-native app you will ensure the most service-oriented outcome. We would also recommend automating low-level tasks within monitoring and maintenance. By applying a standard automating policy to all cloud services throughout the company, you reduce time spent on maintenance, human oversight and allow for seamless updates.

In conclusion, there are substantial benefits to multi-cloud operations as long as you have the strategies and systems in place to manage and maintain them. If you want to get this competitive advantage let us help you develop a strong architectural map with a well-crafted future management plan. From migration, security, change management and working progresses, our data integration consultants have a wealth of experience in this field. 

For more information about ExistBI’s Training and Consulting Service call your nearest office: US/Canada: +020 8610 1823 | UK/Europe: +44 (0)207 554 8568 or complete ExistBI’s contact form.


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Today’s Mobile Workforce Need Mobile Data Analytic Applications

As we start to see an increase in flexible working and working remotely, we also see an increase in workers using mobile devices to view their data. The data analytic software industry has had to pay attention and provide mobile-friendly presentation and apps.

This is something we have definitely seen in the latest software releases over the past six months. Today’s workforce conduct business from multiple locations at any time. Employees therefore expect mobile capabilities from their software and organizations have to deliver. However, to provide analytics on small devices can be challenging. We are not talking about tablet devices, as these translate across from the original web application effectively.

Now the data visualization reports have to support all size screens from laptops to mobile devices. Vendors are divided by their approach to addressing this issue.  Some software’s have invested in applications that are functional on both IOS and Android systems. It is important to note that the majority of these mobile adaptions have had to simplify their interactive functionality due to the small display.  This can range from allowing the developer to study the user interaction and leverage this data to guide development to being more user engagement focused.  

Mobile Data Analytics Apps

Therefore, if you have a mobile workforce you may want to choose your data analytics platform based on its mobile capabilities. So, here is a summary of the most popular data analytics software mobile applications for you;

Vizable – This Tableau mobile application allows the user to view and interact with their data. This app provides feature animations to improve the process of analyzing the data.

Itunes rating: 4.5/5 Stars

SAP Roambi – Cloud-based mobile application from the SAP analytics portfolio. This advanced design transforms data from its source to rich interactive visualizations. Dashboards and reports can be published in a similar way to web applications.

Itunes rating: 4.6/5 Stars

Informatica Cloud – This is an iOS app is designed to manage your task flow and give the user the ability to troubleshoot any problems.

Itunes Rating: 4.3/5 Stars

PC-MobiMonInformatica PowerCenter mobile monitoring brings functionality from the PowerCenter platform to your phone.

Itunes Rating: None available

IBM Cognos Mobile – IBM Cognos mobile app allows the user to view and interact with reports and dashboards. 

Itunes Rating: 3/5 Stars

MicroStrategy Mobile – Interactive interface from the MicroStrategy platform.  This app provides reporting and analysis capabilities.  

Itunes Rating: 5/5 Stars

For more support with your data analytics platform contact our team of experience. We provide training on all the above platforms from Informatica training to Tableau classes. Should you have already adopted one of these data intelligence platforms, yet require support on a complex project or feel you’re not getting the most from