You don’t need big data when you have control over your data. Too much data is bad, loss of control is terrible, and nothing works without data control, which should automatically lead to fewer data.
Big data has come a long way: from a scary term meaning data avalanche, it has become a buzzword in the data industry and, therefore, an instant favorite with marketers. The promise of understanding customers through big data is often replaced by the mantra “big help many” and “what you have, you have”, meaning that quantity trumps quality and data-driven solutions take a back seat.
But the notion that the more data we collect, the more opportunities lurk in a large amount of data is wrong. The idea that we give too much freedom to decide what criteria to use to collect data only in the future – as needed – is detrimental to the business, the data, the customers and ultimately, the environment.
In the everyday lives of marketers, data seems abundant and freely available. Automating data collection, which requires little human intervention, may contribute to this impression. The opposite is true: although tools are freely available in their basic form, the amount of data is limited. Sometimes only a certain amount of data is evaluated, and the rest is lost. In other cases, the cost of the tool increases with the amount of data.
Nor is data storage free. The space requirements of servers purchased abroad or installed in-house increase hardware requirements and energy consumption. Servers need to be maintained, and data flows monitored.
Data is not free and expensive—money, time, and energy. The more you collect, the more it costs.
Extracting information is also essential for personalization. In the age of consent management, customer data platforms play an important role. Customer data can be stored and, for example, compared and combined with anonymous twin statistics. Therefore, data should be compatible by default. Operations, periods and fields should be defined. To be valid, data need purposes, tasks, structures and definitions. They are not usable and, therefore, useless.
To call these data warehouses is needed a misnomer. Unstructured collections lead to poor data quality, leading to inaccurate results. Was the volume of customer transactions high or low? Was the campaign successful? Did we reach our target audience, and who are they? On which channels and with which products were we successful? Marketers and managers have to ask themselves many questions before they create a CDP and collect the data that can be used.
Or they must wait for mistakes to happen. Inventory prices are set incorrectly, products are stocked incorrectly, promotions cause negative sales such as high redemption rates, and automating these activities leads to high costs. Data errors cost companies a lot of money.
But the most painful is the loss of loyal customers. Inappropriate practices, confusing data collection, lack of transparency, and loss of quality and trust are costly and take time to recover. Those who intervene early and create sustainable data quality will prevent the baby from falling down the wall.
The introduction of consent management is, of course, a consequence of the General Data Protection Regulation. However, data processing in the context of data protection is not limited to consent management but also needs to be monitored: where is the data stored, who has access to it, what data is stored, how long is it stored, and if stored is it sufficiently anonymized so that no inferences can be drawn about individuals?
The GDPR contains several data retention rules for businesses and good reason. Those who collect data knowingly and according to pre-defined criteria can check that they are doing so in compliance with the GDPR. Failure to do so risks loss of control, fines and, in the worst case, loss of customer trust and, therefore, loss of the most important asset – customer loyalty.
Trust is the biggest problem in marketing today. Fake tracking makes it difficult to get trustworthy information about unregistered website users. It is even more critical that customers voluntarily register and share their data with your company. Transparency increases trust: what is stored, where and who can use it for what purposes? If customers know their data is protected, they are less likely to consent.
Trust is a fragile plant. It takes time and effort to earn it. It also makes you more playful and faster. But how do you ensure transparency, accountability and clarity when data collection is unmanageable?
Climate protection is one of the most pressing issues of our time. Why does this affect big data? Because data takes up vast amounts of storage space, even if the vague notion of the cloud makes it seem that all data is stored in space. It is stored on servers that need power. Today, the power demand in data centers is increasing despite the capacity increase. This is due to the exponential growth of workloads, which will increase tenfold between 2020 to 2030.
It is essential to bear in mind, however, that these CO2 factors are wasteful. Making conscious choices makes it very easy to save energy without sacrificing anything.
The way out of the big data trap is as simple as it is obvious. Recent advances in tools mean there is yet to be a solution. Google, the leading provider of free analytics tools, is going its own way. With the move to Universal Analytics GA4, many of the settings automatically set by the tool are no longer valid. What to measure? The user now needs to think in terms of definitions; for this, a concept, a data strategy, is needed first.
Such a solution is called a data governance. The data managers know which data is needed in the short term and which in the long term. Cross-channel refers to the post-universe, a world in which customers move seamlessly between online and offline environments, and the business strategy that will determine the direction of this new universe in the short and long term.
This also means that data strategy is not only in the hands of marketers but also in the hands of management. At the same time, they are part of this governance system and hold ministerial positions that allow them to influence government policy. At the same time, marketers contribute to a sustainable future for the company, its customers and the environment.
How can Machine Learning help your Operations?
Machine learning services can help improve your operations in a number of ways. For example, it can be used to optimize supply chain management, predict equipment failures, or improve customer service.
Companies can also use machine learning to analyze data from various sources to identify patterns, trends, and insights that can be used to make better business decisions.
Additionally, machine learning can be used for automation of process, which can improve efficiency, reduce costs, and increase productivity.
Our machine learning consulting services cover these sectors: Manufacturing, Retail/Brands, Pharmaceutical, Healthcare, Insurance, Financial Services & FinTech, Technology Platforms, Gaming, Telecoms, Hospitality, Engineering, Professional Services, Media & Communication, Education, Life Sciences, Public Sector and Logistics.
A data warehouse is an effective tool to store your data and use it. Data warehouse software allows you to process, transform and use your data to make decisions. As data warehouses are popular tools, it is not surprising that the market is growing and developing in new and innovative ways.
Data is essential for effective decision-making, but it is only valid if used. Data warehouses help organize business data but must provide the tools to turn data into meaningful information. Data warehouses provide the analytical capabilities needed to make informed, fact-based decisions.
Trends show that everyone is investing in a data warehouse. It may be time for you to do the same. Below are ten key data warehouse statistics to help you find out, keeping you up-to-date with the latest trends in this exciting field.
This is an estimated annual growth rate of 24.5% for the data warehouse market. This growth will be driven partly by increased investment in technologies such as machine learning and artificial intelligence, which are expected to grow significantly by the end of the decade.
Many companies deal with large volumes of data, but only a few are active in the global data warehouse market. There are 37 708 companies in the global data warehouse market, 39 of which produce this technology. According to data warehouse statistics, more than 64 tools and technologies are on the market.
Snowflake leads the data warehouse sector with 3 174 domains, followed by SAP Business Warehouse with 1 866 domains and a market share of 11.94%.
According to a recent study covering five countries, the main reasons for using data analytics are to improve processes and reduce costs. In addition, most users answered, “strategy and change” (57%). The third most common use of data analytics is to monitor and improve financial performance (52%).
Although nearly all Fortune 500 company executives (92%) are increasing their investment in artificial intelligence and big data, only some companies have created data-driven organizations. Of these companies, 55 percent have spent more than $50 million on AI and big data investments and 62 percent have achieved tangible results. But data warehouse statistics show that only 31% of organizations are data-driven.
This is just one of the benefits of the cloud storage model. Scalability and flexibility are also important, as it easily adapts to changing data volumes. However, changes in data volume do not affect the overall performance of the data storage. Data storage statistics show that cloud solutions are also scalable in terms of cost.
According to data warehouse statistics, integrated support for cloud, on-premises, and regional computing (28%) is another key reason for investing in infrastructure change. Other reasons include the following:
Easier real-time data processing (17%).
Cheaper and simpler data management (13%).
Shorter response times (11%).
Data warehouse statistics show that only a few companies use a central data warehouse. In addition, 37% of executives reported having multiple data warehouses for different data. Similarly, 26% have a data warehouse with linked data sets. This means that 63% of respondents use more than one data warehouse.
This represents a growth rate of 22.56% by 2026 and a three-fold increase in the market from $4.7 billion in 2021. By 2022, the growth rate is expected to be 20.62%, according to Data Warehouse. Technavio’s report warns that increasing cybersecurity threats to sensitive data will hamper growth over the forecast period.
According to the second report, companies lose between $9.7 billion and $14.2 billion yearly due to insufficient data. Data quality can lead to errors that damage customer relationships and a company’s reputation, leading to reduced sales and profits.
Some industries have been using big data for decades. Others are in the emerging or expanding stage. As a result, the power of data warehouses is available to more people than ever.
Areas such as user experience and customer data are particularly important. The volume of customer data is greater today than ever before. In addition, companies are increasingly successful in reaching larger but more fragmented audiences.
This creates unique challenges and opportunities for the data warehouse market.
Have a data warehouse project in mind? Let’s see how our Data Warehousing consulting team can help you harness the power of your data and transform it into reliable, actionable intelligence that can drive business success!
Data science – applying science-based methods to data analysis – is becoming increasingly important. However, it often needs to be clarified how this process works, what role it involves and what benefits are derived from using data scientists. In this article, we will attempt to define data science, explain the fundamental processes involved, and illustrate data science’s roles. To move from theory to practice, we briefly illustrate the added value of data science.
Data science is an interdisciplinary approach to using data to create added value. It consists of statistical, data and economical methods, which opens up the possibility of developing solutions based on big data.
The term “data science” was introduced to distinguish data science from data processing.
Today, we mostly think about using big data and machine learning to develop problem-oriented solutions when we talk about data science. The process of data science has established itself as a method for finding practical solutions.
The application of data science involves understanding the problem and developing a solution based on the data. This solution is based on advanced analytics, such as machine learning. On the other hand, it is essential to develop an iterative and mutual understanding between business and industry experts during the process so that the solution recognizes the customer.
The most crucial step in the data science process is identifying, understanding, and developing the right solution. It adds value to business areas such as sales, marketing and manufacturing.
For this solution, the correct data must be identified, collected and prepared for evaluation, optimally documented in a data catalogue and stored in a data warehouse or data lake for easy access.
In the next step, the data are processed and analyzed using machine learning algorithms to create a schema.
Once the decision has been made to bring the machine learning model into production and deploy it operationally, the next step in the data science process is deployment. Model deployment means the data pipeline or machine learning delivers the model through a dashboard. This means that other systems and channels within the organization can access and process the results.
As mentioned, several times, there are many different roles in data science. All roles are listed here, ordered by the frequency of their presence in the process.
The central role in data science is that of the data scientists themselves. This role is interpreted in different ways. As a generalist, it usually covers the whole process, but more and more companies that specialize in this role are emerging.
Without data, there is no analysis. Data scientists are often at the heart of data analysis, and data engineers are the ones who lay the foundations.
You’ll quickly find data architects if you’re looking for an enterprise data infrastructure. Data architects oversee the entire IT infrastructure and are responsible for the security and access control.
Data analysts typically work only with structured data from the data warehouse and handle ad-hoc analysis from the back office. In contrast, data analysts work with various data visualizations from dashboards.
The data administrator mediates between data scientists and industry stakeholders. They are responsible for translating the technical results of the data science process into a clear understanding of the business.
It has two main aspects that are important for businesses and other organizations. First, data processing is standardized with clearly defined steps. This allows for better, more efficient and transparent use of data. Second, it allows previously unknown patterns to be discovered. This enables initiatives to optimize processes, increase turnover or create innovative business models.
These two aspects, together with the fact that we are creating and storing more and more data, are increasingly at the heart of data science. Like traditional departments such as audit or IT, all companies and organizations will address data science and define it as a business strategy. It will become so deeply embedded in business processes that data science work naturally based on data.
Hiring a data science consultant can be advantageous because professional data science consulting companies like us, are extremely dedicated to your problem. We also provide fast, accurate and well-tested results and have an entire team of experts. Contact us for a free evaluation of your data science project now!
Do you want to realize the full potential of data in your business? Then there needs to be a way around a data governance model. It ensures seamless data flow between all parts of your business and information quality, availability, usability, and security.
Before relying on analytics for all or part of your strategic decision-making, you must first put the right processes in place. Here are some actionable tips for developing an effective data governance model effectively.
To get the most out of data, stakeholders need to know how to select, collect, store and use it effectively. Consider all the data available in your company and identify the different sources, such as administrative systems, websites, social media, and marketing and advertising campaigns. Then identify the points of friction where poor data quality results in loss of value.
All parts of the company should be involved in the use of data, from senior management to team leaders and corporate and field teams. All employees must understand the challenges and benefits of sharing high-quality data.
Set strategic objectives for the whole company or individual business units. Also, define all corporate performance indicators, so everyone understands their role in the data governance model.
When starting a data governance project, avoid falling into the trap of trying to answer all the technical, organizational, and legal questions at the same time. It takes time to achieve the first tangible results. Draw up a detailed roadmap with milestones to evaluate efforts and results.
Remember that different data governance frameworks exist. Choose the solution that best suits your environment, your needs, your human and financial resources, and the maturity of your data.
Appoint a data analyst responsible for data governance within your organization. They will approve and prioritize projects, manage the budget, recruit project staff and ensure complete documentation. Ideally, data analysts report directly to the Executive Director. If your company is smaller, you can assign this role to another manager on a comparable level.
Once the data governance project is ready, it can be put together, and strategic decisions can be made about implementing it in different business areas. This body approves the data governance policies and takes care of all data management, security, and quality issues. It also holds regular meetings where you can give feedback.
Ensure that the data relevant to the project is collected on a data management platform that ensures data reliability and connectivity. All team members must be aware of the existence of a central data warehouse. This creates a shared vision.
To successfully implement a data governance project, standardized procedures must be established, and a common language must be found within the organization. Give your teams a data warehouse that allows them to define databases, storage, and processing methods. In this way, it makes data accessible and understandable to all employees.
The data warehouse consists of an enterprise glossary that contains a precise definition of all terms related to the data in circulation. A template also shows the structure of the company’s data and provides information on how the data is stored. A data flow diagram should also be provided. The data folder should include a section on the format of the different types of data and information on access and conditions of use.
Data underpins most decisions, such as the type and timing of advertising or communication campaigns, target audience segmentation, website or mobile app customization, or feature additions. Rely on the quality of the data. Quality data can have severe consequences for your business, such as lower revenue, traffic blocked by ad blockers, or overestimated conversions due to poor feed performance.
To mitigate these risks, you must be vigilant at every stage of the data lifecycle – from the critical point of collection. Any modification or update to the site or tracking poses a risk to the quality of the record. Implement effective methods and tools to monitor and document the process.
First, ensure that the labels in the labeling plans are applied correctly. Check them regularly and thoroughly, preferably with automated acceptance testing, as manual implementation is very time-consuming and increases the risk of errors.
Since the General Data Protection Regulation (GDPR) introduction, companies have been aware of the importance of protecting users’ data on different digital platforms. Not only can you face penalties in the event of a breach, but it can also damage your brand image and cause a loss of customer trust.
This is why you must ensure that visitors’ consent is correct, voluntary, and up-to-date on your websites and mobile apps. That’s why you must choose a service provider that handles data strictly and complies with all legal requirements.
Democratizing data within your organization is a key element of your data governance approach. All information and resources are available to employees who need them to perform their tasks and create value. Several measures can help, such as defining the purpose of data, where it is stored, and how it is accessed. In practice, the appointment of data analysts to help users in their daily lives has proved to be a good idea.
Finally, a specific support program should be set up. For example, you can organize data governance training and internal workshops to familiarize users with the practical use of the tools and the use of data for specific topics. The team can create checklists for specific activities by encouraging staff to use the data.
It was already a few years since the release of SAP BI 4.3. It brought an overhaul on its already jam-packed features that offers flexibility in the users. With the release of SAP BI SP02, the tool has gotten a lot of improvements and SAP BI is slowly opening its door to more and more sources that allows users to do more in bridging the gap between in-house, file-based, and cloud data.
Since the introduce of SAP BOBJ into the market, SAP BI has been focused to be used with in-house data sources. If you have a data source that is from an external source, you need to do additional steps for you to incorporate your data to these external sources.
Released December 2021, the latest iteration of SAP BI added support for one of the most popular Cloud Drive in the market: Google.
Figure 1: Google Sheet as a data source for Web Intelligence
Other Cloud Support that was added was the ability of a specific scheduled reports to be saved in Cloud Storage, for now it is Google Drive.
Figure 2: Google Drive as a Destination for Scheduling
The introduction of Cloud Support for Google is a positive sign that SAP BI will bring support to others due to the new way of companies are adopting to the new normal of working: Hybrid Workspace. Unlike before that companies are solely reliable to Virtual Private Network (or VPN) to connect to the system to their job. This option can now allow users to use Cloud Storage as an alternate in case they need it.
SAP BI is also adopting a new data source that is only available previously on SAP Analytics Cloud: OData Service
Figure 3: OData Web Services as a Data Source
OData or Open-Data is a protocol allowing users to use data using RESTful API. This became a standard in exchanging data complexity utilizing builds from HTTP, JSON, and others to address and access information.
SAP is utilizing this in SAP Analytics Cloud and now, SAP BI.
In the past, users are adamant of using a file-based data source because of its lack of features like Query Filter. Users are either discouraged to use it and scrap it altogether or some rely on workarounds like uploading an excel file in SAP Business Warehouse, which can be tedious in some cases.
But with the release of BI 4.3 SP 2, users can now take advantage of the full functionality of the Query Panel even if it is a local source.
Figure 4: Query Panel for Excel Data Source
The enhanced Query Panel not only breaks the distinction of Local file and Database but also allows flexibility to users to create ad-hoc Web Intelligence reports with ease.
Note: For Local files uploaded in the BI Launchpad still requires users to have permission to the file in order for them to reload the data in the Web Intelligence document.
In SAP BI Web Intelligence 4.3 SP2 added a hefty feature: Properties Tab. This allows users to modify the object property without going to the data source, query panel, or IDT (Information Design Tool) to change it.
Figure 5: Changing Data Object Property in Webi
Users can now utilize Data Types like High Precision in the tool without modifying it in IDT.
Business need to change if they are to keep pace with the many customer interactions that are taking place online and offline. To do this, they need reliable data analytics that allow them to respond quickly to new demands. One solution is a data lake that receives continuously updated data from different sources. In this blog, we will briefly describe the difference between data lake and data warehouse, along with it’s advantages and disadvantages in business.
The data warehouse is an indispensable information base for traditional company reports or audit assessments in medium and large companies. Structured data collected days, weeks or even months ago is often prepared and analyzed in an ETL (Extract, Transfer and Load) process.
This is no longer sufficient to react quickly to ever-changing customer behavior. Another option is to develop a data lake model. Before companies decide to implement data warehouses, the specific characteristics, objectives and above all, advantages and disadvantages of data warehouses and data lakes need to be carefully analyzed.
First, we will look at how companies can manage the volume of data they collect on a daily basis. What can be deleted immediately? What needs to be stored permanently? What needs to be done?
For security reasons, some companies initially want to archive all data until it is clear whether it is relevant to the business strategy. This is where data lake come in. In this case, data is stored in its original form until it can be used.
Data lakes are scalable, can act as a kind of cache for data warehouses, and are a low-cost way to store files in any format. This is particularly attractive for less structured data such as documents, images, emails and audio files.
Data scientists with expertise in business management and statistics have long been studying data lakes and developing ideas on how companies can manage new data sets, for example at different customer touch points.
A data warehouse is a central location where information is collected from different sources in its original form and without further adaptation. There is no predefined structure for the location of data and data models are only the result of future scenarios.
However, data lakes also have their drawbacks. The unstructured nature of the data makes it difficult for companies to determine the necessary storage space and the most appropriate search tools for analyzing data from different systems and applications.
Another obstacle is the lack of experts to analyze unstructured data. They must first be trained or involved in the company’s staff and gain experience in initial projects.
In addition, integrating data from different sources is a challenge. In this case, it is advisable to test in small environments so that the results can be transferred to large and complex data sets.
Although data lakes are gradually entering the productive data analysis environment of specialized departments in companies, data warehouses are still the standard for evaluating data from relational databases and business applications. Typical use scenarios of data warehouses are classic business intelligence and analytics applications, used for example for corporate governance.
A data warehouse provides tools for reporting, data analysis and long-term archiving of key business data. To date, there is no standardized method for migrating large volumes of data between data warehouse systems. Solutions that are not optimally designed will not cope with the integration of additional database sources. Unlike data lakes, data warehouses are also used to store aggregated versions of the same data in the form of structured reports.
With the growing volume of data, especially less structured data, companies are concerned that data warehouses are not able to provide the scalability and flexibility they need. In addition, traditional data warehousing solutions are reaching their limits in handling large volumes of poorly or inconsistently structured data and require fast response times to ad hoc queries.
Data lakes will not be completely redundant for data warehouses in the near future. The two approaches complement each other in the decision-making process. In this way, companies can overcome the limitations of existing capabilities and discover new opportunities. While both views are valid in the business world, the evolving digital landscape increasingly shows that data lakes (synonymous with a modern data warehouse) are better suited to companies looking to take the lead.
Do you want to use your company data potential profitably? Have you already started projects, or are you in preparation? Or, as IT managers, can they provide the data or build an Analytic platform?
No matter from which perspective you deal with data, a basic requirement is always a clear strategy. For a start, the later course is a guideline and yardstick for the success of your projects.
In our view, strategy development includes the following eight aspects of success:
In contrast to many other IT projects, the use of data is often a cross-sectional task with many tasks to be solved and a large number of participants. For example, data from different systems must be collected, described, and cataloged to be evaluated by other areas. Technologically, the data from the individual systems must be collected, processed, and made available on analysis platforms. All of this must take place on a technically and legally secure framework. This automatically results in a large number of dependencies to be managed, which are crucial for the successful progress of your projects.
Determine who is responsible for developing and managing your data strategy. Find the right organizational framework for you to manage your projects holistically. Please provide a good mix of professional expertise and IT know-how.
In the beginning, it is about recognizing the potential benefits you associate with analyzing your data. These potentials are only sometimes clearly tangible or measurable at the beginning. Nevertheless, starting with reasonable initial goals is helpful to gradually evaluate them in the further course. Instruments for this are use cases or scenario techniques. You will get an overview of the potential benefits and objectives of using your data. On this basis, deciding what you want to focus on is advisable. The clearer and more concrete your vision, the faster you will achieve your goals.
The implementation strategy is closely linked to the data goals. In it, you determine the steps in which you want to achieve your goals. There are different models of thought here. Due to the i.d., An agile and learning approach is recommended for the high complexity of the projects. It helps to achieve quick and visible results. It is about more than one IT topic – the optimal collaboration of different actors on different areas and levels.
Keep all elements of data management in one central document for all stakeholders firm:
This data management plan is not a static document but a data record that is changed dynamically. Determine a person who is responsible for ensuring that the DMP is tracked continuously and that everyone is involved in the changes.
To ensure that compliance requirements are met, you should use data warehouse tools. These help you record, track and monitor personal data storage locations and connection chains. Make sure that the person responsible for compliance monitoring knows these tools.
The data acquisition level forms a layer above the data queries. It is comparable to the catalog of a library. Here the analysts are looking for suitable data records that they can use for evaluations. It is important that they can be used for evaluations. This search platform must include all memories, no matter where they are, and the storage model.
Invest enough time in defining and implementing suitable search criteria. Talk to the users. Find out what they are looking for, which analysis tools the data requires, and which metadata must be available.
Experience shows that many data projects need to be revised because the complexity is getting out of hand. Your investment can quickly develop into a bottomless pit. It is common for such projects to run for several years. Too many topics have to be solved at once. That overwhelms your organization.
The art is to think in small speed boats. How about leading one of your data-driven projects to the goal within a few months, thinking in clear components, and learning for subsequent steps? This procedure is scalable. In the next step, you can start with several speed boats if necessary.
You don’t have to be a data-whiz kid or certified analyst to leverage data successfully for better business decision-making. You do have to develop a plan, try out new strategies, and commit to prioritizing data as you move forward. If you’re willing to do so, good things will happen.
Would you like to use data more effectively for your company? The ExistBI experts analyze your requirements, support you in creating a data strategy, and implement your data storage solution. We offer various cloud and on-premises models. Contact us now for an initial non-binding consultation.
A data warehouse comprises several components and layers that need to be created to form a complete data warehouse system. The section Data Warehouse Components briefly describes the different subsystems of a data warehouse system.
In addition, examples are given to explain how raw data from business systems is fed into the data warehouse, processed graphically, and displayed on a dashboard or business intelligence portal.
Operational Systems, also known as Online Transaction Processing (OLTP), form the basis for data analysis in the data warehouse.
Operational data is generated and processed by management, planning, and accounting systems, also known as bottom-up systems.
Much of this business data is generated in so-called online transaction processing systems (OLTP), where multiple users share the same systems and databases in peer-to-peer functions, such as IT systems, reservations, and orders.
The data is periodically retrieved from OLTP systems and temporarily stored in relational databases or flat files.
The data from heterogeneous sources is sent to the workplace, where it is prepared and processed.
The workspace, also known as the data storage area, is where data is collected, stored, archived, and used for transformation.
The entire ETL process takes place in the data warehouse and involves the extraction, transformation, and loading of data into the data warehouse. Please note that data will only be uploaded after the above processes have been completed.
This has the advantage that neither the operating systems nor the data warehouse is affected. In addition, incorrect data will not be transferred to the data warehouse. After completing the process, the data is deleted from the workspace.
The ETL process is the basis for data warehouse systems. Although not directly visible to users, the performance of the ETL process should not be underestimated.
The ETL process is a vital part of the data warehouse. In large data warehouse projects, the ETL process is often used for monthly backups, weekly storage of subsystems, and daily loading of essential components.
ETL processes are responsible for that data from the source systems to target systems. Data is extracted, cleaned, logically prepared, and loaded into the target tables.
In many data warehouse projects, ETL processes account for up to 60-80% of the total project cost. As this process is located in the back end of the data warehouse, it is sometimes unclear to the end user how much work needs to be done.
The operational aspect of the data warehouse depends on the limitations of the data warehouse architecture.
The integrated database is only available for analytical queries. For operational data, heterogeneous data from different operating systems must still be provided.
The concept of an operational data warehouse was born from the need for integrated operational data.
Data warehouse architectures are typically designed as centralized, distributed data warehouses or with or without a central data warehouse, depending on the application and the specific business requirements.
Each data warehouse architecture has its advantages and disadvantages in terms of implementation, operation, and maintenance.
At the beginning of the data warehouse development process, it is essential to know which architecture concept will be used for further development so that developers and users understand the same.
Implementing a business data model in a central data warehouse often fails due to such large projects’ short- to medium-term planning.
This has led to the emergence of so-called data warehouses, as data warehouse systems tailored to the needs of a specific person or function can be created quickly.
Individual datasets need to be harmonized with each other to ensure the consistency of data models and can be extended to a central data warehouse if necessary.
In this case, brands have predefined views for each business case, for example, data warehouses for the audit department where all relevant critical corporate data is prepared.
Data warehouse dashboards are often used at the management level to present essential business information concisely and meaningfully.
This includes using red, yellow, and green in different strength or weakness situations.
As far as data warehouse systems are concerned, dashboards are seen as a complement to OLAP tools.
The main task of a business intelligence portal is to integrate different thematic contents in a graphical interface.
In the BI portal, content and functions are compiled thematically from the data warehouse.
The prepared content is made available to users through centralized access to individual domains and related information and services.
The high degree of individualization from business intelligence portals can be counteracted by suitable filtering, preparation, and structuring information overloading.
Data warehouses have long been used in industry. They usually support BI with batch processing or process historical data for analysis.
There are also many other developments in data warehouses for real-time data analysis or collecting and processing heterogeneous data structures.
The data warehouse concept and related technologies are the latest trend in the industry and represent an evolution of the enterprise data warehouse concept.
The ETL process in data warehouse is the basis for storage systems. Although not directly visible to users, the efficiency of the ETL process should not be underestimated.
The ETL process is a vital part of the data warehouse. In large data warehouse projects, ETL processes are often used as a monthly backup, a weekly load for subsystems, and a daily load for critical data.
ETL processes are responsible for transferring data from source systems to target systems. Data is extracted, cleaned, logically prepared, and loaded into the target tables.
In many data warehouse projects, the creation of ETL processes accounts for up to 60-80% of the total project cost. As this process is located at the back end of the data warehouse, end-user involvement is only sometimes apparent.
The following article describes the extraction, transformation, and loading elements in more detail.
ETL stands for Extract, Transform and Load and refers to transforming data, especially in a data warehouse environment.
The term ETL is also used in other software applications, such as self-service BI solutions, but has nothing to do with the concept and architecture of the data warehouse.
Only the data conversion and transfer process is based on the same principle.
There are different views of the ETL process in the data warehouse. This aspect is related to the architecture of the system. The data warehouse can be built with three or four layers. Therefore, the transformation takes place in the cleaning layer in the processing phase, while the extraction is first performed in the moving phase.
Extraction is the first step of data processing. In this process, data is extracted, i.e., extracted from source systems or documents and made available for further processing steps in the input data warehouse layer. Only certain aspects of the raw data can be used. Unnecessary data shall not be exported.
In the second step, the data is transformed at a step or cleaning level. Here the raw data types are converted into column types for the target tables. In addition, the content of the data is checked. For example, duplicates are identified and eliminated, calculations are performed, or additional data is merged.
In the third step, the data is transferred to the data warehouse. There it is organized and normalized. Some of the data is also historical, so changes over time can be monitored and evaluated.
In data processing, the conversion and loading phases are interchangeable. The data is transferred to a central database and then converted using unique algorithms.
This process is widespread in big data environments where data is collected and then made available for analysis. There are no special layer normalization procedures as in traditional data warehouses.
The ETL extraction process defines the conventions for source system connections and the types of data transfers.
In addition, a data update schedule is defined. When updating databases, a distinction is made between synchronous and asynchronous extraction:
In a synchronous query, the validity of the data is always guaranteed and updated. The disadvantage is that it requires more resources to use the network.
In asynchronous offloading, data is replicated to save resources, i.e., when resources are sufficient.
These operations are usually shifted to day or night to cause as few problems as possible. A distinction should also be made between static and incremental recovery:
A static recovery creates a complete image of the database. This process occurs when a particular system state is interrupted or restored for the first time.
Incremental recovery reads only the changes between the current and last recovery steps. These differences are highlighted in the operational log.
The second phase of the ETL process is the transformation process. In this phase, data from the different source systems is transferred to a defined internal format.
Besides eliminating structural differences, we also focus on contextual differences.
During the data transfer process, information is aligned to standard data formats, transformed or transcoded, time and line normalization are performed, and units of measurement are converted.
In addition, erroneous, redundant, obsolete, or incomplete values in the databases are corrected according to a correction program.
The ETL loading process loads data from a data warehouse directly into a data warehouse or possibly into an operational data warehouse.
Since no evaluation or analysis can be performed during the data warehouse loading process, the data warehouse is locked during the loading phase.
Updates may replace old data or be loaded into the data warehouse as new data records.
As we have learned the most important concepts about the ETL process in a data warehouse, you should know the extraction, transformation, and loading in an ETL process. If you want your organization to maximize the value of your data, it’s time to implement the ETL process in DWH.
A data warehouse describes a data storage platform that needs to be assessed against specific standards. In this context, the primary data warehouse is often used to refer to the actual process. A data warehouse architecture covers the entire data analysis process. Within the data warehouse process, the data warehouse is managed and evaluated in four steps:
1. Extract relevant data source systems and transform and transfer them to the data warehouse.
2. Long-term archiving of the data in the data warehouse.
3. Supply and data storage of the data required for the current queries.
4. Analysis of the respective data or the supply of downstream application systems.
The first is a functional database containing, for example, relational information. This is followed by a storage area where data is predefined. Special ETL (Extract, Transform, Load) procedures are used to transfer the data to the data warehouse, where the information is organized and collected.
A data warehouse is, therefore, a form of data storage that operates in parallel with operational data warehouses. This separation is implemented in a way that does not interfere with normal search processes and allows separate access to data.
Several tools are used to access the data in the data warehouse at this stage. Access can be provided at different levels, called data tokens. Data warehouse systems primarily work with relational databases, which can be read using a structured query language.
For example, the most common format for evaluating the data is pivot tables from excel. OLAP databases are used to structure the data as the number of data increases. They can compress data of different sizes and create hierarchies. An example is the query of turnover by production area and production zone. However, the data storage systems must be used correctly. Many user problems are not caused by the system but by poor data quality or lack of technical documentation.
Excel spreadsheets, for example, are among the most popular data formats. As data volumes increase, OLAP databases are used to structure the data. However, the data storage systems must be used correctly. Many user problems are not caused by the system but by poor data quality or lack of technical documentation.
The purpose of data warehousing systems is to provide the company with an overview of the data it holds and to evaluate them. Four factors are necessary for this:
All data relating to the analysis should be stored in the data warehouse.
The data of the warehouse and the operating system must be managed separately. The data in the data warehouse are used for a broad search. To ensure that the operating system stays intact, it should be separated from the analytical data.
Most sources provide data formats that ETL procedures can read. The type of data format determines which databases and read-out systems are used.
Data is stored over time. This allows data to be evaluated over time. This is impossible in operational databases, as information is overwritten several times.
– Proven architecture
– Area-specific tools
– Optimal data quality
– High data integrity
– Cannot process unstructured data (video, audio files).
– Long response time for ad hoc queries
According to blogger and IT expert Bill Inmon, data warehouses are a fixed system architecture, while big data is a technology. Ultimately, both are systems or methods of data analysis. Data warehouses specialize in analyzing general and structured information from SQL databases and can be used with various tools and optimization techniques.
On the other hand, extensive data analysis is not dependent on a fixed system database and is more flexible. It includes several tools for evaluating unstructured data, which are becoming increasingly important in the market.
In addition, big data analytics is beneficial for processing large amounts of data without increasing load time or reducing performance. As big data analytics is still a very new field, some analytical methods and evaluation tools still need to be fully developed.
While a simple architecture for data warehouses can undoubtedly be used as a blueprint, many things must be considered individually and thus modified. Whether internal or external data sources, whether full extract or incremental, whether raw dump or transferred directly to the warehouse, whether high volume or high variability, depending on the requirements you place on the data warehouse, a suitable architecture must be selected.
A data warehouse is a database that records structured data for further processing. The most common areas of application for the data are reporting, business intelligence and analytics.The aim of a data warehouse is to provide high quality data as easily as possible in order to simplify subsequent analysis steps. In this article, we go into more detail about the roles and uses of data warehouse in everyday life.
The idea of a data warehouse is based on the use of data beyond operational purpose. As a result, there are many people who should deal with a data warehouse or its use:
The heart of a data warehouse is one or more business intelligence experts. You know the structure, documentation and application of the warehouse and organize the connection of new data sources. They are often directly responsible for creating reports and the like.
While BI experts represent a transverse function, the added value of a data warehouse in the domain, for example sales, marketing or generated logistics. Domain experts are therefore responsible for connecting the correct data and generating the correct evaluations.
If you bet on an SQL database, you need technical support from IT.
In addition to the general BI expert, there are often also responsible data analysts in the domain or a central unit who take care of the evaluation of the data. It is important to combine domain knowledge with data expertise in order to generate actionable insights if possible.
One of the most common origins of business intelligence is a company’s controlling department. Therefore, they are often closely involved in a data warehouse.
If a company is very knowledgeable in the use of data and dashboards, the visualization itself – i.e. the creation of the dashboard, may even be outsourced to experts.
Another type of consumer for data from the data warehouse are data scientists. If the data is available in a very high resolution, data mining can be used and new knowledge is worked out.
If you rely on a cloud solution, it has to be built up and maintained. Whether cloud solution engineer or data engineer or data architect. A very valuable role to ensure data flows and keep the infrastructure running.
With the development of the databases and the digital storage of data, the first step was to establish connections between this data. The flood of data is getting bigger. More and more data is being collected. In order to be able to evaluate these data masses in a targeted manner, they must be related to each other.
Data warehouses develop their great strength here. Without data warehouses, an overall analysis would not be possible, the evaluations would be limited to individual databases.
A data warehouse executes the data from different sources together and provides logical connections between all this data. To do this the data does not necessarily have to leave its source location. They can also be available as a linked data connection in the data warehouse.
The areas of application for data warehouses are diverse. No matter in which area the application takes place, it has always been so far hidden connections found such as data mining. Reports, statistics and key figure assessments are quickly available and flexibly adaptable. Nothing stands in the way of a transparent and comprehensive presentation of complex relationships.
In short, the data warehouse provides all related information transparently and clearly represented.
Data warehouses enable personalized medicine. For example, with the help of gene sequencing algorithms on the genetic material used by blood samples. This can create a medicine based on the genetic, individual virus strain of the data subject.
In addition, the risk of heart attack or genetic defects and the resulting diseases can be determined more easily. The data warehouse establishes connections between the patient record, the image material from radiology and the laboratory results.
First, data warehouses allow us to measure the image of companies and products in a fineness and depth, as was simply not possible before. Representative market images are generated inexpensively using sentiment analysis on social media. In this way, it is determined which product trends arise or are sustainable. Companies can react quickly and flexibly.
Also airlines benefit from data warehouses also by linking data. So can down times and waiting times be assessed much better. Delays are minimized. Unprofitable routes can be easily found through the analysis and can be deleted. The jet fuel consumption based on route, aircraft type and payload can be optimized based on the data from the data warehouse.
Energy suppliers increasingly rely on data warehouse solutions. Consumption recording and billing provide data in the data warehouse. In this way, targeted analyzes can be created according to categorized target groups. Even the evaluation of consumption data in different regions, by age group, gender or household type are possible.
Store and analyze information about faculty and students. Maintain student portals to facilitate student activities. Extract information for research grants and assess student demographics. Integrate information from different sources into a single repository for analysis and strategic decision-making.
As exhibited, a data warehouse ensures improvement in the decision-making process of a business and increases organizational performance. Want to learn more about it? Let’s see how our Data Warehouse Consulting Experts can make it work right for you!
When working with databases, it is important to create optimized data models if storage efficiency, performance and fast searching are the goals. Star vs Snowflake schema are the two most commonly used schema when modeling multidimensional data spaces.
Here you can understand how the star and snowflake schema differ from each other and which schema is best suited for a data warehouse application.
The schema takes its name from the fact that the measuring tables on the outer sides are arranged in a star shape around the event table in the middle.
This data model does not focus on standardization, but on improving the efficiency of reading management. This has its own drawbacks, such as the possibility of discrepancies. There is usually a trade-off between performance and storage requirements. In the case of the star schema, the focus is on performance and therefore the resulting databases are more memory intensive.
In practice, the star schema is widely used in data warehouse applications. This is because the schema is simple and straightforward. It can be adapted or extended as needed to meet changing needs. This is not possible to the same extent as with Snowflake.
If necessary, the star shape can be extended to a snowflake shape. The transition between the two approaches is seamless. This requires the creation of new tables with independent attributes to form the third normal form (3NF). The star shape can be extended with additional event tables. In this case a so-called galaxy schema is created.
An important difference between a star schema and a snowflake schema is that in the latter, each dimension of the pattern has its own table.
This avoids the redundancy inherent in the star schema. The result is more compact and better structured data sets. This is a trade-off between redundancy and complexity. Anomalies with the snowflake schema are avoided, but the data set is more complex.
Creating multidimensional arrays quickly leads to a highly branched, snowflake-like structure.
In this case, aggregation queries must be found to reconnect the resulting tables. A major drawback of the system is therefore the longer search time.
In practice, it is often not possible to distinguish clearly between the two approaches. This is due to the large number of variants that exist for both models.
Practitioners wonder which schema is best suited for analytics tasks in the data warehouse. If you want to simplify SQL queries, you should definitely prefer the star schema. Since there are fewer foreign keys, queries can be executed faster. Overall, the structure is simpler and therefore more user-friendly.
If you have problems with duplicate data in your analyses, the snowflake schema may be a better choice. This is because this model is normalized. However, in SQL queries, it has to bear the disadvantages of large concatenated strings.
Those who do trend analysis or sales forecasting prefer to use OLAP modeling. The advantages include the fact that it requires little work to selectively query data and execute queries. Web analytics allows you to look at data from different perspectives. Data warehouses optimized for data analytics are therefore often based on OLAP processing.
To use OLAP, data must be available in a multidimensional structure, for which star and snowflake schema have been developed. OPAL is therefore closely linked to these models.
Generally speaking, the star schema is a good model to use if you want a standardized and localized data model that is easy to follow. If you are working with one event table and going through several dimension tables, this schema is the right choice.
Since the star schema is suitable for Power BI, this approach is the one to use when working with linked report views. This is because Power BI models are based on fast queries that support the star schema.
On the other hand, if you need a more specific schema for your modeling task, the snowflake approach may be more appropriate. This schema has advantages in data analysis, especially when dealing with multiple entities and relationships. As always, it all depends on the priorities and needs for which you choose the type of schema that fits you best.
Contact our data warehouse consultant to get even more information about the star schema and snowflake schema to multiply the benefits for your business.
For a few years now, two classic approaches to data warehouse modeling have been in the spotlight: the Inmon approach based on standardized object modeling and the Kimball approach based on star modeling, where the enterprise data warehouse is built by matching sizes and using master tables.
Although this method is less popular than the other two, there is a third option: data warehouse modeling, which was popularized in the early 2000s by its author, Dan Linstedt. Data warehouse modeling is a hybrid approach between Inmon and Kimball.
In the following articles, we will go into more details on the motivations to leverage data vault approach for a modern data warehouse modeling.
Data Vault is a data warehouse and business intelligence methodology that provides a comprehensive and flexible approach to data management. It was developed by Dan Leinstedt in the late 1990s when he recognized the need for a more efficient way to collect, store and access data.
The data warehouse is based on the concept of hubs and spokes. Hubs are master tables of important business information, while spokes are the detailed data used for decision making. The modular architecture makes it easy to add or remove data as needed and to quickly update the BI solution.
Data Vault methodology is used by companies of all sizes because it delivers on the promise of simplicity, scalability and flexibility. Data Vault is ideal for organizations that need to load large amounts of data into a data warehouse quickly and efficiently.
Data Vault is ideal for organizations that need to load large amounts of data into a data warehouse quickly and efficiently. It is also a good choice for businesses that need a flexible and scalable solution to meet changing business needs.
Data Vault offers a more flexible and efficient approach to creating data warehouses than traditional methods such as dimensional modeling. It is also easier to implement than many other data management solutions. As the demand for faster and more reliable access to data increases, so does the need for data warehouses. Data Vault is the next generation of data archiving, offering a comprehensive and flexible approach to data management. If you’re looking for an efficient way to collect, store and access your data, you’ve come to the right place.
The data vault offers a number of benefits, including
The Data Vault model has some disadvantages, including.
Despite these drawbacks, data warehousing is a popular choice for many organizations because the benefits often outweigh the drawbacks. Data Vault is a flexible and scalable platform for storing and managing large amounts of data. With the necessary automation tools, Data Vault is easy to use, making it a good choice for companies looking to build a reliable and scalable data warehouse.
Data Vault is the next generation of data storage tools. It simplifies the creation and maintenance of data warehouses and makes it easy to create accurate models from any database or data source.
Creating a data warehouse has never been easier than with Data Vault. This comprehensive tool helps you build an efficient data model that meets all data warehouse best practices and can be quickly extended and customized to meet your unique needs. Call our data warehouse consultants today.
Big data is increasingly diverse data, generated in ever-increasing volumes and at ever-increasing speeds.
In short, big data is about larger and more complex data sets, especially from new data sources. These data sets are so large that they cannot be processed by traditional data processing software. However, these big data sets can also be used to solve business problems that could not be solved before.
The size of data is important. Processing large amounts of data requires processing large amounts of unstructured, low-density data. This can be data of unknown value, such as Twitter feeds, click streams from websites or mobile apps, or sensor data. For some companies, this can mean tens of terabytes of data. For others, it could be hundreds of petabytes.
Speed is the velocity at which data is received and processed. Data is usually fed directly into memory at full speed and not stored on disk. Some internet-connected smart products operate in real time and require real-time evaluation and response.
Variety refers to the many types of data available. Traditional data types are structured and fit well into a relational database. With the advent of large datasets, new types of unstructured and semi-structured data, such as text, audio and video, require additional pre-processing to extract meaning and preserve metadata.
In recent years, two additional elements have emerged: valuability and reliability. Data is valuable in its own right. However, it is only useful if this value is discovered.
Today, big data has become an asset. Take a look at some of the world’s biggest technology companies. Much of the value they offer comes from the data they constantly analyze to make their businesses more efficient and develop new products.
Recent advances in technology have exponentially reduced the cost of storing and processing data, making it easier and cheaper than ever to store large amounts of data. Because big data is cheaper and easier to retrieve, more accurate business decisions can be made.
93% of companies consider big data initiatives “very important”. With a big data analytics solution, companies can unlock strategic value and make the most of their resources.
Finding value in big data is not just about analyzing data. It’s a holistic discovery process that requires analysts, business users and managers to ask the right questions, identify patterns, make assumptions and predict behavior.
It helps organizations:
Companies can use big data to learn what customers want, who their best customers are, and why they choose certain products. The better a company knows its customers, the more competitive it is.
Big data can be used to understand what customers want, who their best customers are and why they choose certain products. The better a company knows its customers, the more competitive it is.
This, combined with machine learning, can be used to develop marketing strategies based on customer predictions. By using big data, companies can become more customer-centric.
Companies can use real-time and historical data to assess customer preferences. This allows companies to improve and update their marketing strategies to better meet customer needs.
Let’s look at why big data is so important.
The value of big data does not depend on how much data a company has. It is how the company uses the data it collects.
Every company uses the data it collects differently. The more efficiently a company uses its data, the faster it will grow.
In today’s market, companies need to collect and analyze data.
Big data analytics helps businesses better understand market conditions. For example, analyzing customer buying behavior helps businesses identify which products sell best and manufacture them accordingly. This allows companies to stay one step ahead of their competitors.
Customers are an important resource on which all businesses depend. No business can succeed without a strong customer base. But even with a strong customer base, companies cannot ignore the competition in the market.
Not knowing what your customers want will affect the success of your business. This will result in a loss of customers, which will have a negative impact on the growth of the company.
Big data analysis helps companies identify customer-centric trends and patterns. Analyzing customer behavior leads to a profitable business.
Microsoft Azure Synapse Analytics, Apache Hadoop, Spark and other big data tools are an advantage for businesses when they need to store large amounts of data. These tools help businesses to define more efficient business processes.
In-memory, real-time analytics helps businesses collect data from multiple sources. Tools like Azure help them analyze data instantly and make quick decisions based on it.
Companies can use big data tools to analyze sentiment. This can give them feedback about their company, i.e. who is talking about it and what they are saying.
Companies can use big data tools to improve their online presence.
Big data analytics drives all business processes. It enables businesses to meet customer expectations. Big data analytics helps a company transform its product offering. It enables effective marketing campaigns.
Big data analytics has roots in finance, banking, healthcare, education, government, retail, manufacturing and many other industries.
Many companies such as Amazon, Netflix, Spotify, etc. use big data analytics. Big data analytics is most commonly used in the banking sector. In the education sector, big data analytics is also used to improve student performance and help teachers deliver lessons.
Big data analytics helps retailers – both traditional and online – to understand customer behavior and offer products that match their interests. This helps them to develop new and better products, which is very useful for businesses.
We are seeing how big data is helping businesses to make informed decisions and understand customer needs.
By analyzing data in real time, it helps businesses grow quickly. It enables businesses to stay ahead of the competition and succeed.
Big data technology helps us identify inefficiencies and opportunities in our business. It plays an important role in business development.
The ETL and ELT are databases used to extract, transform and load data into warehouses. Changing one of these processes can completely change the final product.
Modern analytics processes large volumes and different types of data, which is slower with ETL than with ELT.
The ETL process is therefore a novelty in the data world. It is a scalable, modern and flexible approach that allows today’s businesses to compete in the market.
Find out what really changes in data management by moving redundancy from ETL and ELT.
ETL is a data pipeline that combines data conversion processes in three separate steps:
The ETL process is traditional and familiar to those in the fiend.
In the extraction stage, data is collected from various sources such as spreadsheets and CRMs. After extraction, the data is converted into a format that is available for analysis. Finally, it is transferred to a data warehouse where it is stored and made available for quick use.
The main purpose of ETL is to collect relevant data, prepare it for use in reports and archive it for easy access and further analysis. This process allows experts and developers to focus on other tasks.
Unlike the more traditional and popular ETL, ELT is an extension of this process, making it more flexible by changing the data conversion steps.
In this data management model, the steps are arranged in the following order:
This simple inversion, for example, reduces the data loading time and allows company experts to work with the information directly in the data warehouse without the need to employ high-tech experts such as programmers and data engineers.
This allows a better division of labor: data engineers deal with the extraction and loading phases, while experts more familiar with business rules, such data scientists and analysts, deal with the other phases.
Apart from the division of labor, the reverse process from ETL to ELT has other consequences for the final product.
ETL and ELT are data lines used to export, transform and load data into a file. Changing just one of these processes can completely change the final product.
In ETL, the conversion time increases significantly as the amount of data increases.
In contrast, in ELT, the transformation phase is faster because the cloud infrastructure technology is used. In this case, the speed does not depend on the size or complexity of the data.
The maintenance time of ETL is high because it requires the regular work of expensive and rarely used specialists such as IT specialists or programmers to update the data set.
With ELT, the scenario changes as the data is always ready and available for use in the data warehouse.
With ETL, data is loaded only after the conversion, so each step requires a different device, and the execution takes longer because the load process must be repeated for each data conversion.
In ELT, data is loaded once into a data warehouse, where it is converted for use.
ETL is primarily used by IT professionals, programmers, engineers and computer scientists using spreadsheets and fixed
On the other hand, ELT is scalable, flexible and interoperable and can be used by both technicians and end users.
ETL initially requires less storage space.
ELT requires a thorough knowledge of modern tools for advanced analytics and a well-structured data warehouse architecture.
ETL is designed to support relational databases, local databases and legacy systems.
The ELT, is designed to handle large amounts of data, structured or unstructured, and multiple data sources in a scalable manner across cloud infrastructures.
For SMEs, ETL is not necessarily a cost-effective approach due to the factors mentioned above, such as high operational costs.
As ELT is scalable, configurable and affordable for businesses of all sizes, it is a much more cost-effective, economical and modern solution.
Regardless of the size of your business or how you create value from your data, a data management system is critical to the success of your business.
Contact Existbi’s team of experts to implement ETL or ELT processes for your business, depending on your needs.
ExistBI is technology- and vendor-neutral, which means that your interests always come first and that you can work with a data warehouse consulting service provider to design and implement the best solution for your specific needs.
Data warehousing is undoubtedly essential for a company that wants to maximize its results and is now gaining an analytical advantage. If you have studied data analytics, you have probably heard about data warehouses and the benefits they can bring to your business.
If you’re still not convinced, you need to learn more about data warehouses. So we’ve outlined the key concepts and shown you how they can impact your business performance.
The most basic concept of a data warehouse is one of the most important from a business perspective. It is a special type of database optimized for online analytical processing (OLAP).
DWH is created by pooling all of a company’s data sources for analytical purposes.
This means that a properly implemented data warehouse brings all data sources together in one place. This will allow marketing, sales and production teams to work with the same data, as the implemented data warehouse becomes a single source of reliable information for the company’s business decisions and forecasts.
Having a data warehouse as the single source of reliable information is a concept that ensures that everyone in the company makes business decisions based on the same data.
After all, there’s nothing more frustrating than when the numbers provided by the marketing department don’t match the numbers in the hands of the sales department, right?
Internal data is only valuable for decision-making if it is credible to all stakeholders in the company. This is why it is so important to understand the value of aggregating data in a data warehouse.
A data warehouse is undoubtedly essential for a company that wants to multiply its results and now has an analytical edge.
The functions and applications that define data warehouses are numerous and are linked to the architecture, the type of data stored, the way it is used, and the way data is extracted, loaded and transformed.
If all these steps are properly implemented and controlled, excellent results can be achieved.
Let us look at some of the following basic and very advanced concepts that will certainly help you to better understand the data warehouse.
A data warehouse integrates data from different business sources. For example, source A and source B may identify product X in different ways, but there is only one way to identify the product in the data warehouse.
In a data warehouse, an action can be a property of a computation and a set of actions can be an event table. In practice, for example, a sales order table and other production orders are event tables.
Historical data is stored in the data warehouse to explain trends in the data over time. For example, even if the price of a product changes, the data warehouse stores all historical changes in the unit price of that product.
These are the characteristics of the data warehouse that allow events and activities to be classified, which in turn allows activities to be analyzed and reported. The dimensions can be, for example, the company’s customers, data, suppliers or products.
A data mart is a subset of a data warehouse, for example, a data warehouse that focuses on a specific business area or segment. For example, there may be one data warehouse for production, one for sales, one for maintenance, and one for quality.
ETL is a variation of the ETL (Extract, Load, Transform) process that extracts raw data from various business sources and loads it into a data warehouse. The raw data is then transformed as required for use in analytical processes.
Once data is entered into the data warehouse, it does not change.
All of these concepts are used in the deployment of a modern data warehouse to automate business processes and achieve a single goal: to deliver high-quality results across the enterprise.
The data warehouse actually contributes to this by ensuring the integrity and quality of the data it contains.
A data warehouse can only be an asset to a company if the data it holds is reliable and of high quality. No one will generate ideas and make decisions based on unreliable information. At least, they shouldn’t.
All the data warehouse concepts we have discussed ensure that certain processes, such as data cleansing, data transformation and integration of different business areas, are done in one place.
In this way, the company’s data analysis provides important information for decision making in all areas.
Do you know how important investing in data warehouse consulting services is to the success of your business?
A data warehouse consultant uses the best and most advanced data warehouse tools available on the market and has a team of professionals who are experts in the field.
Whether you are a data science expert or a beginner, one thing is for sure: data warehouses can take your business to the next level.
Would you like to know how to create a data warehouse to fuel your business growth and success?
Start by contacting ExistBi’s Data Warehouse Consultant and let them create a data-driven strategy that will help your business achieve irreversible growth.
Data Warehouse Tools are an integral part of Big Data and Data Analysis. It is an intelligent data warehouse that supports analytics applications and allows users to analyze data to gain a competitive advantage.
Data warehouses are typically located between large data repositories (e.g. databases) and data marts. This software often used in conjunction with ETL tools to create a variety of reports and analyses, from BI to predictive analytics.
Data warehouses also offer businesses with improved access to information, reduced query response times and insights into large data sets. Until now, companies have had to invest in infrastructure to build a data warehouse. Today, cloud technology has dramatically reduced data storage costs for businesses.
Data warehouse is now fast, scalable and application-based. Fortunately, there are many data warehouse tools with powerful features that are trusted by hundreds of companies around the world. This article will help break down the concept and and uses of data warehouse software solutions for you and give you a detailed description of 10 best data warehouse tools in 2022.
A fully managed cloud storage solution, Amazon Redshift scales from hundreds of gigabytes to a petabyte or more. It allows users to upload and analyze any amount of data. No matter what the size of the data set, Amazon Redshift provides high query performance with SQL-based tools and popular BI solutions. Amazon Web Service also offers multiple cluster management options depending on the user’s skill level.
Microsoft Azure Synapse is an analytics service designed for data integration, data warehouse and big data analytics. The tool provides users to feed data into dedicated or server less sources. Azure Synapse provides a unified experience for collecting, extracting, preparing, managing and using data for business intelligence and machine learning. It also offers advanced security and privacy features such as column and row-level security and dynamic data protection.
IBM Db2 is a pre-configured, customer-managed data warehouse that runs in private clouds, and other container based infrastructures. Db2 has built-in machine learning, auto-scalability and analytics. It also offers scalable deployment, allowing users to write an application once and deploy it to the right location. In addition, it provides further key capabilities including fast query processing, support for PDA, Oracle and integration with the Apache Spark engine.
SAP Cloud is a data warehouse service built on the SAP HANA cloud database. It combines real-time data from different cloud-based and on-premises warehouses and stores it in business context. The software allows data modeling, visualization and sharing in a controlled environment. This includes pre-defined data models, semantic visualization of data from SAP applications and transformation logic, and leveraging vendor knowledge across the partner ecosystem.
Snowflake is a cloud-based data warehouse built on Amazon Web Services. Snowflake reads and optimizes data from almost any source, structured or unstructured, including JSON, Avro and XML. Snowflake has extensive support for standard SQL, allowing users to perform complex updates, deletes, parses, transactions and merges. This tool requires no management or infrastructure. The column based database uses advanced optimization techniques for data processing, reporting and analysis.
Tableau server is a web-based data warehouse available in desktop, server and web version. Tableau is a secure, shareable and mobile ETL data warehouse solution that is increasingly becoming the leading data warehouse tool. It allows technical and non-technical professionals to answer business questions and extract value from data in seconds. Tableau is easy to understand, but companies need expert training to make any investment successful. To learn more about Tableau, sign up for Existbi’s Tableau Training.
Informatica PowerCenter by Informatica, Inc. is designed as a data integration tool that allow you to combine and export data from multiple sources. This software is one of the most widely used ETL tools in the world. ETL stands for Extract, Transform and Load. This process is used when creating a data warehouse. Unlike other applications, PowerCenter has a number of components to help you extract data from different sources, transform it to meet your business requirements and load it into the right data warehouse. To learn more about Informatica’s advanced and modern data warehouse tool concepts, visit the Existbi with Informatica PowerCenter training course.
Databricks allows all data, analytics and artificial intelligence on a single data platform. Databricks with Data Lake combines the benefits of data warehouses and storage in a lakehouse architecture, providing a single platform for data, analytics and AI to work together. The tool is designed as a next-generation, scalable platform powered by Apache Spark, providing an interactive workspace for collaborative data analysis and visualization, freeing you from the burden of managing and building your own production platform.
Talend cloud services efficiently solve all your data integration and integrity challenges, whether on-premises, in the cloud, at the source or at the endpoint. Deliver reliable data to all users, when they need it. Simplify and accelerate the import and integration of data, applications, files, events and APIs from any source, anywhere, through an intuitive, code-free interface. Improve data management and compliance with a fully collaborative, integrated and consistent approach. Make informed decision based on trusted, high-quality data from batch and real-time processing with leading data cleansing and enrichment tools. The tool provides greater value by making data available to internal and external users. Talend’s built-in self-service features simplify API creation and increase customer engagement.
Microstrategy is one of the most comprehensive decision support solution designed to meet the needs of large and complex organization. However, it requires dedicated learning process and professional training. It is a strategy for managing large amounts of data. It supports the integration of multiple analytical tools. Over the years MicroStrategy introduced many improvements such as adding dossiers instead of standard reports. This allows users to customize different views and templates for easy of use.
There are many different data warehouse tools available for achieving potential business information. It is therefore important to consider meeting a data warehouse consultant, who can help you determine the requirements of your company before choosing the best data warehouse tool. As data archiving is very important for any business, you need to choose the right solution. We hope this article has helped you understand the best part of the most popular data warehouse tools of 2022.
Microsoft is constantly introducing new products. Azure Synapse Analytics has been available since the end of 2020. In this blog, we’ll help you understand what the Azure Synapse Analytics is and for whom the tool worth using.
Azure Synapse Analytics is a solution developed by Microsoft for integrating, analyzing, transforming and storing large amounts of data. When you create an Azure Synapse Analytics product in Azure, you create a Synapse Workforce and storage.
Synapse Studio is available through the Synapse Workspace website and is configured, analyzed and deployed through a web browser.
Microsoft offers a wide range of capabilities in Azure Synapse Analytics cloud. In this article we want to introduce Azure Synapse and show you who should use it.
Azure Synapse is Microsoft’s cloud-based analytics service that allows you to integrate, combine and analyze data from any source using different methods to draw conclusions.
Synapse Analytics allows you to extract data from multiple data sources by aligning pipelines. Pipelines are designed for integration and can be created without programming. Sequential operations are used to integrate data from different sources and map it into some kind of data stream. In this way, data can be queried locally and in the cloud. In particular, the connection to a high frequency data stream and the processing of input data can be done with different priorities.
SQL pools can be used both to store data and to create data lakes. Databases have the advantage of storing data in native formats that can be used by different technologies. Especially for very large data sets, traditional SQL methods may reach their limits.
In addition to SQL technologies, open source software’s and tools such as Apache Spark can be used for data analysis. Microsoft is therefore opening up its technologies and providing powerful big data processing and machine learning capabilities such as Spark.
In some cases the benefits are obvious,
Most SMEs are still using their own on-premises systems, possibly with some cloud or SaaS (Software as a Service)applications. Big data is still a long way off, so analysis is done on an ad hoc basis, using reports or even a small data warehouse.
Is it worth using Azure Synapse in these businesses? From a technical point of view, of course it is. If there is no cloud strategy and if the above criteria do not apply, the usefulness of cloud analytics is low. If analytics is the only data in the cloud, the disruption to other organizations is too great to be beneficial. In this case, existing technology is sufficient (although it can probably be optimized).
If you already have a cloud strategy in place and want to use more data or even machine learning capabilities, you may want to look at Azure Synapse. If you’re already familiar with Azure, you can quickly create an Azure Synapse workspace that you can use to prove concepts, for example.
Azure Synapse Analytics is filling the gap in the world of data warehouses. Lightweight, even when it comes to big data. It’s easy to get started with and can meet even the most demanding requirements.
The web interface is transparent, highly functional and generally impressive. Unfortunately, Microsoft does not provide enough detail on the individual fields and settings, so trial and error is inevitable. Some error messages are difficult to explain and not very helpful.
The practical implementation is very entertaining. It is therefore worth trying out the problems on a large scale to get a feel for the speed. The ability to solve problems in a different way is modern and exciting. Experience plays an important role in this and prevents the wrong approach to implementation.
Azure Synapse Analytics is the missing piece between multiple data sources and end-user reporting. It reduces the complexity of implementing multiple data sources and ensures system stability.
Microsoft Azure Data Warehouse is a cloud platform that allows you to develop and deploy applications and services using compute and storage resources in Microsoft-managed data centers. The platform supports software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS).
Azure Data Warehouse can handle large amounts of relational and non-relational data. It is compatible with SQL Server and local SQL servers can be easily migrated to a SQL data warehouse with similar queries and structures. Clients can scale up, down and out of data warehouse resources instantly.
If you’re not familiar with Microsoft Azure Data Warehouse, read on to better understand.
Azure is one of the largest cloud providers on the market and is used by many companies across all industries. The most common uses of Azure are:
Azure Data Warehouse is designed for enterprise data warehouse deployments and stores large amounts of data in Microsoft Azure. It uses MPP to manage analytical queries, enabling the rapid delivery of search results across large data sets. It also uses a single SQL-based view for relational databases and non-relational big data warehouses, enabling organizations to consolidate structured, unstructured and streaming data into a cloud-based data warehouse. Users can manage Azure Data Warehouse using SQL Server Management Studio (SSMS) or write queries using Azure Data Studio (ADS).
Azure Data Warehouse uses PolyBase to route queries to large data warehouses such as Hadoop systems. PolyBase enables organizations to import data into a SQL data warehouse using standard T-SQL queries, providing a single interface for SQL-based queries for all data. Azure Data Warehouse stores data in relational tables using columnar storage type, which reduces data storage costs and improves query performance.
The Azure Data Warehouse uses an extended architecture to distribute data processing across multiple nodes. The Azure SQL Data Warehouse architecture separates compute and storage functions, allowing users to extend the system independently and pay only for processing and storing data that meets business needs.
Small, medium and large enterprises are increasingly using the cloud. This is due to the affordability, convenience and speed of the service, making it easier to adopt and use. One of the easiest services to host in the cloud is file storage, for which Microsoft Azure is one of the most efficient and widely used platforms today. Below, we’ll describe the different types of cloud storage and show you how to use them.
BLOB storage is used to store data:
Many devices can use BLOB storage:
This file type offers some options for working in the cloud. If you need to sync your cloud files with local servers, you can easily share files via SMB protocol and work efficiently, easily and quickly without a VPN.
Disks are managed storage volumes used by virtual machines. Managed disks connect to virtual machines in the same way as physical disks. Disks in the cloud offer a number of advantages:
Disks are attached to a virtual machine and contain operating systems; data stores or applications can be added.
The entire hard disk is mounted to protect data against corruption.
Azure offers a high level of scalability. You can quickly create and remove Azure databases as needed.
Azure SQL has a number of security components (row-level security, data masking, encryption, auditing, etc.) Given the cyber threat to data security in the cloud, Azure Data Warehouse components are strong enough to protect data.
Azure Data Warehouse is extremely flexible because it separates the compute and storage components. Computations can be scaled independently. Resources can be added and removed at runtime.
Users can search non-relational sources using PolyBase.
You can easily migrate from SQL Server to Azure SQL and vice versa using Microsoft tools.
Looking for a way to consolidate data sources across different current environments and cloud types? ExistBi’s Microsoft Azure Fundamentals course provides participants with the skills needed to take advantage of the ever-expanding range of cloud services in the Microsoft Azure platform. The benefits of integrating Azure Data Warehouse with ExistBi include:
The conclusion is that an organization can take advantage of the cloud and store data securely using different storage types. Today, the cloud is increasingly available as a comprehensive, reliable, fast and easy-to-use solution. These solutions are available over the Internet from anywhere in the world. Whatever your business, you can find the perfect solution.
When it comes to cloud data warehouse, you don’t want to be left behind. It’s more than a trend – it’s a solution. So it’s no wonder more and more businesses are switching to Azure Data Warehouse. We hope this article helps you understand and make sure it’s the right solution for your business needs.
A data warehouse is an optimized, structured data storage system designed to execute the fast SQL queries required for relevant business intelligence (BI). From fast transactions to predictive analytics, data warehouses have been the standard repository used by organizations to support BI for over a decade.
The benefits of using a data warehouse include
The complexity of the logistics infrastructure required to collect data from different parts of the business and extract actionable insights can grow as the business grows. Data warehouses provide a reliable way for an organization to gather this information into a single database and data model that analysts can use to run the necessary queries.
See how it works:
Extract: collecting raw data from various sources in the organization (e.g. ERP, CRM, sales, marketing) to intermediate databases.
Transform: intermediate data is sent to the integration layer where it is aggregated and converted to an enterprise data warehouse (ODS).
Load: data is moved from the integration layer to the data warehouse, where the schema to be used by analysts in SQL queries is defined before being written to the relational database (schema extraction).
The database used is relational, which means that the data is structured: it is stored in tables with columns and rows. These tables are arranged according to a schema defined in the translation phase.
If the transformation step is performed by the ODS system outside the data warehouse, it is called ETL (Extract, Transform, Load). If the data warehouse performs the transformations internally, it is called Extract, Load and Transform. During ETL, the data warehouse needs structured data and a schema written to interact with relational databases.
The most common uses of data warehouses are:
Online Transaction Processing (OLTP): the data warehouse can be optimized for data integrity and search speed to process large volumes of short data transactions. An example is transactions on a high frequency trading platform.
Online analytics (OLAP): the data warehouse can be optimized to perform complex searches faster on relatively small volumes of transactions. In practice, this is used by analysts to produce BI reports.
Predictive analytics: OLAP can be optimized to predict events and generate business scenarios, typically using machine learning algorithms.
Since data warehouses are made up of stored schema, it is important to know what type of queries you want to run before adding a schema to the data warehouse. To cope with the complexity of different data sources, the data warehouse can be divided into data maps to provide hardware and software resources for each business function, such as CRM.
Now that you know what a data warehouse is, you should know that it has a number of features that make it a powerful ally for organizations. Take a look below:
Metadata organizes data by topic and contains relevant information that improves decision-making.
Data is transferred from the business environment to the data warehouse through an integration system. This ensures that coding is consistent and standardized.
The data warehouse stores an average of 5-10 years of data, allowing for trend assessment, historical analysis, etc.
External operational data is then cleaned to remove inconsistencies and integrated to create a new, more up-to-date dataset for operational analysis.
Data imported into the data warehouse is filtered before transfer and is not updated or modified. They can only be read or deleted.
In other words, the data warehouse is based on a relational model, where data is structured and represented in tables, also known as relational tables.
Access to data via the intranet, including the use of web browsers for browsing, searching and reporting.
The key elements that make up the data warehouse architecture are described below.
Business transaction systems can be composed of different forms of data.
Consists of a storage area and a set of processes. Its role is to extract, clean, transform, merge, replicate and prepare data from transactional systems for use in the data warehouse. This data is not visible to the end user.
The environment in which data is organized and stored for immediate retrieval by end users. Data on these servers is typically stored in relational databases, but may also be stored in real-time analytical processing (OLAP) technology, as many data maps process data only in a one-dimensional model.
A logical subset of the data warehouse, usually divided into segments or views, which are queried by users.
Data mining, also known as data mining, deals with large data sets in which there are many relationships between data that are not obvious. Data warehouses often consist of large amounts of data, so a tool is needed that can automatically search the data warehouse for trends and patterns that would be difficult to find by simple search, using predefined rules.
Did you like this explanation? Now that you know what a data warehouse is, what are its main functions and applications, how is your professional development in data analytics?
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If your organization is serious about using data reporting as a key strategic business tool, you will need to build a data warehouse at some point, but designing a modern data warehouse is not an easy or trivial undertaking. More than 50% of data warehouse projects have low or zero adoption rates. So, how do you start to design and build a data warehouse? What are the pitfalls and how can you optimize it? And most importantly, where to start?
This article provides tips on how to set up a data warehouse and avoid some common pitfalls.
In a modern enterprise, data is usually stored in different locations. There may be a reason for this:
Application databases – this is probably the most important use case for start-ups. For other companies, this may be a product sales application.
Web applications – these may be applications that are needed to grow or maintain the business. Examples include email marketing applications such as Mailchimp, web analytics applications such as Google Analytics or Mixpanel, or accounting applications such as Xero and Quickbooks.
Spreadsheets: can be in the form of spreadsheets (Excel, CSV) or spreadsheets such as Google Sheets. You can update this data manually.
The data warehouse synchronizes data from different sources in one place, for all your information needs.
It provides reliable data and manages the questionable workload of everyone in the organization.
The architecture of a typical data warehouse is as follows:
The data warehouse is designed and built to meet your information needs. Once you have identified the data you need, you design it so that you can transfer it to the data warehouse.
Create a database schema for each data source that you want to synchronize with the data warehouse. This
When you import a contact table from Mailchimp into your database, you can give it the following name:
Choose name, Mailchimp email address. Contact Mailchimp.
Creating a layout is very simple. Just insert one line to create a new layout. In Postgres, it’s really just three words.
Creating a layout
Creating a layout for Mailchimp
Note a: New analysts may get confused between a database schema. Schemas can be defined in two ways. Schemas can be used to describe
Note b: MySQL databases do not support schemas, so you need to use naming conventions for imported tables, e.g. mailchimp_contacts etc.
The next step is to synchronize the source data with the data warehouse. Your engineers should be aware that this is an ETL scenario.
Please consider the following points when designing your import plan:
One question that often arises is how to convert data before you need it. The archive generally recommends not to do this. At least not from the start. In particular If this is your first data warehouse project. There are several reasons for this.
Even if you have clear requirements, these are likely to change or be exceeded during the project.
You don’t want to waste time redesigning the ETL scenario according to what different stakeholders want at different times.
Providing reference data helps to separate the dependencies of the ETL scenario from the business requirements.
Think of the source data as an interactive database that can be transformed into a series of derived tables by grouping along different dimensions or combining tables from other sources.
When the data is transformed, details of the original data are lost that may be needed in subsequent usage reports.
For example, if you aggregate sales by period, you lose the details of each transaction item that another user may need for comparison with other reports. If you enter data that is not translated, you can link it to other data sources.
The need for raw data becomes even more important when you start to create data models that can be reused to answer different questions.
Performing a data transformation on a source system can be resource intensive, especially when it is a database that serves customers from all over the world.
You don’t want to burden them with time-consuming data conversion tasks.
In some cases it makes sense to convert data before transfer, but this is usually for companies that have already built a robust data warehouse and want to improve it.
The decision to transform data can be complex. If you don’t take action, you risk wasting a lot of time optimizing data that has no value for business planning.
A good rule of thumb is to set a goal first. Transformed data should only be created to solve a specific case or problem.
These practical use cases can be identified through reports and dashboards generated with the imported data.
When users start reporting performance problems, the data can be transformed. This is possible because the reports contain
This is where the flexibility of SQL-based reporting can help identify problems that can be solved by data transformation. Any analyst can quickly identify the cause of reports with long queries and optimize their performance.
This can be achieved primarily through automated pre-combination of data. This can be achieved through materialized visualization, where data conversion tasks can be created that simultaneously
Another suggestion is to create a new database schema in the data warehouse to store transformed (or post-processed) tables.
Similar to the previous approach, where all data sources are mapped to a schema, creating a specific schema can help to define the list of created/transformed data tables. This will be useful later when additional data import and transformation sets are created as the data matures.
This is important, especially if you don’t want your data warehouse to be a black box that only a few engineers can work with. If users don’t understand, they won’t dare ask questions.
You can start by creating a shared document that describes a common understanding of:
Each time a report is created, update this document to reflect any new level of business understanding of your data.
Uncertain or changing business requirements make it difficult to choose the right storage technology. Whatever tool is chosen, it must be scalable and flexible.
Generally speaking, designing a data warehouse is an excellent solution to easily collect and analyze business data. It increases access to data, speeds up analysis activities, improves the quality of information for reporting and ensures secure data management.
Did you find this article strategically relevant? Want to learn more about how to build a modern data warehouse? Then check out our page on Data Warehouse: Cloud, Hybrid & On-Premise Solutions or visit our page on Data Warehouse Consulting.
There are many different tools to choose from in the field of analytics. Decision makers need to know what the differences are and which tool best suits their business needs. In this article, we present the features and benefits of Tableau BI Tool and why they are the best for your business.
Increasingly large data sets are challenging for businesses, enabling them to use analytic tools and make strategic decisions based on reliable data. One of the most popular solutions is Tableau. Here, relevant data is presented visually and presented in the form of interactive dashboards and stories.
Tableau has the particular advantage of customization options and security. User-friendly interfaces and intuitive features such as drug-and-drop make it easy to work with, even for less experienced users. In addition to Tableau Desktop, packages and licenses can be extended to include Tableau Server or Tableau Online if required. Tableau Server is particularly suited for managing mission-critical data, while Tableau Online is a cloud-based option that can be used and portable without special hardware.
Tableau offers a number of useful features for users. These include a high degree of data interoperability, as Tableau can access data from Google Analytics, MySQL or Excel, for example. Extensive use of data makes sense in BI and analytics environments, as more data allows for deeper insights and more reliable data.
Tableau also provides access to specific data sources that traditional connectivity is not available. Tableau also provides suggestions for future updates.
Tableau provides users and administrators with a range of options to ensure data security, such as access and authentication. Data security is particularly important when highly sensitive information is being transmitted. Authentication options include SAML, Kerberos or OpenID. Depending on user rights, you can also hide certain values in Tableau.
Reports are also very important for data generation. Tableau offers automatic and ad hoc reports. There are also several options for displaying reports, such as tables, graphs or bar charts. Reports can be downloaded to a dashboard or are available in the most common file formats.
Real-time data visualization is important in analytics and BI to present complex problems in an understandable way. Tableau is known for being very easy to use. Users benefit from options such as data queries or filters that clearly highlight certain information. For dashboards, you can use the ready-made templates on the Home Dashboard tab or create your own dashboard.
Tableau uses VizQL to analyze data and better understand business processes and relationships. This allows users to quickly find answers to a wide range of questions. Again, drag-and-drop functions and queries are easy to use, especially since formatting queries is simple.
In the analytics area, Tableau enables advanced analytics, i.e. the integration of artificial intelligence and machine learning. This makes it particularly accessible for analyses that require much more manual work. Advanced analytics can also be used to predict future process flows.
In addition to useful features such as visualization, reporting, analytics, forecasting and privacy options, Tableau also impresses with a number of system-wide benefits such as integrated analytics, IoT analytics or the ability to use mobile devices. The later is available for Android and iOS
All dashboards and key indicators are available wherever you are. Features such as scrolling, searching and highlighting make it easy to work with prepared data.
As an added benefit, Tableau offers a 14-day free trial. This gives unsure users the opportunity to try it out for themselves and see how practical the analytics solution is for their purposes. Interested users can choose between Tableau Server and Tableau Online. Tableau Desktop comes with both options as standard. Tableau Public also offers a free unlimited trial.
Tableau BI Tool offers many interesting analysis features and has an overall intuitive user interface. Tableau has become one of the top BI solutions, along with Power BI and SAP BusinessObjects. However, as the general differences between these solutions are more in the details, a pragmatic approach is recommended when choosing the right business intelligence tool.
When it comes to data quality issues in data warehouse solutions, people often talk about – garbage in, garbage out. Most decision makers already know that reliable results can only be achieved with good quality data. This is not only true for classic business intelligence data warehouses. Real-time data warehousing and artificial intelligence only work well when they are based on a reliable set of data, whether it’s predicting sales in retail, optimizing production processes in industry or identifying trends in law enforcement
How can we get good quality data? Here are 5 most common data quality issues you can take into consideration to improve the reliability of your data warehouse. When implementing these actions, it is important that it is not a “one-of”’ project. Instead, data quality should be seen as an ongoing process. The term “closed cycle” or “data quality cycle” is often used in this context. Therefore, quality must be defined and regularly monitored to achieve sustainable results.
This includes, among others, the following activities:
What is data quality? As is often the case with data quotes: ask five experts and you will get five different answers. This makes it even more important for business professionals to develop a common understanding of the term before addressing data quality. One definition, admittedly rather general, in this context might be the following:
Data quality is the suitability of the data for a specific purpose.
Data quality can be assessed according to the following criteria:
Consistency – data should not be inconsistent or overlapping.
Completeness – data must be accurate.
Validity – data must come from reliable sources
Accuracy – data must be in the correct format and have the required number of decimal places.
Timeliness – data should be provided on time and as expected.
It is reasonable to prioritize the different criteria. Depending on the sector and the business process, individual data can be analyzed in more detail. For example, the accuracy of data is particularly important in the case of the profit and loss account during the audit. On the other hand, consistency or uniformity of data is important in the assertion based approach. However, these priorities may change over time. You should also regularly monitor comprehension and data quality requirements. This will allow you to assess whether your understanding of data quality is appropriate to current needs.
Continuous and automatic measurement of data quality using appropriate software. Inconsistencies, redundancies and missing data are identified by the appropriate tools and indicated by automatic alarms when necessary. All monitoring results are presented in a transparent and clear manner. The evaluation and quantification of these results should be guided by business-oriented data quality principles and appropriate targets to be defined in advance.
However, audits are also useful because they establish the status quo on data quality by first starting from zero. An automated process can then be established.
As in the previous point, do not rely solely on the “green light” of the data quality assessment software. Seek feedback from business users who work with the data on an ongoing basis. Reputed data warehouse consultants can also help with best practices and expertise. In addition, regularly invite different stakeholders to meetings. This intensive exchange of information prevents misunderstandings and makes changes in business processes transparent. Seamless interaction between business and technical contacts is therefore an important step towards improving data quality.
Gaps in manual data entry, for example, in call centers, can also be a source of inaccurate data. It is therefore advisable to start taking appropriate action at the data entry stage, not just in the system. This includes validation and format checking, which can be done with an intelligence input mask. For example, the date of birth should not be a free text field. Similarly, address data should not be sent to the system without validation, but should be verified by post. Other measures include comparing the input data with references or searching for copies. In other orders, careful monitoring of data quality issues in the data warehouse at the time of data entry can save a lot of hassle during subsequent sorting.
Another source of inconsistent and inaccurate results is historical data from different departments. These need to be analyzed and integrated into a single data warehousing platform. These will create a single version of the truth that provides a unified view of all the data in the company, and thus a single point of contact for all users.
In short, high-quality data is always the result of the interaction of technology, knowledge and personal interaction. Every company needs to find the right combination for its needs. It is also important to consider the cost-effectiveness of each measure. If you take these aspects into account, you are on the right track to build a reliable and high quality data warehouse.
Want to learn more about how to build a quality database for digital transformation? Then check out our page on Data Science and Big Data Implementation Services or visit our page on Data Warehouse Solutions.
Data warehousing is a progressively common buzzword. But what is a data warehouse? It’s primarily the collection and analysis of data. The data warehouse specialist offers exciting opportunities and good income. Find out what data warehouse professionals do.
A data warehouse is a permanent database designed to analyze and use data to make business decisions. This is why this type of data warehouse is very important for companies, as it is used to analyze competition, optimize processes and adjust the strategic direction of the company. Therefore, various information, data and events that occur continuously in business are recorded in this database.
When this data is collected in a uniform way, a so-called database is created. When new business decisions have to be made, this data is an important aid to decision making. Data warehouses are used to create these datasets. Data is collected from different sources, compiled into a standard format and prepared for research. The collection of these data must always comply with data protection standards. This applies in particular to personal data.
The data warehouse specialist analyses and manages the company’s most important data and stores them in data warehouses. An important task is to clean, prepare and store the data so that all employees can access it as quickly as possible. The data warehouse specialist is therefore the link between management and IT. The data warehouse specialist works with customer data, which provides important information about the current market situation and future developments.
Another task is to rank and prioritize the collected data using business intelligence software. This requires specialist knowledge of various database systems such as SAS or Oracle. Future tasks include evaluating and developing strategies and ideas for the use of information and supporting the company in quality control. This will require the data warehouse specialist to have a broad and varied knowledge in many different areas.
The key skills of a data warehouse specialist include extensive experience in software development and project management processes. In addition to database expertise, the candidate should also have good communication and abstraction skills. The candidate must be able to work under pressure, work in a team and be customer focused. The ability to work independently is important, and mathematical procedures and computer skills are also preferred.
There are several ways to become a data warehouse expert. The classic way is to study economics, business IT or other related fields. Computer scientists, physicists and mathematicians also often choose other types of training to become data warehousing experts. However, you can start working in this profession without having a higher education.
Those who do not have a university degree in these fields will need to have some years of experience in similar fields to enter the profession. For example, training as an administrator or manager of computer systems. There are already a number of courses on data warehousing that can prepare you to enter this profession if you want to change careers. If you have had enough experience as a data analyst, you can be trained to become a data warehouse specialist. In this context there are certainly opportunities for advancement.
Data warehouse specialists work in a wide variety of industries in medium to large companies, all of which need to create, process and evaluate data. This includes conglomerates and multinational companies. In this case, they work in the marketing or IT department, which can always be slightly different, but this should be made clear in the job advertisement or at least made clear during the interview.
Potential employers include insurance companies, banks, IT service providers, consultancy firms, recruitment agencies and many others. Depending on the industry and experience, the base salary for data warehousing specialists ranges from $99,569 to $125,283, with a median base salary of $111,641. There is often a lower limit, but the average is $9,300 per month.
Existbi will help you develop your skills in a competitive digital environment. Our certified, enthusiastic and experienced trainers will help you understand the complexities of data and give you in-depth knowledge.
To start your career as a data warehouse specialist, you’ll need an in-depth understanding of relational database theory, database systems, modeling and data architecture. We’ll help you learn database software such as SAP Business Objects, Microsoft SQL Server or Azure Synapse Analytics Services, which in turn will increase your chances of finding a job in a technology company or corporate IT department where you can practice designing and developing data warehouse systems.
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 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.
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 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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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:
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.
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.
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.
In this step, the structure of the dimensions part of DW is created. We also define the data history in the dimensions.
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.
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.
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.
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.
When it comes to deciding which infrastructure elements are worth migrating to the cloud, the data warehouse rarely tops the list.
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.
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.
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.
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.
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.
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
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.
The main benefits of business intelligence stem directly from its purpose in today’s business environment. Business intelligence helps:
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.
Let’s look at the components of business intelligence and the way it can help you transform your business processes for success.
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
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.
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.
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
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.
Business intelligence can help businesses:
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
It all starts with a business problem to solve. The process of using data science to solve a problem is as follows:
Customers cancel their banking packages every 2-3 months after signing a contract.
Data collected and analyzed concluded that customers are leaving their service packages as their debt increases.
Based on the data, the management decided to take a proactive approach toward customers in the same group with similar characteristics.
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.
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:
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.
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.
in this phase, the raw data obtained in the previous stage is transformed into a suitable format for easy use in the following steps.
This phase involves understanding the patterns in the data and trying to draw valid conclusions from them.
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.
It is the final stage where the model is applied to the actual project and verified its performance.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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:
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.
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.
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.
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.
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 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.
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.
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.
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.
Having outlined some of the major AI merits, let’s look at its main disadvantages.
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Easy to start, no training required, quick to set up, and has dashboards for services such as Salesforce, Google Analytics and Microsoft Dynamics.
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.
Application navigation allows report developers to customize navigation so users can quickly find content and understand the connections between different reports and dashboards.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Brands such as Amazon are already using AI to evoke emotions. Analysis of customer sentiment can be used in marketing.
Automated services such as chatbots and virtual beauties can be made smarter with AI.
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.
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.
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.
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 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.
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.
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.
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.
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.
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/)
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:
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:
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:
This has been resolved in BOBJ 4.3, which makes development easier and less time-consuming.
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.
There is a great improvement from BOBJ 4.2 and 4.3, which leaned to modernization and ease of use.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
From a technical perspective, there are several options for integrating the solutions:
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.
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.
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.
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.
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.
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 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?
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.
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.
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.
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.
The challenges of big data lies in the data itself:
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.
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 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.
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 can be used in different architectures and on different hardware. It allows you to aggregate large amounts of data in a relatively fast cluster.
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 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.
With Tableau, you can extract and process data. With visualization, you can gain instant insights that you can use to optimize your processes.
Zoho is a big package with many programs. These include CRM, home office toolkit, financial platform and data analytics.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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:
No one can afford not to take advantage of these opportunities.
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.
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?
We will also briefly outline the key points that can lead to success in an era of digitization:
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.
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.
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 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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
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:
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.
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.
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.
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.
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.
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.
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.
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:
Below we can see the basic elements that make up the architectures of a Data Warehouse.
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.
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.
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.
Transactional systems of the company can be composed of various forms of data.
Logical subset of the Data Warehouse, usually divided by department or views needed by users.
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:
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.
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.
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.
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 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.
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.
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.
There are several ways to retrieve data from a data warehouse:
Used to visually standardize data records. The information remains in its original location but is displayed in a uniform way on the front panel.
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 (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.
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.
Unlike uniform access integration, in this approach data does not remain at the data source but is copied to a single data warehouse.
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:
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.
Data Integration brings many benefits to enterprises:
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 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.
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.
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.
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.
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.
Below are the top SAP business intelligence tools you would love to try this year:
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.
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 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.
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.
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 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 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.
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.
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.
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.
Here’s an overview of how it will help in data analytics:
Convert rows of data into visualizations that help you understand the big picture of data at a glance.
Find out opportunities for more effectiveness and identify possible risks before they impact your business.
Visualize all of your data in a single view
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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!
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
To know if you should look into hiring a data warehouse consultant, consider the following factors:
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.
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.
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.
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.
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.
Let’s explore here how Tableau makes it easy to automate reporting tasks:
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.
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 helps you to modify the programming of Tableau files, create templates and transfer workbooks from test to production.
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.
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.
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.
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.
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 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.
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.
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.
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 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.
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.
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.
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:
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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 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:
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
Informatica with its strong experience, these are 8 concepts of data processing engine that you should know when evaluating various data platforms:
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.
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:
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.
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.
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.
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.
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.
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.
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.
In this blog post, we are going to discuss the importance of ERP and things to consider before implementing it.
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.
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.
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 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.
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.
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.
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 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.
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.
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:
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.
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.
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:
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…
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.
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.
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.
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.
In this blog post, you will learn:
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.
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.
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:
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.
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.
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.
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
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.
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.
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.
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.
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:
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.
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.
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.
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:
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.
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.
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.
Redesigned bringing the Fiori Tile, which is can be rearranged according to the user preference.
This new feature allows users to display all the user-created documents in one single view according to the last saved date/time.
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.
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)
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.
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.
Located on the top of the page, this new feature replaces the original tab-like interface.
All alerts, documents sent by others, and scheduled instances from scheduled jobs have been simplified into one view.
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.
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:
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.
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.
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?
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
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.
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.
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.
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.
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/
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.
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!
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.
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:
3. Supply chain
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.
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.
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.
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.
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.
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.
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.
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!
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.
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.
This software provides various tools for data 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.
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:
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.
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.
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:
Now, considering your professional knowledge, your staff, your budget, you can choose one that fits this criterion the best!
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!
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.
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.
It’s not hard to use the IBM Watson analytics; it just requires a little exploring and a lot of practice.
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.
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:
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.
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.
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.
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.
The architecture of IBM Watson analytics is not as complicated as you would think. It basically comprises three Ds: Data, Discovery, and Display.
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 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.
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!
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.
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?
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:
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.
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.
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.
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:
As a data manager, you should also have the following essential and useful skills:
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.
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.
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.
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.
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.
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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.
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:
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.
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:
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.
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.
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.
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.
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.
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.
If you are a small business, here are a few tips to make data management more effective and convenient for you:
A regular schedule to maintain data is non-negotiable. It helps ensure that your information has zero errors and there is no security risk.
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.
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.
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.
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.
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.
Here are some faqs about data management…
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.
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.
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.
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 ExistBi. We have plenty of everything.
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:
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:
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.
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!
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:
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?
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.
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.
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.
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.
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.
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.
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.
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!
The star schema is so widely used because it has several benefits over types of schemas. Some of these fantastic benefits are the following:
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.
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!
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:
And this way, you can create your own schema using these specific components of a Snowflake schema model.
Despite the challenging characteristics we just discussed above, there are some significant advantages of the Snowflake schema. These benefits include:
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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.
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.
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/
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:
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.
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.
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.
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.
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.
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:
Webhooks has great potential to perform the following works:
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:
With the upcoming releases, more events are expected to be added to the workflow in Tableau 2019.4.
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:
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.
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.
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.
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.Bodybuilding pu mature clips prima max mastebolin (vial) drostanolone propionate buy online – sports bodybuilding.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Apart from the significant upcoming business intelligence trends, there are certain other aspects that most people are curious about.
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.
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:
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.
Essentially, business intelligence has four stages:
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.
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.
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.
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.
Apart from all the advantages and benefits, we have discussed so far, here are some more benefits of incorporating 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.
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.
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.
The entire course is a blend of various subjects, including:
The curriculum teaches the scientist how to identify and extract meaningful, useful, sometimes hidden, insights from a collection of raw data.
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.
With the growing availability and accessibility to both these programs, people must learn the difference between them.
So, let’s compare them, shall we?
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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!
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!
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…
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.
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.
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.
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.
The surgical specialization of Orthopaedics necessitates exceptional surgical skills, clinical acumen, sensible physical strength, and improved knowledge. As a complement to such requirements, new technology (e.g., AI) have been adopted in the past couple of decades, which has helped to produce innovations in the area of Orthopaedics and has also given favorable influence from the treatment and operation. Substantial adjustments and inventions are possible with the support of new technologies including big data, AI, and 3D printing. These technologies give the opportunity for better service and the best patient outcomes.
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.
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.
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.
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.
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.
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.
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.
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.Squats in bodybuilding – anabolic steroids online buy steroids in italy modalert 200 australia luca sgrò goldsmith workshop – kunena – topic: buying ceftin bodybuilding.
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.
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.
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 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 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.
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.
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.
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 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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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:
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.
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…
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Let’s take an overview of how specifically predictive analytics solutions can help you to boost your sales:
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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!
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 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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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!
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.
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
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Based on some key factors, let’s see how the two data storage terms differ from each other:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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!
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…
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…
That’s a lot, right!
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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.
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:
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:
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Here are a few reasons to have an effective data management system:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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…
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;
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?
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.
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.
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 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.
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.
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:
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.
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!
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.
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.
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.
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.
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.
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.
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:
This is how businesses require data science to facilitate their decision-making process.
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.
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 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.
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 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.
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.
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.
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.
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:
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 )
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.
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.
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.
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.
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.
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 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.
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.
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:
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:
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.
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:
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:
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:
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:
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:
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.
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.
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.
Here are some of the major changes that are expected to cover in BI 4.3:
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.
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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...
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.
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.
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.
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.
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.
A diverse number of methods, manual and automated both, have been