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.
Top Reasons to Consider Tableau for Automate Reporting
One of the major reasons is that Tableau can run programmatic automation tasks in R, C, C++, Python and Java. The harmony of such programs helps to smooth the learning curve implicated with system integration.
Another thing that makes Tableau perfect for automation is the user-friendly and easy interface with drag and drop style, which enables the users with no coding experience to contribute to having superior insights.
The last one is the enormous added values supported by the Tableau platform that ensures you save time and effort.
Let’s explore here how Tableau makes it easy to automate reporting tasks:
Rest API(Application Programming Interface):
Just like a language or set of rules created for systems, these are used to communicate and give instructions to each other. Rest API of Tableau automates tiresome tasks like site management and users, workbook updates, and custom app integration, etc.
Extract API –
Extract API enables you to drag data into the extracts, which allows offline access also that improves performance. Data sources not supported by Tableau can be dragged into Tableau with the Extract API that makes them in a fully supported format. You can create custom scripts in Python, R, Java, C, C++ and run them on Mac, Windows, and Linux.
Document API –
Document API helps you to modify the programming of Tableau files, create templates and transfer workbooks from test to production.
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. Tableau Catalog 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.
Tableau Catalog: Track, Manage, and Commune
Eliminating all the presumptions and manual work, Tableau Catalog provides a correct and trusted view of the analytics environment. It captures the stock records from data sources automatically, builds up a connection between various data sources, analyzes content, and conveys the details about data quality to the users. Let’s check out some key components of the Tableau Catalog.
External Assets list
With Tableau Catalog, you can view the data comprised in your tableau environment easily. You don’t need indexing and configuration for processing with automatic ingestion. The External Assets Lists allow viewing an inventory of all files, databases, and tables that exist in your environment. Moreover, it also provides the tools to identify the disused data, which you can remove easily.
Lineage and Impact Analysis
Tableau Catalog helps the data users to visualize the relationship between various tables, preps, databases, columns, and workbooks by using a lineage graph. It will also help you to identify the workbooks connecting with a particular table or column and let you know about the changes in those tables. Lineage and Impact Analysis lets you know about the users operating the column, and also about the sheets or dashboards of the column.
Data Quality Warnings
When a data asset gets outdated or under maintenance, it is vital to inform users about that to avoid them from making decisions using corrupted files. You can add a data quality warning to all the data sources under maintenance, and it will be shown on all contents within that source.
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.
What is Data Integration?
Data integration is the process of gathering data from numerous different sources into one joined vision to make the data more actionable and valuable to an enterprise. It provides business users with constant access and delivery of data throughout different business processes to meet the information utilization requirements of all applications.
While there is not any common method that can work as a general solution for all to solve data integration needs, the majority of solutions offer a few common features, such as a data source network, one master server, and allowing clients access to data from a master server.
Benefits of Data Integration
Data integration tools powerfully aggregate data and make it available to the users who require it. There are a lot of benefits for an organization that uses a data integration solution.
Various types of data require different levels of specialty that the dataset achieves. Each set of data has individual qualities for everything ranging from metadata, structure and schema. An integration solution assists all such datasets and qualities.
Some dedicated applications serve specific business information needs, but they also bring new opportunities to take benefit from data in new customs. Data integration lets the users switch between formats and view data in traditional or cloud applications and consume the information these systems deliver.
Data turn out to be less complex. Data integration handles the intricacy that arises from a data transfer and rationalizes those connections to make it deliver to any system effortlessly.
Data gains exceptional value than ever before. Users can now split their internal data and combine it with external data and combine structured and unstructured data from various sources.
Data gets more centralized. Hence, it becomes easy for anyone in the company to utilize it. Centralized data can be converted easily earlier to data integration.
Collaboration within data also improves due to its accessibility. Now employees can easily share data with one another internally or throughout the organization.
Data accuracy gets better. It grows to be more consistent and is typically error-free to make certain that the data is valid and feasible.
These are a few of the ways that an organization can actually take benefit from a genuine data integration strategy. Without a pre-decided plan, it may be tough to manage, but having the right strategy can support the companies to realize considerable business value from a data integration solution.
General Business Use Cases for Data Integration
Are you aware of the ways you can put data integration into action? What is the reason that makes it so appealing in the first choice? Here are some ways confident organizations use data integration solutions:
Leverage Big Data
A big data analytical solution presents a way to collect important information from your structured, unstructured, and semi-structured data. Big data integration enables the IT team of an enterprise to integrate and merge all data at once and make it available for analysis and helps to gain actionable insights to make valuable business decisions. It doesn’t matter what type of data IT needs to split and analyze, whether it’s conventional data, machine-generated data, social media, web data, or data from the Internet of Things networks as data integration conducts real-time ingestion of data quickly.
Customer Relationship Management (CRM) Software
One trendy approach that enterprises use to take benefit of data integration is through customer relationship management (CRM) software. CRM enables an enterprise to capture and collect information about the customers who are interested in their services. Therefore, it is easier for an organization to recognize and target their customers and also garner benefits that boost revenue, including updated records that imitate correct customer information, managing a database of sales leads that is tracked and monitored across the process, and find out future opportunities to move toward or associate with customers.
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.
Selecting the Best Approach!
There are numerous ways that a company can adopt to make use of data integration technology. These approaches correspond to functionality that no other tool does. The type of approach you select to conduct data integration depends entirely on the specific requirements of an organization and the outcome which you desired from the data integration. Here are some ways through which a company can utilize data integration technology:
Extract, Transform, and Load (ETL)
Integration Platform as a Service (iPaaS)
Enterprise Service Bus (ESB)
The major step to a successful digital transformation strategy, data integration can reform your business technology to work together with customers, vendors, suppliers, and applications. Contact a leading Data Integration Consultants today, ExistBI has offices in the United States, United Kingdom, and Europe.
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 Consultingis 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.
What is Data Governance?
Data governance is described as the management of data to confirm its accuracy as per the requirements, standards, or rules that a specific organization needs for its definite business.
It is a combination of data management applications and processes that help an organization manage its internal and external data flows. By implementing Data Governance, your business can make data quality more efficiently and help secure the accessibility, safety, integrity, and usability of its data assets.
According to Gartner, data governance is the specification of decision rights and an accountability structure to make sure the suitable behavior in the assessment, creation, consumption, and control of data and analytics.
When building your Data Governance strategy, you should customize the data governance definition according to your company’s concerns and goals.
Benefits of an Established Data Governance Strategy
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.
Greater Data Quality
As data governance helps to improve discoverability, businesses with efficient data governance programs also take advantage of improved data quality. Though technically two different initiatives, some of their objectives overlap, for the consistency of data and its consistency. One way to visibly distinguish the two programs is to consider the questions imparted by each field.
Data quality helps to know how useful and absolute data is, whereas data governance helps to know where the data stays and who is accountable for it. Data governance makes data quality better.
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.
Why Data Governance Matters?
Data governance plans are often driven by the requirement of complying with internal policies, regulatory consents, such as SOX, GDPR, HIPAA, frameworks, or standards. But the profit of setting up clear rules and procedures for data-related actions is further than compliance. Here are some of the other general advantages of a well-established data governance program:
Enhanced security, which is attained by locating critical data, finding out data owners and data users and assessing and reducing risk to critical data
Better data quality that enables improved business decision-making
More operational efficiency due to processes and procedures that facilitate faster and simpler data management
Less data management and storage costs
Reduced security breaches due to superior training on proper handling of data assets
Implementation Process for Data Governance
The data governance plan can be very intricate and costly to implement. Here are the steps included and the aspects that need special attention.
Step 1– Set up a value statement and create a thorough plan
Step 2– Identify and employ the right people
Step 3– Build a data governance policy
Step 4– Apply the policy
Step 6– Assess growth continuously
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 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, BOBJ4.3 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.
Fold and Unfold
With the BOBJ 4.3, Showing and hiding of data sections has been revamped. From the use of plus buttons, which similarly works with grouping cells from Microsoft Excel, we can now hide areas using the down arrow placed on the either the headers of the table or the section headers.
When you hear the sound of a Ferrari, you’ll find that sound so unique, which is a result of years of hard work by the designing engineers, connecting the driver’s experience to the car. Similarly, the data processing engine plays the role of connecting the user experience to the data. If you want to dive deep into the data solutions implementation, joining Informatica Classes will help you learn the various aspects of data needs and their fulfillment.
When you talk about data management in an organization, data processing engines receive the data pipelines, conceptualize the business sense, either simple or complicated. Then you can process data on various frameworks like Apache Spark in optimized, streaming, or batch-wise approach in cloud or on-premises.
Many data engines are available in the market, but just like selecting a car for your use, you search for different main features and differentiators that change your opinion from one to another. Informatica is designing data processing engines for at least 25 years. Over this time, it has implemented top-class and enterprise-ready data engines to assist different data workloads on-premises or in the cloud.
8 Key Concepts of Data Processing Engine
Informatica with its strong experience, these are 8 concepts of data processing engine that you should know when evaluating various data platforms:
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:
Quickly translating data from a relational table when you do it on a small subset of the data
A relational database engine allows speedy reads by using database index
Combining the steps between “read” and “filter”, this method eradicates the need for unnecessary data flow
Code Generation & Pushdown:
After validation and optimization of the pipeline, it needs to be translated into an optimized code to carry workloads regarding the transactional, database, big data, and analytical. The data processing engines present two modes of code translation to support various computations of workloads that are: native and ecosystem pushdown.
The data processing engine of Informatica provides its own execution environment with its native-mode capabilities. For execution, the ecosystem pushdown mode translates the data pipeline into another abstraction, such as Spark or Spark stream processing.
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’vecovered 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.
What Is An ERP (Enterprise Resource Planning)?
ERP (Enterprise Resource Planning) is a software system that manages and supports business operations. A business is an activity that a company performs on a daily basis to add value to its business and generate profits. Types of activities include the tasks that are usually performed in real-time.
What Is The Purpose Of ERP Software?
ERP (Enterprise Resource Planning) is an integrated software system that automatically manages most aspects of a company’s operations and production, including finance, purchasing, production, logistics, human resources, marketing service, and customer support.
Key Features of ERP
ERP offers a wide range of services to companies that want to optimize their operations. The systems used are constantly updated to provide the fastest and most reliable services.
As the name suggests, the main objective of ERP is to manage and utilize the various resources of a company in an economical manner. It is also designed to ensure that all functions are used correctly.
The ERP system is particularly well suited for tracking and managing the company’s production capacity, available cash, availability of raw materials and supplies, payroll, purchase orders, etc. A purchase order is the main document issued by the company’s purchasing department when an order is placed with a company or supplier.
The Importance of ERP in the Enterprise
The most tangible change that ERP systems have brought to the enterprise is undoubtedly the increased reliability of data, which can now be viewed in real time, and the reduction of duplication of effort. This can be achieved through the systematic updating of data in the chain of ERP modules and, ultimately, through the cooperation and commitment of the employees who interact with the business.
This allows information to flow through the modules in real time. In other words, a customer’s order triggers a production process, which in turn sends information to multiple locations, from the warehouse to product logistics. All of this is done through seamlessly integrated and unduplicated data.
To better understand this, you can think of an ERP system as a large database of information that interacts with and responds each other.
For example, a sales order becomes a finished product that is distributed to the company’s warehouse. An ERP system eliminates the need to track each process individually. This gives you the support and time to plan, reduces costs and analyzes your supply chain to produce more efficiently, reduce costs and improve product quality.
Six Benefits of ERP
Simplify IT – An integrated ERP application using the same database simplifies IT and makes everyone’s job easier.
Increased productivity – By simplifying and automating key business processes, everyone in your company can do more with fewer resources.
Insights – Eliminate information gaps, create a single source of truth and get quick answers to important business questions.
Reduce risk – Maximize visibility and control of operations, ensure compliance, and anticipate and avoid risk.
Greater flexibility – Streamlined operations and instant access to real-time data allow you to quickly identify and seize new opportunities.
Accelerate reporting – Accelerate financial and operational reporting and simplify the sharing of results. Leverage information to improve performance in real time.
5 Things to Consider For ERP Implementation
Many businesses start by using several simple, standalone tools such as QuickBooks and Excel spreadsheets to manage their various processes. Here are five reasons when your business needs to get out and buy a modern ERP system.
#1. You have unmanaged business processes: Do you have uncontrolled processes in certain areas? Managing inventory, improving customer satisfaction, and keeping costs within budget can be more challenging. In this case, you need to reorganize your business processes as your business grows and priorities change – the ideal environment for ERP software.
#2. You are spending more time on day-to-day operations: ERP software integrates solutions and data into a single system with a common interface to facilitate communication and collaboration between business units.
#3. Have many unanswered business questions: Can you easily answer key business questions such as sales metrics and product line performance? If not, your system may be fragmented or you may not have access to key metrics, which could hurt your business. Enterprise resource planning software is designed to solve these problems.
#4. Your business has missed the opportunities in brief: Are you spending too much time managing your business and not taking advantage of new opportunities? Today’s ERP systems include advanced intelligence features such as machine learning and predictive analytics that make it easier to identify and exploit new business opportunities.
#5. Manually processing multiple data sets: Do most departments in your business use their own applications and processes to get the job done? If so, you’re wasting time entering duplicate data. When data doesn’t flow from one system to another, reports take longer to run, errors occur more often, and decision-making is delayed.
Having an integrated ERP system is essential for any industry to get the most out of its resources. From the smallest to the largest, it helps companies of all sizes to successfully implement strategic business plans.
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.
What is Nursing Informatics?
Nursing Informatics is a specific area of nursing and a profession that has lots of potentials. The purpose of this blog post is to be an introduction to nursing informatics and the importance of nursing informatics.
The nursing profession is quickly changing to keep up with new challenges and advancements in healthcare services. As one-to-one caregivers, nurses are the frontliners of patient care and always feel the impact of changes in best practices more quickly than other medical services experts.
One of the primary ways that informatics has changed nursing practice is through documentation. The time of paper charts is gone where all the records updated with handwritten notes. Nowadays, nurses have to keep notes in digital health records and different systems that keep a patient’s clinical history easily accessible and up-to-date.
Health informatics is also a significant piece of care coordination in nursing. The capacity to track staffing, communication, and workflow can assist nurses to identify areas where current workflow can be improved. This can also help to make sure that staffing levels stay sufficient, which is important for giving patients the best possible healthcare. The more data that is gathered and analyzed, the more accurate the results will be and giving the most ideal data to deciding how best care for patients can be provided in the future. If nurse to patient ratio drops low, patients are more likely to suffer the worst outcomes.
Nursing Career Option in Informatics
Nurses at every level presently work with informatics through patient records and other healthcare technologies. Some nurses decide to focus their careers on the intersection of informatics and clinical practice. There are various career choices accessible in this path, including the following:
Clinical informatics specialist
Clinical informatics organizer
Nursing informatics specialist
Clinical informatics administrator
Nursing informatics Analyst
These roles can be found at each level and feature of healthcare organizations, including management and leadership, support, risk analysis, consultation, research, education, and evaluation. As informatics turns into a more noticeable part of the nursing field, job opportunities will probably keep on creating.
While healthcare informatics jobs are available to experts from different backgrounds, nurses are especially appropriate for these roles because of their deep insight into clinical workflow, past healthcare training, and experience in information systems and the latest healthcare technology.
With the proper informatics training combined with your existing medical knowledge and clinical, you could have an effect on inpatient care in a medical organization through a career in nursing informatics.
What a Nursing Informatics Expert
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:
Director – clinical informatics
Clinical informatics organizer
Informatics nurse expert
Why Nursing Informatics is
Nursing is progressively turning
out to be as “high tech” as it is a “high touch” job.
Nowadays, Nurses have more technology in their hands than any other medical professionals ever before, and as one may anticipate, it’s impressively improving patient care.
So how are nurses utilizing informatics as an approach to improve the healthcare providing to patients? Let’s discuss the several ways that nursing informatics is being used and why is it so important…
1. Improved Documentation
One of the most important parts of
the nursing profession is Documentation and it has been recognized as a more
vital part of patient care. The standard of nursing practice, practice and
theory of nursing, ethical and legal concerns, and other factors that are
taught in the advanced nursing programs make an impact on patient care.
Nowadays, modern nursing care is
organized patient history and special care requirements by using data generated
and organized in electronic patient records. By documenting a patient’s
physical condition and added that information electronically, nurses can manage
patient care more effectively. Also, nurses can improve the quality of patient
Lots of documentation is automatically produced by connected devices. Those devices collect patient-oriented specific data in real-time and send it to patient records. Taking a look at the documentation of a patient’s medical situation from time to time, nurses can make better decisions about how to give the best medical care, when adjustments, or changes need to be made.
2. Makes Sure There are No Medical Errors
The safety of a Patient is the main
concern of any health care professional, and nurses are the frontliners of
patient care to ensure that their patients remain safe and reducing medication
errors, falls, misdiagnoses, and other difficulties. Health informatics gives
valuable data that can stop these medical errors; for instance, an electronic document
can store data about a serious medication communication or allergy that might
not otherwise be instantly visible. Loaded with data, nurses can make smart
choices that keep their patients secure and safe.
Patient complaints and nurse
training errors are some of the main reasons for disciplinary actions, nursing
board license investigations, and malpractice lawsuits. Accusations have been
growing in recent years because of the ease of registering complaints online.
Health informatics makes sure regulating many patient care decisions which
makes it simpler for healthcare industries to restrict their responsibility and
ensure compliance with the Nursing Practice Act and other medical care
3. Decrease the Medical Costs
Medical service’s errors expenses
cost nearly 40 billion every year, and many of these errors can be solved with
health informatics. Not only with the information with health informatics
nurses can avoid errors but also they can automate different tasks such as
create doctor note templates, improving patient care, increase nurse’s
productivity, and stopping some of the expenses related to healthcare.
4. Improved Coordination of Care
Nurses are often called upon to
help organize the medical care of their patients. This means sending information
from therapists, physicians, pharmacies, billing, and more services during
medical care and at discharge. Without all of the important data, patient care
can suffer. Health informatics increases the coordination of this data,
improving both satisfaction and outcomes with care, and allowing nurses to
provide their patients all of the information they require.
What is Integration Platform as a Service (iPaaS)?
4 iPaaS Benefits
3 iPaaS Integration Patterns
Common Challenges with an iPaaS
What you should look for in an iPaaS?
What is Integration Platform as a Service (iPaaS)?
An Integration Platform as a Service or iPaaS – gives incorporated support to manage, administer, and coordinate cloud-based applications, utilizing fools that interface cloud applications and services, and control joining stream. Organizations use iPaaS solutions for scale execution needs, add product usefulness, and design SaaS applications and on-premise application coordination, all to expand the estimation of their business connections.
While it is not difficult to perceive any reason why an iPaaS is a particularly successful tool for integration, there are a couple of various types of iPaaS that are different from each other. Depending on your necessities inside the enterprise, a particular class might be more qualified to solve the most pivotal integration challenges you face.
Why Use an iPaaS?
These days, to satisfy client needs, stay in front of competitors, and improve activity; companies should have an enterprise integration solution installed that can adequately incorporate always growing integration prerequisites across various applications, data, and ecosystem processes. That is the reason an ever-increasing number of companies are hoping to tap the potential for expansive integration capacities offered by a powerful subset of the application framework and middleware (AIM) technology market – (iPaaS) Integration Platform as a Service.
4 Benefits of iPaaS
As an ever-increasing number of companies take their business in form of cloud computing, the struggle turns out to be managing various tools and business processes efficiently. Enter iPaaS, which is intended to incorporate the many cloud applications with each other in a consistent, simple-to-manage way. Attempting to integrate numerous cloud frameworks can be a significant pain for enterprise IT solutions, which is the reason iPaaS is growing so quickly. In fact, the iPaaS market is expected to reach $10.3 billion by 2025.
In any case, there are numerous ways an enterprise can gain the advantage of an iPaaS platform such as:
1. Better Connectivity
An enterprise’s IT situation can get complicated in a quick time. The advantage of iPaaS is that it can possibly associate all that a venture requires connected. How can you be benefited from software, applications, and other business processes if they don’t even work together? Here comes the iPaaS, which permits the business to incorporate a huge variety of on-premise apps to make easier hybrid data flows, improve operational work processes, synchronize information, and gain better visibility.
2. Cost Control
Assemble it or buy it? It is a well-established inquiry for the IT industry. Companies that utilize a multitude of coders to plan and keep an in-house integration framework will regularly discover costs out of their hand while paying for consultants to develop custom connections with various 3rd party providers can likewise dramatically raise costs. Alternately, iPaaS is commonly consumed as a service permitting the flexibility and adaptability of an enterprise that reduce the expenses of traditional integration.
3. Better API Management
Effective and easy-to-use API management
has become a struggle and difficult task as companies look beyond the
specialized need of APIs and deploy more business-situated APIs. In order for an
enterprise to rapidly and effectively access and share real-time data, it’s very
crucial to have a level of API management functionality. Through iPaaS,
organizations acquire a single platform to integrate and manage all of their
APIs with the capacity to scale depending on the situation. Companies are then
ready to make, convey, and manage APIs while adding new capacities and tools as
4. Secure Your Enterprise
It might be the greatest concern
enterprises have in regard to cloud computing. Because enterprises constantly
facing security and thereof problem within their system. An iPaaS arrangement
can decrease the danger of a data breach because the vendor continually deals
with the infrastructure and framework. IPaaS vendors additionally give
confirmation and verification methods to the different data flows streaming in
from all over the business ecosystem.
An iPaaS solution likewise gives companies a tension-free sleep at night, realizing that their systems and applications are genuinely secure.
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.
3 iPaaS Integration Patterns
As an ever-increasing number of businesses choose some type of cloud computing, the struggle turns out to be managing various applications and business measures viably. Enter iPaaS, which is designed to integrate the many cloud administrations with each other in a consistent, simple-to-manage way. Attempting to integrate various cloud frameworks can be a pain for big business IT, which is the reason iPaaS is becoming so quick. In fact, in 2017, the iPaaS market managed to surpass $1 billion for the first time. Here are 3 iPaaS integration patterns:
1. B2B Ecosystem Integration
Present-day B2B integration technology provides ecosystem enablement through multi-enterprise business continuity and communication in its capacity to control, administer, and automate frictionless data trades past the four walls of the business. A domain-specific platform permits businesses to meet far-reaching communication necessities with clients and partners, move data between unique internal systems, and integrate and connect cloud services and applications in a well-represented manner.
2. Hybrid Integration
Also, an iPaaS platform empowers organizations to speed up ground-to-cloud and cloud-to-cloud integration measures that effectively integrate applications, and storage and business platforms, to connect all data, regardless of whether it’s on-premise or in the cloud. Through iPaaS, it’s simpler than ever to hybrid connectivity to SaaS (Software as Service applications) and other cloud applications with a safe strategy to access on-premises applications behind a firewall.
3. Application Integration
Perhaps the greatest challenge facing companies today is the expansion of cloud applications across the enterprise. An iPaaS is regularly the primary line of protection in giving the capacity to unify integrations among applications and give some rationality across all the data moving through the enterprise. However, independent cloud application integration without considering the need to tie in on-premise integration and ecological integration necessities. Thus, an overemphasis on application integration alone possibly makes another sort of integration silo.
What to Look for in an iPaaS
An iPaaS architecture offers a
ton of promise, however businesses by and large search for some basic features
and capacities during the selection and discovery stages. Some of the things to
look for in an iPaaS architecture include:
The capacity to integrate with new data sources and other business processes.
Data reliability, security, and uptime.
Including API management, management solutions.
Monitoring solutions that provide start to finish visibility.
Ability to scale and adjust to meet developing business needs
Storing data on-premises, in the cloud, and in a hybrid scenario
The Future of iPaaS
The real question is – what’s to come in for big business IT? Enterprises should have an integration solution, even if it is confronted with the most complex situations. Integration platforms as a service get more famous and widely utilized in businesses as the year passes. Technological platforms will keep on advancing, as more enterprises are involving. Cloud-based integration solutions will become more evident than on-premise ones. Companies that have been terrified of moving to the cloud will be forced to dip their toes into the iPaaS market, and before they know it, will jump headfirst after understanding the advantages that come from iPaaS.
SAP BusinessObjects BI 4.3 is a major follow-up with BI 4.2 which aims to bring new features (BI Launchpad) and enhancements with a modern twist. The well-loved tool with a fresh look aims not only to make development easier for developers but also for front-end users with its improved user interface.
1. BI Next Generation Launchpad
It is getting an overhaul with the design based on the Fiori Theme. From the Windows XP-like interface, the new Fiorified BI Launchpad has been redesigned and packed with the old and new functions that make the user navigation easy to do.
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.
Revamped Scheduling and Publication
Newly revamped scheduling and publication are now grouped into two
distinct categories: General and Report Features.
Other than the revamped look, a new Recurrence type is added: Business Hours. This schedule sends the scheduled report every hour within the specified start and end hours (which is considered as Business Hours) only on weekdays. This enhancement is available for both BI Launchpad and Central Management Console.
Other than the revamped look and new feature, the Retry Option has been added in the options, which was an exclusive Central Management Console function.
Another feature that is introduced is creating multiple destinations in one scheduling job. This eliminates the workaround of creating multiple jobs for different destinations.
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.
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.
2. Currently, Open Documents Drop Down List
on the top of the page, this new feature replaces the original tab-like
3. BI Inbox
All alerts, documents sent by others, and scheduled instances from scheduled jobs have been simplified into one view.
4. HTML5 Web Intelligence
Intelligence has improved with the new look taking advantage of HTML5
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:
Icon side panels have been revamped into panes. Report Structure and My Objects (formerly Available Objects) are displayed by default. Other panels can be shown or be hidden by using icon buttons located on the top right.
Document Views has been consolidated into one button: “Edit”, which toggles to Design Mode and Reading Mode. Data mode has been completely removed.
Most Options appearing as a dialog box have been removed except for Formulas, Complex Filtering, Input Controls, Break, and Conditional Formatting. All options have been incorporated into Option Pane.
Report tabs have been repositioned at the top of the working page with an add button displayed at the end of the section.
Charts are now organized based on their use. Report elements dialog box has been removed.
The changes made in BOBJ 4.3 make the user experience simple but packed with great features. These improvements not only remove some of the workarounds from 4.2 but also deliver the best user experience and best visualization will greatly help the company in their data analysis.
Business intelligence has been a growing profession, and its importance is only increasing every day. Organizations and businesses, both small and large, realized how important the concept is for their progress and development. In case you don’t know what business intelligence is, visit ExistBi and read everything about it, including the components of business intelligence, why it is essential for small businesses, and much more.
As technical as it is, specific tools can make it simpler for professionals. For companies that need help with organization and growth, here are the top 12 top business intelligence tools.
Benefits of BI tools
Before moving on to the actual tools, let’s talk a little more about what they do and how they’re helpful. Why do you even need a business intelligence tool? What does it do to help a business grow?
Centralization Of Data
An efficient business intelligence tool helps bring all different kinds of relevant data together. All enterprises and companies collect their data from various portals, databases, and flat files, etc. Using all this data and making sense of it can be challenging since it comes from different directions in various languages and formats. So, modern business intelligence tools can help you centralize these sources. They give you one single point of view on all the different processes going on. This way, They help you identify the various issues, recognize patterns, and make critical decisions based on data and evidence.
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.
tools give you specific data that you can help to make meaningful predictions.
Basically, they make the jobs of data analysts and scientists much more
manageable. Also, if the tool is user-friendly enough, it is easily
interpretable by a non-professional. You can recognize patterns and routines in
the data to make plans and decisions. You can make or change strategies for the
best results and efficiently avoid a hazard.
Elimination Of Manual Tasks
Since the tool is
doing most of the job for you, you can take a break. For example, such tools
can make reports and presentations. You can automate processes and do a lot
more work, which typically requires manual assistance. Since it reduces the
workload on you and your staff, you can focus on more critical aspects of your
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.
Finally, let’s talk about the actual tools you should consider. The following is a list of 12 Top Business Intelligence Tools in 2021 and their reviews. Based on their features and characteristics, you can select the one that suits you best.
1. Microsoft Power BI
Isn’t it obvious that
Microsoft would have something for business intelligence? Microsoft Power BI is
entirely web-based, and it features downloadable software. Thanks to this
feature, you can access your analytics through your reporting server or a
cloud. You can get a 60-day trial if you want to explore the software first.
This trial includes connectivity to Microsoft applications, Oracle, Sybase,
Facebook, and many other sources. Since you can connect all these platforms,
you can centralize the data and make reports in minutes!
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:
Social media analysis
Up-selling and cross-selling analysis
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.
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 application integration, visualizations, analytic queries,
storage, and data ingestion. The user can easily embed the Good Data analytics
into their mobile applications, desktops, and websites. Pls, you can create
reports and dashboards every day without any professional knowledge whatsoever.
A modular data pipeline and a suitable platform for all developers are vital
features of the software. Plus, Good Data comprises four separate data centers:
Canada, the EU, Dallas, and Chicago.
Additional support and services provide a complete life cycle of data analytics. It includes maintenance, launch, testing, and development. However, despite the training sessions and higher costs, some users still find it challenging to navigate.
Even if you have the top business intelligence tools, sometimes, you need extra help. You need information, support, and professional guidance. ExistBi can do that for you! Business intelligence consultants offer advice to people who are struggling with issues like:
Do I need business intelligence?
What kind of tools would work best for me?
Why isn’t my tool or strategy working the way I want it to?
How long should a business owner or strategist wait before changing the tool and try another one?
Basically, they look inside the business analytics and figure out what’s wrong and what should be done to fix it. Business intelligence consultants collect, organize, and use computerized data to help you solve your problems. You can contact ExistBi to learn more about these services and how to book one for yourself.
Business Intelligence Training
If you want to become a business intelligence analyst or consultant yourself, you need training for that. ExistBi can help you with that as well. We offer five-star business intelligence training at ExistBi. If you want to see some recent testimonials and what you get in the training, you can check out the website for more information.
Above are only some of the top business intelligence tools. There are many others on the market as well. So, if you are confused as to which one to choose and how to make a decision, here’s a small criterion you can use. The software you choose should:
Be easy to use and navigate
Be budget-friendly according to your separate financial status
Enable easy access to data
Allow you to deploy data and use it easily
Now, considering your
professional knowledge, your staff, your budget, you can choose one that fits
this criterion the best!
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!
What is the IBM Watson Analytics?
Let’s take a more in-depth look into what IBM Watson analytics really is: it is a smart, cloud-available solution to data discovery. It features guidance on data exploration and automatic predictive analytics. This way, it allows easy, simple, and effortless creation of infographics and dashboards. As a result, you can get quick and accurate answers to your questions, obtain newer, better insights, and make swift, confident decisions about your business within minutes. And you can do all this entirely on your own, all by yourself!
You don’t need a massive team of experts. Even if you hire professionals, you won’t be clueless about what’s happening on the slideshow. You’ll be able to understand data in a much better way and make sense out of everything. Ultimately, you can make more conscious decisions about your organization and lower the risks of failure.
IBM Watson has three versions: the free trial, Plus, and Professional version.
The free trial gives you access to data, Discovery, and Display tools with the data capacity of 1 MB of free storage.
The Plus version includes all free features with additional complete access to the resources of Analytics Exchange. It has a data capacity of 2 gigabytes of free storage, 256 columns, and a million rows. Plus, you also have the opportunity to purchase more storage.
The Professional version of the IBM Watson analytics includes all the free features and the Plus features. Moreover, it has a data storage capacity of a hundred gigabytes, 500 columns, and 10 million rows.
You can pick and choose whatever works for you based on what you need and what you can afford. Trying the free version first will also give you a quick idea of what the software is doing for you and whether you actually need it. If you have a positive feeling about it and would like to invest in the time, you can purchase the Plus or Professional version.
How To Use The IBM Watson Analytics
It’s not hard to use the IBM Watson analytics; it just requires a little exploring and a lot of practice.
Get started by navigating through their site and signing into your IBM account. If you don’t already have one of these, register yourself as a free or paid user.
The process starts by loading the data and shaping it. It explores all the data and discovers insights, uses visualization tools and dashboarding features to help display, and communicates these insights to the end-user, that is, you.
You can go into your account settings and check out your current account. The tab called Overview gives you the user’s information like your username, active licenses, subscription type, space you’re using, purchase history, and other details.
The next step is to import and refine data. Import data by downloading the CSV file on your computer. Go to the Data tab and choose “New Data” to import the local file.
The next step is data refinement. Click on the ellipses and check out the refine options. You can select particular fields to visualize every value and set specific conditions.
You can also see three expandable windows called:
Column Properties (which reviews data, sorting options, and aggregation modes),
Data Metrics (it summarises data quality, distribution, and missing values), and
Actions (exposes a complete list of auto-detected hierarchies, other available columns, and creates calculations).
You can also use Watson’s cognitive skills to discover insights. Click on the Airline Satisfaction Survey- Refined Dataset. It exposes all the cognitive starting points, relationships, and trends that the software has detected throughout the uploading process.
You can utilize Watson’s natural language processing capabilities, as mentioned earlier.
You can create custom visualizations by choosing a combination chart and dragging it onto a column tray or the x-axis.
There are many other features and components of this software, and it will be hard to put it all in a nutshell here. However, these are all the basics of using the tool. The rest is all about how you experiment and explore the platform to get the most out of it.
The Different Applications Of IBM Watson
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
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.
Law and Legality
This one might be a
little hard to imagine, but it is definitely practical, and it’s happening.
Companies and organizations are using Watson to make law information more
accessible and easy to obtain. They’re aiming to create more awareness, promote
the law’s understanding, and help users understand legal knowledge and use it
to their benefit.
ROSS Intelligence Inc.
is one of the example startups using IBM for law and doing it successfully. The
company is using Watson to obtain answers to legal questions easily and
quickly. As per their website, consumers have the opportunity to ask questions
on the site and get quick, informative, and accurate answers within seconds.
You can use plain English. The application then uses NLP to interpret your
questions. Then, it efficiently filters through an entire database to find you
a cited answer to your problem with legislation that’s relevant to it. The
company also effectively monitors any potential alterations and changes that
happen in the legal world. It then alerts you if any changes have
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
IBM Watson Analytics Architecture
of IBM Watson analytics is not as complicated as you would
think. It basically comprises three Ds: Data, Discovery, and Display.
Data refers to the information it gathers from online platforms, user interaction, and manual input.
The Display means showing the data and explaining it to the user or audience in the simplest way. Finally, the data is collected at the beginning turns into understandable, readable information. Using this information, users can make quick decisions about the future.
IBM Watson Analytics vs. Microsoft Azure
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.
IBM Cognos Analytics, or IBM Cognos Business Intelligence, is a web-based software, an integrated IBM business intelligence suite. It basically provides a complete toolset that enables monitoring, scorecard, analyzing, and reporting metrics and events. The software also consists of many components specifically designed to meet all the necessary information requirements of a company.
In simpler words, Cognos allows the user to create dashboards that are intelligent and interactive. This way, it helps businesses make important, informed decisions. It is based on machine learning systems and artificial intelligence, which helps automate all the data creation processes. It also helps analyze the data and allows the users to obtain required relevant answers to all of their questions.
IBM Cognos offers a free trial for unsure people or just wants to try the tool first before investing in it. As a user, you can explore the entire product with all the features for a maximum of 30 days. Then, you must purchase one of the two paid plans: Premium or Enterprise.
IBM Cognos Training
Using the IBM Cognos
can be a little tricky, so specific training programs make you a certified
Cognos user. Most of these training programs are online courses that involve
business intelligence, data warehousing, Cognos Analytics, and much more.
If you want to know more about how the IBM Cognos training can benefit your organization and the IBM Cognos’ different insights, ExistBi has everything in detail. You can find multiple articles on the subject, including tips, tricks, and other valuable information. Enlighten yourself!
Pros & Cons Of IBM Watson Analytics
Even though IBM has
been a massive hit and is being used in various industries worldwide, it still
has its benefits and drawbacks. While we’re still on the subject, let’s take a
look into what exactly you should expect from IBM Watson
Easily understandable user interface
Strong, secure querying
Information in a visually appealing format
Accessible from various gadgets and devices
Capacity to process natural language
Technologically-advanced guidance features
Patterns are easy to detect
Faster future decisions
Lacks the option to stream real-time
Doesn’t cooperate with relational databases
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:
Assess and document a business’s needs
Determine the scope of implementation
Take into account the available resources
Outline a comprehensive conceptual solution
Chalk out the approach and process
Compute complex data
Test the approach
Implement the solution
Deploy the necessary resources
Develop and deliver the necessary assistance, training and help manual
Review the process
ExistBI can equip you with tools that can help you run your business efficiently and effectively. Our MicroStrategy consulting services are scalable to an organization’s needs and size. Reach us out on our US number +1 800 280 4376 or our UK number +44 (0)207 554 8568.
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 that definition was too complicated for you, here’s a more straightforward explanation:
If you run a business, the data that is relevant to your products and services, as well as your audience, is essential to you for many reasons. So, you probably write everything down in a register. This register now holds your data, and the hardcover protects it from environmental damage, right? Essentially, you’re managing your data! This is data management and how people used to do it several decades ago. Today, you have software and tools that can collect data for you. Also, some specific professionals are dedicated to data management only – data managers. And they don’t just hold and protect your data; they also organize, verify, and process it.
Who Is A Data Manager? What Does He Do?
Data managers are knowledgeable professionals, educated in their specific field of, you guessed it, data management. They help develop and govern systems that are primarily data-oriented. These systems aim to meet an organization’s needs and make decision-making more straightforward and more efficient.
Raising more awareness about data and its importance across an entire organization
Promoting the best practices and implementing them
Promoting the adoption of useful and practical data-related guidelines, standards, and processes
Lowering all types of duplicative efforts
As a data manager, you should also have the following essential and useful skills:
i. Being able to look at important, unorganized data and analyze it
Data analysis is a
massive part of the management. It incorporates looking at various summaries
and lists, identifying any patterns, and analyzing the results. Then, the data
manager should be able to create presentations and make this data easily
readable for other people in the team. His skills also involve using the
information effectively and improving various programs after the analysis.
ii. Database Software Navigation
As mentioned earlier, there are various software and online tools that help with data management. As a data manager, you should be able to navigate this software and use them creatively for benefits.
iii. Files And Account Management
One of the many vital skills of a data manager is to track all the online files and accounts efficiently. By doing this, they help other people in the team keep track of their own accounts, IDs, passwords, and further details. They should be able to organize different files and folders, both on a network and a computer. Plus, they should also have enough knowledge about copying, moving, uploading, and downloading different files and folders. Understanding emails, sending attachments, and managing the inbox is also a part of a data manager’s skill set.
iv. Designing And Planning A Database
Database design concepts should come easily to a data manager. They should understand the different benefits and limitations of various database types, like an online and PC database. Being able to actively participate in both short and long-term plans regarding database projects is a must. Moreover, figuring out an efficient storage and analysis plan is also one of the expertise of a skilled data manager.
v. Understanding and helping small businesses
Data management for small business is a skill. A new startup requires unique expertise in guidance, building effective plans, and implementing various techniques. A skilled data manager should be able to tackle a small business just as wisely as a large-scale organization.
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.
Why Data Management for Small Businesses is Necessary ?
Suppose you run a
makeup store online, offering multiple kinds of skincare and beauty items. You
take orders from customers online and deliver their parcels to their doorsteps.
After a month, you will probably want to know how many people ordered from you,
how many packages you delivered, and what profits you made. When, suddenly, you
realize you never gathered all of that critical information. So, now, you have
no idea what your audience likes, what products are more in-demand, and whether
you made a net profit.
Now, if you had all that information, you would be able to:
Understand your target audience better and focus on their needs
Make your advertisements and marketing strategies more targeted so they would work more efficiently
Innovate your products and services based on what’s more in-demand
Fix prices based on their affordability and your desired profit
Invest in products that give you more profit
Simultaneously track how much profit or loss you’re getting
Take appropriate actions and decisions to grow your business and fix mistakes
This scenario explains
precisely why businesses need proper data management. If they don’t collect,
store, and protect the data they need, they cannot make relevant decisions to
grow. It hinders their growth and development. They make guesses and risky
decisions that should have otherwise been based on evidence and accurate
Data 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:
1. Identify customer preferences
As discussed earlier,
your data helps you understand what your customers like best and what is not
their favorite. Based on this information, you can invest in the products that
your audience wants and is interested in. So, this way, your big data gives you
leverage. It helps you attract your target audience as well as retain it.
2. Identify different trends
A data manager’s job
is to identify different patterns, behaviors, and trends in the data. Are
people moving more towards skincare as opposed to makeup? Is your audience
reacting better to video ads instead of text and graphics? This type of big
data helps you improvise and go with the flow. Otherwise, you’re just stuck in
one place. You’ll keep doing what you have been doing for the past many years.
Even though the trends have changed and these tactics do not work anymore, you
won’t have any idea. Thus, it stops you from growing.
3. Being aware of the competition
Thankfully, we have come very far from when businesses had to pretend to be customers to know more about their competition business and its insights. Today, financial data is readily available. You can do your research to determine which brands are doing better than you and precisely what they’re doing to make them better.
4. Improving transactions, processes, and operations
manufacturing companies can use big data to improve their operations by several
folds. Machines show real-time data, they’re connected to various tools, and
you can quicken many processes that initially involve a lot of time and effort.
Retail companies can now successfully manage their stock based on data
generated from their websites, weather-forecast, web searches, social media,
and whatnot. The possibilities are endless if you’re an innovative individual.
5. Recruit and manage talent
Individual data from
within the business can help you identify more talented, devoted, and
knowledgeable personnel. This way, you can engage them in activities that they
could do better. It helps manage your team so you can get more efficiency out
of every individual.
6. Upgrade business model and strategies
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.
Some data management guidelines and suggested practices for small businesses:
If you are a small business, here are a few tips to make data management more effective and convenient for you:
1. A maintenance schedule is essential
A regular schedule to maintain data is non-negotiable. It helps ensure that your information has zero errors and there is no security risk.
2. Outsource when you can
Contrary to what most
people believe, outsourcing isn’t really that bad. Third-party operators can
turn out to be a great help because they’re sometimes more equipped and ready
to take care of data than you are.
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
4. Prioritize security
important your data is, don’t forget about prioritizing its security and
privacy. Make sure you perform all the necessary measures to protect the
business’s data from hackers, data thieves, and viruses. The security system
should be consistent and very robust.
5. Make sure you have a backup.
Backing up data is
usually forgotten, but it is actually crucial. Use online clouds and backup all
your data there regularly. If not, save your information on an external hard
drive or put it in a USB flash. Make sure you have it on you at all times for
quick and easy backup.
6. Allow access to the data.
It’s good that you
want to keep your data private and secure but don’t forget to make it
accessible for other business members. You and your team members should not
have to spend days trying to unlock and access the information they need.
Here are some faqs about data management…
Why Is Data Management Important In Businesses?
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
How Can Small Businesses Utilize Big Data For Their Benefit?
Small businesses can use big data to decide what stocks they want to invest in and what should be left out. Plus, they can use it to make the right business strategy and be competent enough in the market. A new startup can sometimes have a hard time challenging its competitors. So, data management can become a strong backbone for these small businesses.
What Is A Data Management Strategy?
Data management for small businesses involves a good strategy. It comprises precisely how you’re going to manage the data, the software, the tools, backup, and much more. In simpler words, it’s a roadmap for businesses to achieve the goals they have set. You can learn more about the various important benefits of having a sound data management system on ExistBi. Plus, they offer consultations to help you create a strategy as well.
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.
REFERENCE AND MASTER DATA
Anyone who has been involved in any master data projects will have no doubt heard or read numerous dire warnings about the dangers of attempting these without having Data Governance in place. While I am not a fan of wholesale scaremongering to get people to embrace Data Governance, these warnings are genuine.
For master data projects to be successful, you need data owners identified and definitions of all the fields involved drafted and agreed, as well as processes for how suspect matches will be dealt with. Without these things (which of course Data Governance provides) you are likely to be faced with a mess of under, over, or mismatching!
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.
WHAT HAPPENS IF YOU DON’T HAVE DATA GOVERNANCE?
Well I’ll leave that to you have a look around you and decide what the likely consequences for your company could be, but it is usually the opposite of the benefits that can be achieved.
Remember data is used for dealing with your customers, making decisions, generating reports, understanding revenue, and expenditures. Everyone from the Customer Service Team to your Senior Executive Team uses data and relies on it being good enough to use.
Data Governance provides the foundation so that everything else can work. This will include obvious “data” activities like Master Data Management, Business Intelligence, Big Data Analytics, Machine Learning, and Artificial Intelligence. But don’t get stuck thinking only in terms of data. Lots of processes in your Company can go wrong if the data is wrong, leading to customer complaints, damaged stock, and halted production lines. Don’t limit your thinking to only data activities.
If you are a Data expert who deals with data warehouse consulting and different schemas in data warehouses, you probably already know the importance of these terms. However, if you are a beginner, you probably don’t know the subjects’ basic knowledge. As a data expert, it is essential for you to understand these basic terminologies, what they mean, and what purpose they serve. Throughout this article, you will find everything you need to know about schemas in data warehouse. We will discuss their two significant types, Star schema, Snowflake schema, and each’s advantages and challenges.
What Are Schemas In A Data Warehouse?
Schemas in data warehouse are logical descriptions of a database. One schema is a complete collection of objects like synonyms, indexes, views, and tables from a database. You can arrange schema objects in a variety of ways in different models for data warehousing.
Different kinds of schemas in data warehouses include Galaxy schema, Star schema, and Snowflake schema. We will discuss two of them ahead, but if you want to know more about data warehouses, ExistBi has plenty of information on the subject. You can find out what a data warehouse is, why it is essential, its advantages and disadvantages, and everything else relevant.
What Is Star Schema?
As mentioned earlier, one of the two schemas in data warehouse is the Star schema. It is undoubtedly the most straightforward data mart schema styling. Therefore, it is one of the most widely used approaches when developing dimensional data marts and data warehouses.
A star schema’s characteristics and components include a dimension table that is connected to a fact table through a foreign key. The schema also includes dimension tables that are not interrelated. Other characteristics include BI tools that support a schema, non-normalized dimension tables, easy understandability, and disk usage.
Designing A Star Schema:
Creating a Star schema isn’t a tough job if you know what you’re doing. Understanding how to make it can also clarify many concepts regarding the topic, like what it’s made of, how complex it is, and how you can enhance its usage. Here, the process is broken down into simple steps for you to understand:
Step 1: Identification after the business process to analyze. These business processes include sales.
Step 2: Identification of the facts and measures, such as the sales dollar.
Step 3: Identification of the various factual dimensions. These include the organization dimension, time dimension, location dimension, and product dimension.
Step 4: Organization of the columns describing every dimension, including the region name, branch name, etc. Lining up these dimensions and organizing them is an important aspect of the job.
Step 5: Determination of a fact table’s lowest summary level, which includes the sales dollar.
And that’s how you create a Star schema on your own!
The star schema is so widely used because it has several benefits over types of schemas. Some of these fantastic benefits are the following:
Star schemas have a higher speed, and they are relatively faster.
Their read-only performance is very high and efficient.
Theories are more compatible and manageable to perform since one large table of data represents various dimension tables.
The star schema provides data for Online Analytical Processing systems.
It also simplifies the transactions of making the period over period and business reports.
What Is Snowflake?
This one is the other
type of significant schema in data warehouse. Snowflake schemas are
logical arrangements of various tables In a single multi-dimensional database.
This arrangement happens so that the diagram mimics the shape of a snowflake,
hence its name. This particular schema is actually an extension of the Star
schema, meaning that they’re both pretty similar with added dimensions. In this
schema, however, the dimensional table is normalized and divides the data into
various separate tables.
A snowflake schema
comes with its own interesting characteristics. For example, they are
relatively more high maintenance And require more effort because of the
excessive lookup tables. Plus, they involve multiple tables query, so the
performance is somewhat reduced. They take more time and effort than the Star
schema, which is why it intimidates many people. However, if you know how to
make it and understand its composition, you can slowly start to like it!
Designing a Snowflake Schema:
Like the characteristics,
creating a Snowflake Schema is also different from that of a Star schema. The
following parameters are a part of this process:
Name: you must create a unique name for your schema.
Transient: it presents a schema that is temporary and volatile. Hence, it is automatically deleted once you terminate the session.
Clone: a clone creates an identical copy of a schema that already exists. You simply have to enter the specific name of the selected schema.
At|Before: this part provides a timestamp for cloning an existing schema. It chooses a particular period from where you wish to copy the data.
With Managed Access: this particular field identifies managed schemas. It adds you to monitor your access controls.
Data Retention: data retention specifies a particular number of days that the object remains retained within the memory. Data retention has a default value of 1, but you can alter it as you wish.
Comments: the comments provide a minimalistic description of your schema that you just created.
And this way, you can
create your own schema using these specific components of a Snowflake schema
Despite the challenging characteristics we just discussed above, there are some significant advantages of the Snowflake schema. These benefits include:
A Snowflake schema occupies a much smaller amount of disk space compared to the Star schema. Lesser disk space means more convenience and less hassle.
Snowflake schema of small protection from various Data integrity issues. Most people tend to prefer the Snowflake schema because of how safe if it is.
Considering that both
the systems have their perks and drawbacks, different experts prefer Snowflake
and Star schema depending on their needs and preferences. The Snowflake schemas
generally take up less space, which is always convenient. However, the Star
schema is much faster and involves a more straightforward design. So, depending
on what your priorities and needs are, you can choose one that fits you best.
That being said, IT teams around the world generally like to prefer the Star schema versus the snowflake schema. This worldwide preference is a result of several reasons. One of these reasons is that a star schema consists of one or more tables, much more straightforward than the other schema. Since this schema does not compromise the team’s speed and efficiency, experts around the world tend to widely use the Star schema, as mentioned in the beginning.
Examples of Dimensional Schemas
Apart from the Star
schema and Snowflake schema, there is another type of schemas as well. It’s
called the Galaxy schema or Fact Constellation Schema.
This one is another
extension of the star schema and is a collection of multiple stars. A fact
constellation measures online analytical processing, and it consists of
dimensions segregated into several independent ones depending on their
hierarchy levels. It has various fact tables and is often called a Galaxy
schema, even though some argue that they’re both different systems. At this
point, there is quite a lot of mixed information and opinions you’ll find on
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.
Is Snowflake OLAP or OLTP?
Before discussing the
answer to this question, let’s first discuss the terms OLTP and OLAP and what
they stand for.
Both of these are
different systems. OLTP refers to online transaction processing, which gathers
data from various transactions and stores, processes, and captures them in
real-time. On the other side, OLAP involves analyzing aggregated historical
data through complex queries from OLTP systems.
Now, let us use this information and co-relate it with the question. Apparently, a snowflake schema is an OLAP system and was specifically designed to be one. One of the most significant and highlighted aspects of a Snowflake schema is that it separates between processing and storage, clearly making it an OLAP database.
What Are The Major Differences Between The Star And
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:
The star schema offers queries with relatively higher performance through the Star Join Query Optimisation system. The tables in the schema can connect through multiple dimensions. In contrast, the Snowflake schema involves a centralized fact table, improbable to connect with other various dimensions.
Cube processing is much faster in a Star schema as compared to the Snowflake schema. The reason behind this difference, as mentioned earlier, is because a Snowflake schema is much more complicated, and it requires more time and effort.
Thanks to this reduced time and effort, the productivity and efficiency levels are much higher for star schemas compared to Snowflake schemas. Since the processes are simpler and easier, transactions are smoother, and results are faster, more accurate.
Star schema also has a higher data redundancy. In contrast, a Snowflake schema has deficient levels of data redundancy.
The single dimension table of a Star schema consists of aggregated data while the data is split into various dimension tables in a snowflake schema.
Star schemas have a de-normalized data structure, which is why their queries run much faster. On the opposite side, a Snowflake schema has a normalized data structure.
A Star schema has a relatively more uncomplicated and more straightforward DB design, while a snowflake schema has more complex and complicated DB designs.
Star schemas involve a single join only, which generates a relationship between dimension tables and a fact table. A Snowflake schema, however, needs multiple joins to gather the data and collect it.
Star schemas involve fact tables that are surrounded by multiple dimension tables. A snowflake schema contains just one fact table that is surrounded by dimension tables.
Hierarchies in a star schema gather in a dimensional table, while the hierarchies in a snowflake schema are further divided into multiple tables.
Which Schema Is Faster, Star Or Snowflake?
As discussed earlier, Star schemas are widely popular for their fast speed and efficiency. Since their dimension tables and fact tables are much more straightforward, they result in faster, more straightforward SQL queries. For this reason, IT teams and specialists around the world prefer to use the Star schema since it provides aid and speeds up their work. Snowflake schemas, on the other hand, use less space compared to a Star schema, but they are relatively more complex. They require more effort, so they take more time and lower efficiency.
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.
What are Webhooks?
Webhooks are simple techniques through which one computer system is able to notify another when an event happens by using typical web technologies, like HTTP and JSON. Webhooks enables you to join Tableau to your apps, which means any action in Tableau Server or Online can trigger a different app. To understand this in a simple way for initial setups, review this example, it will send an e-mail alert whenever a new workbook is published or deleted. And in complex setups, you can integrate various Tableau triggers to refresh extracts in superior workflows. Webhooks brings in a lot of stimulating opportunities to automate your Tableau usage. So here you’ll know what Webhooks are, why you should use Webhooks, and how can you use them in your Tableau setup.
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:
Constantly Checking: – Continuously keep an eye on system X to check if the particular task is going straight or not. In that case, System Y has to uphold a copy of the preceding status of System X and continually test out for a new state that imparts some additional load on System X. Both systems are doing a ton of extra work for something that may not happen that often.
Scheduled Checking: -Check the System X after a specific time interval or at a scheduled time. The load is compacted by merely checking from time to time, but also depends on the schedule period. There may be extensive delays for checking by System Y whenever anything happens on System X.
Requesting Notification: – In this process, System Y asks System X to informs when some specified events occur and after that waits for the notification. And when anything happens, System X informs System Y.
What is the Use of Webhooks in Tableau?
Webhooks has great potential to perform the following works:
When refreshing an extract fails, automatically it files a ticket in Service.
When updating of the workbook completes, it notifies your team through their Slack channel.
When publishing of data source is done, emails a data steward requesting the team to evaluate and verify it.
When refreshing the workbook is completed successfully, produce a PDF, and publish it to SharePoint.
Webhooks will always inform you when something happens in a system on Tableau, so the information will help you to understand that when you can proceed further. In the preliminary release of Webhooks with Tableau 2019.4, there are only 13 events accessible to create custom workflows:
For Data Source
Source Refresh Started
Source Refresh Succeeded
Source Refresh Failed
With the upcoming releases, more events are expected to be added to the workflow in Tableau 2019.4.
Create and Manage Webhooks
Site and System admins are allowed to create and manage Webhooks with RESR API within the site. Either you can write your own code for this, or you can utilize the Postman API Client tool from the existing Webhooks REST API collection. “Postman” is a great tool that allows easy access to RESTful API, and you don’t require writing code.
For creating a Webhook message, these three things need to be
specified in Webhooks to issue a create command in endpoint:
Event for which you want notification/alert
URL where you want to receive the message
Name defined for the task done by Webhook
Testing of Webhooks
You must need to verify and test the created Webhook carefully to find whether it is working correctly or not, and then you can build your workflow accurately. Luckily, you’ve got a number of sites, such as webhook. site or testwebhooks.com, which provides free access to test your Webhooks without doing any kind of setup. They do provide a temporary URL to point at your Webhook.
For testing the Webhook, point it at the URL presented by the site and click the Webhook. If everything is fine and functioning well, a pop-up message will appear inclusive of information regarding the event.
Responding to Webhooks
You need a well-developed system for responding to the messages received through Webhooks. You might require an IT specialist or developer to build such a program. Moreover, there are several low-code websites like Zapier and Automate.io, which offer native support for Webhooks and help to create automated workflows.
Start Automating Your Workflows!
Webhooks is a general approach to activate automated workflows that counter any action on events in your Tableau environment. So you can initiate creating custom workflows with Tableau Server and Tableau Online with the upcoming release of Tableau 2019.4.
You can sign up for a Tableau Bootcamp consulting to discover more features and functionalities of Webhooks in Tableau. ExistBI offers Tableau training and Tableau consulting in the US, UK, and Europe. Join the Tableau 2019.4 beta to start creating automatic workflows today.
Business intelligence revolves around different technologies and strategies to analyze business information. Simply put, it allows you to interpret the stats of your business, measure its growth, and make appropriate decisions based on data. The reason why we’re discussing this complex topic is its rising importance in the world of commerce. Today, every business person should know what business intelligence is and how they can incorporate it to develop their company. If you’re interested to learn more about it, let’s discuss the top twelve business intelligence trends in 2021.
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12 Business Intelligence trends of 2021
Considering how fast technology and businesses are developing, it is essential today for every business person to be aware of the current and upcoming business intelligence trends. In order to help you be a step ahead of the game, here are the top twelve business intelligence trends you should work on.
1. Collaborative Business Intelligence
Interaction within a business was always important. However, the newest trends involve a different kind of communication. We’re talking about the latest tools and software to communicate within a business community. These include modern technologies, social media, and various applications. Such advanced and real-time communication allows the business to make collective decisions based on collaborative information and information enhancement.
2. Data Discovery and Visualization
Data A has only increased in terms of importance during the last year. Naturally, data discovery tools have also seen a spike and are expected to become more in-demand in 2021. Similarly, the use of tools for online data visualization has also become a crowd-favorite. They have become a valuable resource for developing relevant insights and a sustainable process for business decision-making. Furthermore, these tools enable easy management of various kinds of heavy volume data. Plus, they are straightforward to use, highly flexible, and reduce the time and effort to insight.
3. Self-service Business Intelligence
The world of technology is changing so fast that you no longer need extensive teams and professionals to handle analytics tasks for a business. The latest trends in business intelligence promote self-service interfaces so you can manage your own analytical procedures. Such services and tools allow business users to handle data tasks themselves without IT teams and data scientists’ involvement. It is especially beneficial for small businesses that cannot yet afford to hire professionals.
4. Mobile Business Intelligence
Since 2020 was all about using your smartphone for everything, even business intelligence has made its way into your pocket. The newest trends support access to BI data and tools through your mobile phone. It’s not only going to make navigation more comfortable but also reduces the need to carry bulky computers and laptops all the time.
5. Embedded Analytics
Embedded business intelligence significantly decreases the workload of a data worker. It gives them a much faster way to generate insights so they can focus on more things. These analytics provide the users with better power to work with data by zooming in, aggregating it, and looking at it from multiple angles by just pressing a button. Since it’s so beneficial for the users and data workers, this trend will hopefully see a significant spike in 2021.
6. Story Telling
It has always been a concern that business owners and managers cannot interpret and understand the data provided and interpreted by analysts. Since they don’t have the proper knowledge or know-how of the jargon, they cannot utilize all of that valuable information to make the right decisions.
However, in 2021, this problem will hopefully see a solution. The upcoming trends involve analysts describing the information in a very story-telling manner. This particular technique adds a little context to the data and statistics. This way, it provides a proper narrative for business management to use all the insights and make the right decisions.
7. Data Governance
The process of data governance stipulates blueprints to manage a business’s data assets. It includes the process, architecture, and operational infrastructure. Simply put, data governance forms a robust foundation for organizations to manage their data on a broader scale. Overall, the process impacts the strategic, tactical, and operational levels of an organization and, as a result, helps the business use the data as efficiently as possible. The trend is seeing a significant rise and will be even more popular in 2021. Why? Because it will help instill confidence in business leaders and promote the use of business intelligence.
8. Connected Clouds
The use of connected clouds has seen a significant spike during 2020 for obvious reasons. However, it is easy to say that this trend is not going anywhere in 2021 either. The cloud-connection strategy reduces costs and risks associated with data work. Plus, it provides the required flexibility to develop relevant essential data and use it to make data-driven decisions. Moreover, it enhances the quality of real-time communication within the business, and we’ve already discussed how important that is.
9. Data Security
After the significant
security breaches on popular online platforms, like Facebook, businesses and
consumers have become aware of how important this concern is. As a result, data
security trends have been on the rise since 2019. Experts say that the trend
will prevail in 2021 as well. Database security has become a priority for all
businesses to avoid breaches and cyber-attacks. They’re taking appropriate
measures, using the right tools, and taking the proper precautions to make sure
it never happens again.
10. Artificial Intelligence
In the last decade or so, artificial intelligence has improved by several folds. Lately, it has started to make a significant appearance in business intelligence as well. Speculations for the year 2021 say that the latest digital assistants will make work even easier for data workers. They will simplify business intelligence processes through voice-activation, voice transcription, and efficient conversion of data.
11. Predictive And Prescriptive Analytics Tools
Predictive analytics allow data workers to extract information from a bundle of data and set it in order. Doing this will enable them to forecast probabilities for the future and take the necessary precautions and actions. Similarly, prescriptive tools are a step further than that. They help you examine data and content to make essential decisions and take the right steps to achieve a goal. The techniques involved in the prescriptive analysis include simulation, graph analysis, neural networks, heuristics, complex event processing, machine learning, and recommendation engines. All of these techniques help you optimize production, scheduling, supply chain, and inventory to deliver to your customers efficiently.
12. Real-Time Data And Analytics
Up-to-date information and real-time data have become more and more critical during the last several years. Thanks to the quick collection of information, analysts can now be fully and quickly aware of the business’s ups and downs. In 2021, this trend will also see a significant spike. The analytics industry and business intelligence will incorporate more real-time data for forecasting, alarms, and business development strategies. Based on the real-time data, they will respond appropriately and make the right data-driven decisions.
about Business Intelligence
Apart from the significant upcoming business intelligence trends, there are certain other aspects that most people are curious about.
1. Does Business Intelligence Have A Future?
The use of the latest technology, artificial intelligence, and efficient strategies have become more critical for businesses and organizations. There is overwhelming pressure on various industries to implement all of these changes. It’s becoming an absolute necessity for companies and bodies worldwide to adapt to these changes. As a result, the incorporation of business intelligence is helping companies stay relevant, robust, and competitive.
As for the BI industry’s future, the trends that have been on the rise and are upcoming in 2021 seem to promise a rapid shift in the business intelligence landscape. It’s safe to say that yes, business intelligence has a bright and shining future!
If you are willing to learn more about it, ExistBi offers business intelligence consulting services. You can know more about what it is and how you can use it.
2. What Companies Use Business Intelligence?
Generally speaking, almost every kind of company and organization can take help from business intelligence. If you are looking for some real-world examples, here are a few of the most popular brands that go hand-in-hand with business intelligence:
Des Moines Public Schools, and many more.
3. What Are Some New Uses Of Business Analytics That May Become Possible With This Trend In The Next Few Years?
If 2021 goes as
planned and speculations come out correct, business data will become easier to
interpret. Everyone will be able to collect, analyze, and use data for proper
business development, strategies, and growth. Also, data and content will
become more secure, and consumers will feel safer purchasing and transacting
Also, small businesses
will no longer need to hire extensive teams and expensive professionals. They
can use online and offline data tools to do the job on their own. Doing this
will reduce the cost of their business maintenance and also be a more
sustainable option. Moreover, we will be able to analyze business data from
different sources at a time. Advanced tools will help find any hidden patterns
in large sets of data. Interactive reports and dashboards will help disseminate
important information to the relevant stakeholders. Businesses will be able to
react and monitor KPIs according to the changing trends and in real-time.
4. What Are The Different Stages Of Business Intelligence?
Essentially, business intelligence has four stages:
a. Information Gathering
This step involves
preparing data from all of your existing sources like your files and financial
database. You can also collect data externally from online surveys,
questionnaires, polls, or other people. Once this feedback data is collected,
you can move on to the next step.
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.
d. Monitoring & Prediction
The final part of a business intelligence process takes you back to the first page. You monitor the data that you first collected and notice any changes or ups and downs. Monitoring has three common types: Dashboard, KPIs (key performance indicators), and business performance management.
Then, you predict. Prediction helps you foresee the future and make appropriate decisions accordingly. The prediction part has two major types in business intelligence: data mining and predictive modeling.
5. What Are The Benefits Of Business Intelligence?
Apart from all the advantages and benefits, we have discussed so far, here are some more benefits of incorporating business intelligence:
BI improves data quality.
It increases its competitive advantage.
Your rate and ratio of customer satisfaction improve dramatically.
Your employees are satisfied, and their retention rate improves.
The customer retention rate also improves significantly.
You can make better, quicker decisions about your business and organization.
Your planning, analysis, and reporting will become more accurate by using business intelligence systems.
Planning, analyzing, and reporting also becomes very fast and needs less time and effort.
The overall costs of your business development and maintenance reduce dramatically. It is especially beneficial for small startups or companies that are facing a significant financial downfall.
You will notice a saved headcount by using business intelligence.
Revenues will significantly increase since the quality of work improves, and the costs go down.
You will no longer need to depend on someone else or be unaware of what’s going on in your business.
You can take matters into your own hands and make decisions by fully understanding the context.
Since you are a significant part of the decision-making process and know what you’re doing, there will be fewer risks of failure and loss.
Conclusion-Learning about Business Intelligence
If you wish to learn more regarding business intelligence, why it matters, and various examples of it, ExistBi can help. Hop on to the website to know the difference between modern business intelligence and traditional business intelligence, how to choose a BI tool, Business intelligence consulting, and much more.
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.
What Is Data Science About?
Let’s first briefly discuss what data science is and what it involves if you’re new to this concept and the big data industry. Data science utilizes systems, algorithms, processes, and scientific methods to extract necessary information from data. This data could be structured or unstructured. Data science revolves around machine learning and data mining.
In simple words, data science is the extraction of meaningful insights from a piece of data through domain expertise, statistics, and mathematics and programming skills.
What Does A Data Science Course
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.
What Does A Data Scientist Do?
The useful information extracted by a data scientist helps businesses make crucial decisions. Analysis and structuring of raw data can help companies make valuable changes to their strategies, understand their profits and losses, and grow financially. Moreover, by sharing and extrapolating such useful insights, these scientists can help businesses come out of financial crises and solve many problems.
What Are the Major Differences between
Data Science and Business Analytics
With the growing availability and accessibility to both these programs, people must learn the difference between them.
So, let’s compare them, shall we?
Data science is studying data with the help of technology, algorithms, and statistics. In contrast, business analytics involves statistically analyzing business data to gain insights.
Data science utilizes both structured and unstructured data, while business analytics mostly involves structured data.
Data science incorporates a lot of coding. It is a perfect blend of excellent computer knowledge and traditional analytics. In contrast, business analytics does not include a lot of coding. It is more oriented towards statistics.
In data science, scientists use statistics after finalizing the analysis after coding. In business analytics, however, the analyst completes the entire research on the basis of statistical concepts.
Data scientists study almost all kinds of trends and patterns, while business analysts study trends and patterns only specific to businesses.
Industries like e-commerce, machine learning, manufacturing, and finance are the topmost applications of data science. These industries are telecommunications, supply chain, marketing, retail, health care, and finance for business analytics.
So, as is evident here, both business analytics and data science are entirely different. Both fields incorporate different strategies, and both types of professionals have varying jobs and opportunities. The point of discussing this comparison was that you should know what you’re getting yourself into and what your opportunities look like. You should always know the difference between two similar professions, especially if you’re trying to pursue one of them.
Leveraging Data Science To Combat
Amidst the pandemic, it has come to everyone’s attention that data scientists have become more valuable than ever. Indeed, their role in making businesses flourish is undoubtedly significant. However, since the pandemic started, they have also made a significant contribution in helping manage healthcare departments.
Since 2010, we have been growing our knowledge and capabilities in terms of algorithms, identifying patterns, and obtaining insights. However, since the pandemic hit us, the world of data science has seen abilities and potentials beyond what we ever imagined. Data scientists are now using their capabilities to help predict how the disease will affect various businesses and industries. So, is the pandemic opening a new door for data science? You might rightly think so.
Data scientists provide their skills and services for screening, analyzing, predicting, forecasting, tracing contacts, and developing drugs and solutions. If you think this is great, experts speculate that more of such services will come from the data science field very soon if the pandemic continues.
Experts speculate and hope that, soon, machine learning and data science will help categorize and predict which people are prone to getting the disease and which ones are immune and safe from Covid-19. Such categorization will be of immense help in many ways. It will help prioritize those who need the treatments and vaccinations first and who can wait. Plus, it will help reduce the spread of the disease and contain the damage.
Moreover, data science is also helping us keep track of Covid-19’s spread worldwide. Plus, we are also using data science at a macro level to assist what information and results disseminate and where. Such data and information help keep track of where the disease might spread next, where it can do the most damage, and how many waves we can expect.
Apart from these services, data science is also playing a vital role in making industries and businesses run. It’s helping companies make proper arrangements and decisions according to the data they receive and the information they structure out of it. This benefit applies to both private and government industries, and it’s helping many countries stabilize their economy and prevent them from collapsing.
Covid-19 Data Science Urban Epidemic
Modelling And Visualization In Python
Since coronavirus spreads from person to person, to keep track of the virus among the population, you must keep track of the people. The disease goes where the people go. Understanding, analyzing, and predicting their movements can help us understand how the disease spreads, where it’ll spread to, and how to stop it effectively. Predicting where a disease virus is saturated and where it will move on to the next can help control the damage and minimize it.
Urban epidemic modeling and visualization in python is precisely this. Python is a coding language you will typically see in data science. Python is the most preferred, most popular programming language among data scientists worldwide. The reason for all this love for the language is how versatile it is. Data scientists use machine learning, special statistics, and complex networks to understand urban communities’ mobility.
Keeping track of the disease’s potential carriers can help you make proper predictions and appropriate measures to stop the disease’s spread. Data scientists use python to build epidemiological models while taking into account urban mobility patterns. Then, they convert the data into understandable, visually pleasing graphics and diagrams. This process includes simple mathematics, statistics, formulations, and special equations. Then, they present the information on a map so that it is easy to understand and interpret.
Online Data Science Courses During
With the rising demand for data scientists, more and more people are looking up to this profession and adopting it as a career. However, even those who are already certified need to take their game up a notch. The data world is transforming, and if you’re not up-to-date, you’ll soon be far behind the world and what it needs right now.
ExistBI provides valuable data science courses and consultation during the pandemic. If you are a learning data scientist, you can get Big Data training classes on ExistBI. The training classes include:
1. Big Data Training For Beginners
This one-day course aims to provide you with a basic competitive understanding of all the Big Data topics, primarily Hadoop. You’ll learn what Big Data is when we should consider something like Big Data, what a Big Data system architecture looks like, and much more. You’ll learn all about the ecosystem, the key players in the space of Big Data, and whether it’s related to technology and data volume. In the end, you’ll identify whether it can enhance the existing technologies or completely replace them.
What’s more, you don’t require any programming experience to enroll in this training. If you’re someone who needs a complete overview of what Big Data is, its various components, and more about the Hadoop ecosystem, this class is fit for you.
2. Fundamentals Of Big Data And Hadoop
This three-day course aims to provide a maximum understanding of Big Data, including the basics as well as its usage. You will learn what Big Data is and what its architectural system looks like. Along with this basic knowledge, you’ll learn about the implementation of Hadoop jobs for the extraction of business values from vast and varying data sets. You’ll also learn the development of queries to simplify data analysis using Impala, Cassandra, Pig, and Hive.
3. Big Data Analytics Training
Big Data Analytics is a 3-day course that helps improve and expand your skills. The topics include data visualization, statistics, and data mining. You’ll learn how to analyze a larger, more massive amount of data and use this data for the management of risk. This way, you will help make businesses change their route from collapsing to flourishing. It will help to make crucial business and financial decisions.
This course aims to define Big Data Analytics, exploring big data, and explaining the difference between real-time data processing and batch processing. Moreover, you’ll experience both supervised and unsupervised learning and understand the difference between the two. Mining techniques, handling stream data, and defining strategies Big Data is all a part of this course.
4. Big Data For Advanced Learners
As the name suggests, this particular training program is for advanced learners. However, the good news is, you still don’t require any prior programming experience to take this training. (though if you have prior knowledge, it will certainly be helpful). The training course goes on for four days and includes hands-on exercises. These activities and exercises will help you gain a stronger understanding of the Big Data platform and ecosystem.
The entire course has four models, three of which include lectures with hands-on labs. Module one will introduce the subject, while two, three, and four will be all about the architecture, tools, and analytics.
5. Informatica Developer Tool For Big Data Development
This two-day training has objectives like data extraction from flat file sources and relations, parameterized mappings, development of mapping transformations that are most commonly used, and much more. The course is easily applicable to the 10th software version, and you can learn the Data Integration mechanics through the Developer Tool by Informatica. Through this course, you’ll learn about the key components of development, configuration, and deployment of data integration mappings.
6. Informatica Big Data For Developers
This one is a 3-day course that you can get on-site or virtually. You’ll learn the definition of big data, leverage the Informatica smart executor, and describe the way Informatica reads, writes, and parses data collections by NoSQL. This course has 11 modules in total. Each of them has valuable information on big data basics, data warehouse off-loading, Big Data management architecture, and much more.
7. Integration of Informatica Big Data
This three-day training program aims to describe how to optimize data warehouses in a Hadoop environment. Plus, you will learn the processing of different file types using Hadoop that you cannot process using traditional Data Warehouse settings. You will also learn how to describe optimum methods of map designing while executing Hadoop’s Informatica mappings. You’ll learn all this and much more!
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 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
What is Big Data?
Big data is an advanced technology that can digitally store a great number of informations. It can help to computationally examine to show patterns, trends, institutions, and gaps. In addition, it can help in showing insights into the spread and management of the Coronavirus. With comprehensive data shooting capability, large data may be properly used medicinally to lessen the probability of spreading this virus.
Big Data is a phrase used to refer to a group of information that’s substantial in quantity and is continuing to rise exponentially with time. In summary, such information is so big and complicated that none of the conventional data management tools have the ability to keep it or process it economically.
How Big Data Can Help in Fighting Against Coronavirus (COVID-19)?
Scientists and medical professionals require unprecedented
information sharing and collaboration to understand COVID-19 and produce a proper
cure to end the pandemic.
Although fever and cough have been considered as the most common symptoms of Coronavirus. Researchers and medical professionals have published a study that loss of taste and smell were the first symptoms to predict that a person could be infected. That insight came from data shared with millions of people who reported through different phone apps, or any other media. Scientists are extracting a huge amount of data to anticipate Coronavirus outbreaks particularly in communities and to research different risk factors for the illness.
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.
How to Secure Patient Data
Since the data includes information such as places and dates
essential to monitor the outbreak, scientists and medical professionals required
to develop security plans to protect patient privacy. To begin with, data is
put in a safe enclave, meaning it cannot be downloaded or eliminated from its server.
In reality, it cannot even be seen directly by the majority of the researchers
utilizing it. Rather, they need to program software that could analyze the data
and give answers.
In Europe and America, privacy concerns for people are of larger concern than they’re in China, nevertheless, medical research workers and bioethics specialists understand the ability of technologies to encourage contact tracing in a pandemic. Oxford University’s Big Data Institute worked together with government officials to decipher the advantages of a mobile app that could provide invaluable data to get an integrated Coronavirus management plan. Since almost half of Coronavirus, transmissions happen before symptoms occur, efficacy, and speed to alert individuals that might have been vulnerable are overriding during a pandemic like Coronavirus. A mobile app can accelerate the notification process while preserving ethics to slow down the speed of infection.
Tech innovators had worked on alternatives to efficiently track and monitor the spread of Flu. In the USA, the authorities are in conversations with technology giants like Facebook, Google, and others to determine what is potential and moral in terms of using location data from Americans’ smartphones to monitor movements and comprehend routines.
Dashboards from Officials to Track and Outbreak Analytics
Another tool that’s been useful for private citizens, authority policy-makers, and health care professionals to find the development of contagion and also to develop models of how invasive this virus will be are dashboards from organizations like the World Health Organization that offer real-time statistics. These stats show the data around the world in terms of confirmed cases and deaths from Coronavirus and locations. These dashboard data sets can then be utilized to predict red zones for the pandemic so you can make decisions to stay home and help healthcare systems prepare for a surge of cases.
Outbreak Information carries all available information, including the number of verified cases, deaths, and tracing contacts of infected individuals, population densities, maps, traveler stream, and much more, then processes it via machine learning enabling the user to make models of this illness. These models represent the very best predictions concerning summit infection rates and results.
[Case Study] How Big Data Analytics Succeed in Taiwan
As Coronavirus rapidly spread in China, it had been presumed that Taiwan will be greatly hit in part due to its proximity to China. In addition to the flights which moved from Taiwan to China daily, and the number of Taiwanese citizens works in China. But, Taiwan utilized technology plus a strong pandemic plan created following the 2003 SARS epidemic to minimize the virus effect on its territory.
Part of their approach integrated the federal medical
insurance policy database with data from its own immigration and customs
database. By centralizing the information in this way, when confronted with Coronavirus,
they were able to receive real-time alarms regarding who may be infected
according to symptoms and travel history. Along with this, they had QR code
scan and internet reporting of traveling and health symptoms that aided them to
classify travelers’ infection risks and also a toll-free hotline for citizens
to report suspicious symptoms. The authorities took quick action when they got
the first reported case of Coronavirus, and Taiwan’s rapid response and
application of technologies would be the probable reason they have a lower rate
of infection than others regardless of their proximity to China.
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.
Data migration Services involves transferring data from one application to another application, database, or the cloud. Most people opt for data migration to shift data from one place to another or transfer from one email client to another. It has become a common requirement, you, therefore, need to build a data migration strategy that will help you manage data migration.
What is Data and Data Migration?
The process of moving data from one place to another is known as data migration. This process selects the data that has to be migrated moves it to a designated storage system. It is also referred to as system storage migration. In addition to this, data migration services can help in transferring on-premises infrastructure to cloud-based storage/applications.
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ROI reduces the costs of media and storage
unnecessary interruption activities
daily manual effort for business operations
the productivity of an organization
the growth of the business
Types of Data Migrations
All data migrations are not conducted from the same sources. Generally, the migration is expected to include storage, database, application, cloud, and business process migration.
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.
Difference between Data Migration, Conversion, and Integration
Data Migration- Transferring data between storage devices, locations, or systems. It includes subsets, such as quality assurance, cleansing, validation, and outlining.
Data Conversion- Converts data from a legacy
application to a modernized or new application. ETL (Extract, Transform, and
Load) process is used.
Data Integration- Combines stored data existing
in different systems to generate a unified view and overall analytics.
Risks and Challenges in Data Migration
People often find data migration as a risky and difficult task and it is definitely not an easy process. It is time-consuming, which needs detailed planning and implementation strategy and there is always some risk included in projects of this scale. Let’s take a look at some key challenges.
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
Impacts the Business
Many IT teams choose to do a migration process in-house to
save funds, or the management team makes this decision for them. But doing it
by yourself is hardly ever a good strategy. Migration is an uncertain business
with major business inferences and requires widespread expert attention.
A badly run data migration project causes extensive downtime, loses data, misses deadlines, surpasses budgets, and results in balanced performance.
Planning A Successful Data Migration Strategy
Regardless of the intricacies and risks, IT should ensure a successful process within budgets and time limits. The project will require knowledge, strategic planning, management, and software tools.
A well-functioning data migration plan will include the following steps:
Budget for Expert Help
Many IT organizations aim to be practical and some migration
budgets do not allow for expert guidance. However, unless IT already has
migration specialists within the team, they can save money and time by hiring
consultants who have experience and expertise in data migrations.
Plan the Strategy
Be aware of the design requirements for migrated data together with migration schedules and priorities, backup and duplication settings, capacity planning, and prioritizing by data value. It is the step where the IT team needs to decide on the type of migration execution schedule; it can be a big bang or a more gradual tickle migration.
Let’s take a look at these terms:
Big Bang migration involves the complete transfer within a
limited time interval. There is always some downtime during data processing and
transfer, but the project is finished rapidly.
Trickle migration executes the project in stages, including
operating source and target systems simultaneously. It is more complex than Big
Bang and takes more time, but has less downtime and more chances of testing.
Work with Your End Users
Consider the data migration process as an important business process instead of just a set of technical steps and engage your end-users. They will have comprehensible concerns over the success of the migration project. Work with them to know the data rules and definitions, what data is the focus in compliance, and priority data that should move first. Also, realize what they are trying to achieve in the process- Is it for Analytics or better performance? A simple way to subject legal holds?
When you spend time working with the end-users, you will
understand more about a successful data migration project in less time and at a
Audit the Data and Fix any Concern
Firstly, you need to know how much amount of data you are migrating, target storage capacity, and growth opportunities. Database migrations need auditing the source database for idle fields, outdated records, database logic, and making changes before moving data to a new platform.
Storage migration is easier because you don’t need to update the older storage and plot to the new. However, migrating data between two storage systems is not as simple as just copying data from one secondary system to another. You can use software tools to find out dark data and remove or archive them correctly before the migration. It is important to erase obsolete files, discarded e-mail accounts, and out-of-date user accounts. Figure out and compress the source data if you are migrating data over the WAN, then transfer and test.
Backup the Source Data before Migration
Even if the worse happens, if you lose any data during the
migration, you should be prepared to restore it to the original systems before
starting again. It will be best practice to create a backup image that you can
instantly restore to the original system if you lose data in the migration.
Move and Validate the Data
Invest in an automated data migration tool that enables you to plan staggered migrations of data subsets, validates data integrity in the target system, and sends reports for troubleshooting and confirmation. Protect databases during dynamic migrations with a software tool that connects the source and target databases in real-time.
Final Test and Shutdown
Once you have transferred all data- then you can test migration using a reflection of the production background. When all the checking is done, carefully go-live, and carry out final tests. After the new environment starts running smoothly, you can shut down the legacy system.
What is the need for Database Migration?
In this competitive world, modern needs have given companies some of the evident reasons to adopt new technologies. It includes the speed of doing things, standardization of overall performance, etc. Now when it is clear what database migration is, then you need to know the reasons for performing database migration. Let’s check out these reasons below:
To Save Expenses
Making use of old databases might increase overhead expenses for the company. It is similar to installing other applications or systems to work in a speedy mode. They will transfer its database to a platform that will serve their purpose in a competent way. It will help in savings on infrastructure, personnel, and expertise required for supporting it.
Upgrade to New Technology
It is a common reason for migration, where the company would
move from either an out-of-date system or a legacy one to a system that is
intended for the modern data needs.
In this age of big data, adopting new and proficient storage
techniques is a need. For example, a company might select to shift from a
legacy SQL database to a data lake or any other agile system.
To Decrease Redundancy
Data migration is a vital task for the companies in order to
transfer all the company data to a single location. It will help in reducing
redundant data. Also, the data saved in one place can be easily accessible by
all the departments of the company.
Sometimes, it happens after acquirement when the systems
require to be united. It can also occur when various systems are siloed across
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.
Why Should You Hire Experts for Data Migration Services?
The process of moving data from an old application to a new one or a completely different platform is managed by a team of data migration experts. These data migration experts plans, execute and manage to change forms of data for organizations, particularly streams transferring between different systems.
Data migration professionals generally manage the following responsibilities:
Connect with clients or management to identify data migration needs
Strategize and plan the complete project, comprising migrating the data and converting content as necessary, while evaluating risks and potential impacts
Audit available data systems and deployments and find out errors or areas for improvement
Cleanse or convert data so that it can be efficiently migrated between systems, apps, or software
Manage the direct migration of data that may require slight adjustments
Test the new system once the migration process is completed and check the resulting data to discover errors and points of corruption
Document the whole thing from the strategies implemented to the correct migration processes put in place, including documenting any fixes or modifications done
Build up and recommend data migration best practices for all present and future projects
Ensure compliance with regulatory needs and guidelines for all migrated data
If you are considering migrating your data from one system to another, it’s best to get expert support. Otherwise, it may result in a loss of time and data. You will be provided help with setting up your plan, strategy, and overall compliance to conduct a complete data migration. ExistBI offer Data Migration services in the United States, United Kingdom, and Europe, contact us to find out more.
Nowadays, organizations are facing tremendous pressure to get better healthcare coordination to provide the best patient care outcomes. To achieve these outcomes, healthcare organizations are turning into predictive analytics. In this blog post, we are going to discuss the key benefits of predictive analytics in healthcare…
What to Know about Predictive Analytics in Healthcare
There are some confusion and erroneous perceptions of predictive analytics in healthcare. The area isn’t all about software tools which are generally tied to predictive analytics located in many different businesses.
There is a report from Rock Health about predictive analytics in healthcare: a business that offers seed funds to startups in digital health, it stated that much of conventional medicine and healthcare work inside predictive analytics. The main difference is that many years back, doctors’ minds were predicting the unknown, based on their experience. Now, software tools are broadening the information collection to encompass more information.
Predictive analytics in healthcare utilizes historic data to make predictions about the future, personalizing medical care to each person. An individual’s previous medical history, demographic information, and behaviors may be utilized along with healthcare professionals’ experience and expertise to forecast the future. Software tools do not set predictive analytics in healthcare; they represent the most recent wave of technologies to advance the area.
By all stats, the industry is forecast to thrive. According to Allied Market Research, the worldwide predictive analytics in the health care marketplace garnered $2.20 billion in 2018, and it is expected to rise to $8.46 billion by 2025, almost quadrupling in size. The projected compound annual growth rate during that interval is 21.2 percent.
Listed below are the top 7 benefits of using predictive analytics in healthcare:
1. You Can Pick the Ideal hospital and Clinic
Launching a brand new healthcare facility is a costly investment. Predictive analytics can help you evaluate sites by calling prospective visits, measuring the effect of a new center opening on existing centers, and assessing competition by leveraging competitor insights and information so that you invest in the ideal property and avoid costly mistakes.
2. Manage Staffing Levels and Enhance Business Operations
How many staff members if you intend to have in your new hospitals or healthcare facility? By employing the visits prediction generated with a predictive analytics version, you are able to gauge the volume that the facility will probably manage and will maximize your staffing levels so. For existing facilities, you are able to compare the visits prediction to real performance to identify business opportunities to enhance operations. If the center has high-quality operation but low real visits, maybe you’ve got operational issues that will need to be dealt with.
3. Identify that Families are Most Likely to React to Marketing Messages.
Instead of blanketing a trading place with advertising messages to your healthcare center, you are able to identify which families are most likely to reply to your message using a marketing solution that integrates predictive analytics modeling. Taking a targeted strategy to advertise improves return on response rate and advertising spend.
4. Optimize Existing and New Small Business Markets.
Predictive analytics will be the cornerstone of in-depth marketplace research, which identifies your company’s optimal amount of amenities in a current market, the positioning of these centers, as well as the sequence in which you ought to start the facilities. By attaining the proper balance, you are able to optimize a market’s growth potential and deliver the ideal healthcare services into the locations that need them the most.
5. Help Long Run Tactical Planning Initiatives
Healthcare organizations use many different tools from the
long term tactical planning process and predictive analytics are a very helpful
resource. Arm your staff with a different source of advice to aid with
6. Plays a Vital Role in Imaging
In medical imaging, predictive analytics is already making
waves in accuracy and speed.
Stanford researchers create an algorithm called CheXNeXt,
can display chest X-rays in a matter of seconds. It discovers 14 distinct
pathologies having an accuracy rivaling that of radiologists. CheXNeXt researchers
expect to have the ability to use the algorithm to aid with the identification
of urgent care or emergency patients that come with a cough.
Lungren, assistant professor of radiology at the Stanford University Medical Center stated that this algorithm prioritized categories for physicians to review, such as normal, abnormal, or emergency. We will need to be considering just how far we could push these AI versions to enhance the lives of individuals anywhere in the world.
Predictive modeling will fundamentally help oncologists make better-informed decisions concerning patient care. Rather than conducting tissue-destructive evaluations or relying upon genomics, AI algorithms can exploit information from pictures to identify patients with a more aggressive disorder that therefore needs more aggressive therapy. It might also let doctors know which patients have significantly less aggressive cancer and may have the ability to prevent the unwanted effects of chemotherapy.
And though the study in predictive analytics for individual care is still growing. It will become a substantial tool for radiologists and oncologists within their functions treating cancer.
7. Perfection in Identification and Preventative Care and Diagnosis
Predictive analytics utilizes the CheXNeXt algorithm to help doctors make more precise diagnoses of the patients to help resolve problems before they appear.
This is done by assessing data collections from tens of thousands of individuals to acquire a larger comprehension of patient travel.
This helps provide a sign of any problems they may have for diagnosis intentions and then allows physicians to better knowledge of how well a patient is being treated.
Using predictive analytics in this way means healthcare
providers or hospitals may intervene sooner and ease patient treatment faster,
more accurately, and having an increased chance of a much greater result.
Still Wonder Why Predictive Analytics Matter in Healthcare!
Predictive analytics in healthcare is going to be one of the revolutionary things to happen to healthcare providers this century.
Now, take a close look at some of the revealing industry stats for predictive analytics in healthcare:
Society of Actuaries stated, 93% of healthcare companies
agree that predictive analytics is crucial to the future of their businesses.
In 2017, the market size of big data analytics in healthcare in North America was estimated at 9.36 billion USD and projected to increase 34.16 billion USD by 2025. The growth rate is almost 17.7%.
82 percent of Respondents at a CWC survey suggested that the
top advantage of analytics execution was enhanced patient care.
It is apparent that there will be significant use of predictive analytics in healthcare in the future just they are using in other industries and it’s thriving. For example, the manufacturing industry is one of the best sectors that constantly benefited from using predictive analytics.
The Upcoming Future of Predictive Analytics in Healthcare
Till now, it seems the advantages of utilizing predictive analytics in healthcare is more significant than other concern. Healthcare organizations agree with the companies investing more money in Artificial intelligence, predictive analytics technologies, and machine learning.
Over one-third of healthcare organization’s executives said
they had been investing in Artificial intelligence, predictive analytics technologies,
and machine learning since 2018.
As the technologies are mature and information sets that may be used by providers to keep growing, predictive analytics will become an extremely significant aspect to take into consideration when it comes to handling patients.
But you may question that this will be in the future, but now what should companies do to look positive? They have the number of data sets required to satisfy their patients. In 2018, Infosys discovered that half of the respondents in an active survey believed their information wasn’t ready.
However, predictive analytics in healthcare is fast-growing among all the industries that use predictive analytics. This is something of an inevitability for larger organizations, even for smaller service providers.
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.
What is IBM Cognos Analytics?
IBM Cognos Analytics is a self-service analytic tool that combines cognitive computing technology, involving artificial intelligence (AI) and machine learning, initially developed as Watson Analytics. For instance, the platform makes use of cognitive tools to get help in automating data preparation. The system discovers the users’ data and can produce recommendations for data connections and visualizations. It’s proposed as an all-in-one platform, presents analytics features, ranging from building dashboards and data integration to exploration, reporting, and data modeling.
Principles of Advanced Cognos Analytics
This business intelligence tool helps in managing and analyzing data easily. Its self-service features help users to prepare, explore, and share data. It includes predictive, descriptive, and exploratory methods, also recognized as numeric intelligence. Cognos Analytics uses a lot of statistical tests to evaluate your data.
It is significant to appreciate the descriptions of these tests as they implement Cognos Analytics. Numeric algorithms are utilized as a part of the workflow to present features to the user that provides information about the numeric assets and relationships in their data.
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.
Unlocking Business Intelligence with Cognos Analytics
The days of providing strategic Business Intelligence (BI) solutions have passed. Now, the market is seeking more tactical projects and insight-driven Analytics, and numerous tools in the market place can no longer provide those features.
Based on a recent survey by MIT Sloan, 85% of CIOs consider artificial intelligence (AI) as a strategic prospect. AI-driven Analytics present actionable insights through their self-service capabilities and enable organizations to attain transformation.
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
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
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 models take out patterns from past and transactional data to recognize risks and opportunities. Self-learning software will automatically evaluate the existing data and provide tools for future problems. It will enable you to build new sales strategies to adjust according to the changes and increase profit growth.
How Predictive Analytics Helps Business Boost Your Sales?
Let’s take an overview of how specifically predictive analytics solutions can help you to boost your sales:
1. Identify Market Trends
Based on data from previous events, predictive analytics will find out the points of highest and least demand that the company might get throughout the year. It allows eCommerce businesses to respond before their competition by planning a good customer acquisition campaign and having sufficient stock in hand to fulfill demand. They can also build an active pricing strategy to optimize sales.
Similarly, considering it for the prices, dynamic pricing depends on predictive analytics to adjust prices to the requirements of the market. Moreover, there are many tools available in the market, which analyze more than numerous different KPIs automatically to set up the best prices for your products and services while always considering historical data and the outcomes of decisions made in the past.
2. Create Personalized Offers
Predictive analytics enables you to predict which offers will be most efficient based on the definite features of each client. With superior segmentation, you can guess the future behavior and attitudes of each user group based on their activities and behavior in the past and offer them only specific products and services in which they are interested. The key to making this possible can be found in the data analytics about what each client purchased, how much they spent, their location, the channel through which they reached to you, and other key performance indicators.
3. Optimize Sales Resources
With predictive analytics, you can also forecast the behavior of your clients across the whole sales channel. You can easily detect whether there is any risk of them discarding their professional relationship with the eCommerce business or if they are open to making new purchases in the future. Briefly, you can spot the most profitable customers and those customers who should be given more attention from your side.
In spite of its countless benefits, CEOs and marketing
managers should never forget that, as it is based on historical data,
predictive analytics can’t always track the changes in the behavior of
customers or competitors. Therefore, you always need to have the correct past
and current data in your systems to predict the results correctly.
Use of Predictive Analytics in Different Industries
Other than eCommerce businesses, any industry can empower predictive analytics to optimize their processes, increase revenue, and lessen risks. Below are the industries that use predictive analytics:
Banking and Financial Services
In the finance industry, there is a huge amount of data and money at risk, and leveraging predictive analytics for a long time to detect and reduce fraud, calculate credit risk, capitalize on cross-sell/up-sell opportunities, and retain profitable customers.
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
Oil, Gas, and Utilities
Whether it is forecasting equipment failures and future supply requirements, evaluating safety and reliability risks, or improving overall performance, the complete energy industry is embracing predictive analytics with confidence.
Governments and Public Sector
Governments have always been a source of encouragement in the
progression of computer technologies. Today, governments are making use of
predictive analytics like many other industries to make their service and
performance better, detect and avoid fraud, and better comprehend consumer
behavior. They are also utilizing predictive analytics to improve
It is really important for manufacturers to classify factors
leading to decreased quality and production failures, and also to optimize
parts, service resources, and allocation. So the manufacturers coordinated with
the sales team can forecast the demand of products and manage their
manufacturing units accordingly.
In the Nutshell
Predictive Analytics provides numerous benefits and helps enterprises make more accurate predictions for business outcomes. Though every business is different, so they need different tools for disparate areas of analytics. In addition to diversity, many companies also presently come across other complexities when it comes to implementing machine learning and predictive analytics across their businesses.
Developing a successful data-driven business strategy requires participation from all levels within the organization, comprising management and staff throughout the departments. All level contributions will help businesses internally assess their existing business conditions, recognizing the major weaknesses and opportunities for growth to find out if predictive analytics can help to resolve those business challenges and impel growth.
Once business recognizes their exact needs for advanced analytics regarding sales and marketing activities, they can begin by evaluating options to apply Predictive Analytics in their businesses.
As a whole, the integration of big data as a distinguishing factor in decision-making becomes a competitive advantage for those businesses that want to boost their sales. Actually, predictive analytics can provide an edge to every organization, no matter what the size your firm has or on which business model it works.
Are you looking for a tool to set your business ahead in the game by growing more sales? Then, make your business shine by implementing advanced predictive analytics solutions today!
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.
What Do You Mean By Data Integration?
Data integration is a combination of data flowing in from various sources to single, unified storage space. Integration starts with the ingestion process and involves steps like data cleansing, ETL mapping, and transformation. Data integration eventually enables analytics tools to fabricate efficient, actionable business intelligence.
The main idea behind is making your data more meaningful and actionable and easy to understand for the users who are accessing it. Technology is progressing extensively with time. The data management techniques that were used previously have been trounced by the new emerging technologies, such as cloud storage and other big data technologies.
There is not any universal approach to data integration. However, data integration tools typically engage some common elements, comprising a network of data sources, a master server, and clients who access data from the master server.
In the typical process of data integration, the client makes a request to the master server for data. Then, the master server drives the required data from internal and external sources. After that, the data is extracted from various sources and then combined into a single, unified data set. It is provided back to the business users for further usage.
How Does Data Integration Work?
It’s vital to understand that data integration is a thorough process, not an individual technique. And a variety of data integration tools are available to serve both the assortment of data being collected and the requirements of individual businesses.
Here let’s overview of a basic data integration process:
is ingested from two or more databases with heterogeneous organizational
structures. Even though two or more databases may store rationally structured
data, they would not usually be able to correspond with each other.
varied data is stored in a data warehouse. It is processed through a definite
schema or set of rules, and classifications developed to resolve various
methods in which the information is referenced between databases.
governing schema enables users to propose queries based upon a generally
understood system, meaning that numerous data sources can be discovered in
These steps describe data integration in its simplest form. The demands related to data integration technologies are increasing. Unstructured data like information enclosed in-text comments often needs the intervening of the plan to understand semantic links between various units. It adds a level of intricacy to the process and signifies the revolutionary of data management technology.
Why is Data Integrity Important for Business?
Business intelligence applications utilize a complete set of information provided through data integration to obtain important business insights from the past and current data of the company. Data integration can have a direct great impact by providing executives and managers a deep understanding of current processes, as well as the prospects and risks it faces in the market.
Also, the data integration process is sometimes crucial for work together with external organizations like suppliers, business partners, or governmental oversight agencies.
One significant application of data integration in the modern IT environment is in providing access to data stocked up on legacy systems, such as mainframes. For instance, modern big data analytics environments like Hadoop generally are not natively well-suited with mainframe data. A good data integration tool will fill that gap, making valuable legacy data of the organization accessible for the modern business intelligence tools.
How Is Data Integration Accomplished?
An assortment of methods, both manual and automated, has previously been used for data integration. Most data integration tools today utilize some form of the ETL (Extract, Transform, and Load) method.
As the name refers, ETL works by extracting data from its host ecosystem, converting it into some consistent format, and then loading it into a target system for use by applications performing on that system. This step of transformation generally includes a cleansing process that tries to correct errors and insufficiencies in the data before it is loaded into the target system.
Role of Enterprise Data Integration Strategy
An enterprise data integration strategy is a group of policies, processes, plan rules, and governance measures that you decide to ensure data integration is executed consistently, controlled centrally, and is fully sustained by your IT systems.
These strategies are a range of activities that move data
from one system to another, supervise the flow of data, implement security
rules, and facilitate business processes. When you evaluate how many disparate
data sources your company has, you’ll find why you need a holistic enterprise
data integration strategy to make sure these important IT components are well
Features of Data Integration
Do you know why people are so much interested in Data Integration? The answer to this question might be the several powerful features that it has. The following are a few main features of data integration.
Create Assurance On
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
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.
Benefits of Data Integration Services
There are numerous factors that impact the productivity of your business online. One of the main causes that might be stopping your progress is the lack of Data Integration. When you talk about the benefits and significance of data integration, then it will not be wrong if you say that you cannot run any system or business without data.
Consider this whole scenario as a real-life example. If things keep on messing and spreading all around, it will be harder to access those things and ultimately making it complicated for you to manage those things. Similarly, data is available in massive volume in any online business, and you must provide your users with the facility to reach that data easily without putting much effort. Here, let’s discuss some of the benefits that you leverage with data integration.
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.
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
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.
Leverage Data Integration for Strategic Benefits
Data is the fuel that adds to innovation and digital transformation endeavors of any enterprise. By leveraging the capability to produce access, collect, analyze, and interpret data from numerous combined data sources, like HR and ERP systems, digital enterprises can comprehend significant competitive and operational benefits.
The unified data can provide insights into business
processes, customers, human capital, sales, and finances. These insights can
lead to improvements in business processes or recognition of problem areas and
facilitate strategic objectives.
In an IDG survey of top-class IT and business decision-makers
in organizations with more than 500 employees, 91% of the respondents agreed
the capacity to integrate data from any source is critical to creating
strategic goals for their organization.
Addressing Data Challenges in Current Crisis!
The current crisis of COVID-19 is bringing the subject of business stability to the forefront of the business leader’s minds. Businesses are trying to survive, adjust, and stay responsive, changing the business processes, positions, systems, and operations to deliver the correct business results. Today, cost management is at the top of the mind of business owners, as companies revolve around the new normal scenario where business conditions change every day. Having an accurate data integration strategy can help you steer through these times of pandemic and come out healthier and more thriving on the other side.
Leveraging an industry-leading data integration tool enables you to connect anything, anytime, and anywhere. By joining hands with professional partners, you can get help in bringing your enterprise data integration strategy to life. It will provide a centralized set of tools for deploying and managing the data integrations across your organization. Do you want to know more about adopting this approach? Visit the leading data integration services provider and consult experts for more details!
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.
Examples of Business Intelligence
The Explain Data feature in Tableau helps rapidly identify competent explanations of outliers and trends in data. Business intelligence is much more than a single process – it is an umbrella that covers all the processes and methods, from collecting, storing, and analyzing data from business functions or activities to manage performance. All of these operations work collectively to produce a complete view of a business to help people in making superior, actionable decisions.
In the last few years, business intelligence has developed to comprise more processes and activities to make performance better. These processes comprise:
Data mining: Making use of databases, statistics, and machine
learning to discover trends in large datasets
Reporting: Sharing reports of data analysis to stakeholders so
they can depict conclusions and build decisions
Descriptive analytics: Utilizing preliminary data analysis to determine what
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
Data visualization: Transforming data analysis into visual illustrations
like charts, graphs, and histograms to more effortlessly understand data
Visual analysis: Discovering data through visual storytelling to
converse insights on the board and continue to be in the flow of analysis
Data preparation: Collecting multiple data sources, recognizing the
dimensions and measurements, making it ready for data analysis
Importance of Business Intelligence?
Business intelligence can help businesses make advanced decisions by presenting current and historical data within their business state of affairs. Analysts can empower BI to deliver performance and competitor benchmarks to make the organization operate more smoothly and efficiently. Analysts can also easily identify more market trends to boost sales or revenue. If the data is used effectively, it can help with everything from compliance to employing staff.
A few means that business intelligence can help organizations to make smarter, data-based decisions:
Find out ways to boost profit
Evaluate customer behavior
Evaluate data with competitors
Mark market trends
Find out issues or problems
How Does Business Intelligence Work?
Businesses and organizations have queries and goals. To find out the answers to these questions and manage performance against these goals, they collect the essential data, analyze it, and conclude which actions should be taken to attain their goals.
Technically, raw data is collected from all activities of the business. Then the data is processed and stored in data warehouses. Once it’s saved, then the users can access the data, starting the analysis process for responding to business questions.
How BI, Data Analytics, and Business Analytics Work Collectively?
Business intelligence involves data analytics and business analytics but makes their use only as a different part of the overall process. BI helps users conclude from data analysis. Data scientists mine into the particulars of data by using advanced statistics and predictive analytics to identify patterns and predict future patterns. Data analytics helps you to get answers for why did this happen and what can occur next? Business intelligence makes use of such models and algorithms and shatters the results down into actionable words.
According to the IT Glossary of Gartner, business analytics involves data mining, statistics, predictive analytics, and applied analytics. Briefly, organizations carry out business analytics as part of their superior business intelligence strategy. BI is intended to answer exact queries and deliver quick analysis for decisions or planning. However, organizations can use analytical operations to improve follow-up questions and iteration.
Business analytics cannot be a linear process because finding
answers to one question will probably lead to follow-up questions and
iteration. Otherwise, you can consider the process as a series of data access,
discovery, exploration, and information sharing. It is known as the cycle of
analytics, a modern phrase explaining how businesses leverage analytics to
respond to varying questions and expectations.
Difference Between Traditional BI and Modern BI
Previously, business intelligence solutions were designed based on a traditional business intelligence model. It was a top-down approach where business intelligence was determined by the IT organization, and the majority of analytics questions were answered via static reports. It meant that if somebody had a follow-up question about the report they got, their request would depart to the base of the reporting queue, and they needed to start the process once more.
This process led to slow, annoying reporting cycles, and
users weren’t able to empower current data to make decisions. Traditional
business intelligence is still a general method used for ordinary reporting and
answering fixed queries.
On the other hand, modern business intelligence is interactive and accessible. While IT departments are still a vital part of managing access to data, various levels of users can modify dashboards and generate reports on short notice. With suitable software, users are enabled to visualize data and get answers to their own questions.
How Some Major Industries Use Business Intelligence?
A lot of different industries have implemented Business Intelligence more than before, including healthcare, information technology, and education. All companies can use data to change processes.
Financial firms use business intelligence to take a complete view of all current to realize performance metrics and spot areas of opportunity. Access a centralized business intelligence tool that allows you to take all of their branch data together into one view.
Business Intelligence lets branch managers recognize clients
that may vary the number of investment needs. And management can track if a
performance within the region is above or below average and check out the
branches that are responsible for that region’s performance. It leads to more
prospects for optimization, together with better customer service for clients.
How to Choose a Business Intelligence Tool?
A lot of self-service business intelligence tools and platforms align the analytics process. It makes it easier for users to view and understand their data without the technical knowledge of mining into the data themselves. There are numerous BI platforms existing for ad hoc reporting, data visualization, and building customized dashboards for several levels of users.
Here are some recommendations for analyzing modern BI platforms so you can select the right one for your company. One of the more general approaches to provide business intelligence is through data visualizations.
Benefits of Visual Analytics and Data Visualization
As you’re aware, data visualization is the most common way to deliver business intelligence. Humans are visual beings and vary in tune with different patterns or dissimilarities in colors. Data visualizations illustrate data in a way that is more handy and comprehensible.
Visualizations accumulated into dashboards can rapidly tell a story and emphasize on trends or patterns that may not be exposed easily when manually evaluating the raw data. This accessibility also allows additional conversations around the data, leading to wider business impact.
Leveraging Benefits of Business Intelligence with Tableau
The processes included in business intelligence help you manage your data so it can be simply accessed and analyzed. Decision-makers can then mine deeply and find the required information rapidly, allowing them to make well-versed decisions. But better decision making is just one advantage of business intelligence. Let’s take an overview of the most practical benefits of BI and how organizations are utilizing this technology to attain their goals.
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.
Having correct data and quicker reporting functionality helps in better business decision-making. Organizations can use customized mobile dashboards for their sales department so they can see real-time data and predict sales before attending any meeting with potential clients. They can confidently talk about the need of clients as well as of prospects and understand that the data is up-to-date. So the business leaders don’t have to wait longer for reports and tackle the risk of data that may be expired.
Better Customer Experience
Business intelligence can directly influence customer experience and satisfaction. With Tableau, companies can deploy BI systems across various departments, building more than thousands of dashboards for employees. These dashboards withdraw data from different processes and text data from customer support interactions. Using such data, companies can discover opportunities to progress customer service and decrease support calls by 43 percent.
Now, IT teams and analysts spend less time answering to business user queries. Departments that didn’t have access to their own data without consulting analysts or IT can now directly perform data analytics with minute training. BI is intended to be scalable, delivering data solutions to the people who need it and for employees who require data.
Trusted and Governed
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.
Adopt Tableau’s Self-Service Business Intelligence (SSBI)!
Today, many organizations are shifting towards a modern business intelligence model, distinguished as a self-service approach to data. IT supervises the data (security, accuracy, and access), enabling users to act together with their data straight away. Modern analytics platforms like Tableau help organizations tackle every step in the cycle of analytics- data preparation in Tableau Prep, analysis and discovery in Tableau Desktop, and data sharing and governance in Tableau Server or Tableau Online. It means that Tableau Consulting can help you govern data access while empowering more people visually to discover their data and share the insights.
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.
It is common that people often get confused between two types of data storage, but they are much more different than their similarity. In reality, the only likeness between them is their sophisticated intention of data storage. The dissimilarity is important because they provide different functionalities and need diverse sets of skills to be correctly optimized. While a data lake serves good for one company, a data warehouse can be suitable for another.
What is a Data Warehouse?
A data warehouse is a combination of different technologies and components that allows the strategic exploitation of data. It is a practice for gathering and managing data from wide-ranging sources to deliver meaningful business insights. The electronic storage system saves a large volume of data generated by a business, which is intended for query and analysis instead of processing transactions. Data warehouse performs the process of converting data into information.
Modern enterprise data warehouse (EDW) is a database, or assortment of databases, that unifies a business data from numerous sources and applications, and keeps it ready to access for analytics and operation within the organization. Companies can include EDW in an on-premise server or in the cloud.
The data stored in such a digital warehouse is one of the most valuable assets of a business. It showcases much of what is extraordinary about the business, its people, its customers, its stakeholders, and more.
Advantages of Data
Superior ability to analyze relational data that is flowing through online
transaction processing (OLTP) systems and business applications (e.g., ERP,
CRM, and HRM systems)
High-quality integration with consistent data sources, particularly for relational
sources, making it robust for small to medium-sized businesses
Disadvantages of Data
Data silos, in
which information security maintenance directs to restricted access such that
important data isn’t reached by the people who may have profited from it,
obstructing efficiency and collaboration
Higher prospects of distortion of BI analysis outcomes due to impulsive or wrong data
cleansing — since data quality is frequently one-sided, with diverse analysts
having various tolerances for what comprises quality
What is Data Lake?
A Data Lake is a repository that can store massive volumes, including structured, semi-structured, and unstructured data. It lets you store every sort of data in its native format with no fixed borders on account size or file. Data Lake supports large data quantity to boost analytic performance and native integration.
Data Lake is similar to a big container, just like real lakes and rivers. As lakes have numerous tributaries flowing in, a data lake contains structured data, unstructured data, logs, machine to machine data streaming in real-time.
Advantages of Data Lake:
Simple integration with the Internet
of Things (IoT), as
data like IoT device logs and telemetry, can be gathered and analyzed
First-class integration with machine
learning (ML), with
the schema-less structure and ability to amass large volume of data
The flexibility provided by the
that assists in evaluating data coming from social networks and mobile devices.
Also, it carries large, varied, multiregional, and micro-services ecosystems
Flexibility in Data Lake
The flexibility offered by data lakes can lead to mistreatment, making shortcomings that create
more problems than they solve. For example, Data graveyards are data lakes
storing data that is collected in large amounts but never used, and Data Swamps
are data lakes with low-quality data.
Key Difference in Data Lake and Data Warehouse
Based on some key factors, let’s see how the two data storage terms differ from each other:
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
A data warehouse will comprise data that is pulled out from
transactional systems or data that includes quantitative metrics with their
traits. Then the data is cleaned and transformed or further process.
2. Data Capturing
A data lake captures every type of data in their original
format from real source systems, whether it is structured, semi-structured, or
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.
The data warehouse is suitable for operational users since it
is well structured, easy to use, and understand for general employees.
5. Storage Costs
Storing data in big data technologies is comparatively
low-priced than storing data in a data warehouse.
In a data warehouse, storing data is expensive and
Data lakes can include every data and data types, as it
allows users to access data before the process of transformation, cleaning, and
Data warehouses can deliver insights into pre-definite
questions for predefined data types.
7. Processing Time
Data lakes leverage users to use data before it has been
converted, cleansed, and structured. Thus, it enables users to obtain their
results more rapidly comparing to the traditional data warehouse system.
Data warehouses provide insights into pre-definite queries
for pre-defined data forms. So, any changes in the data warehouse required more
8. Position of Schema
Generally, the schema is determined after storing the data in
data lakes. It offers more agility and easiness of data capture but needs
efforts at the end of the process.
In a warehouse, the schema is determined before storing the
data. It needs efforts at the beginning of the process but presents good
performance, integration, and security.
9. Data Processing
Data Lakes works based on the process of ELT (Extract, Load,
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.
11. Key Benefits
They incorporate various types of data to generate entirely
new questions as these users may not possibly use data warehouses because they
want functionalities beyond their potential.
1. Healthcare: Data Lakes Store Unstructured Information
Data warehouses have been used for a lot of years in the healthcare industry, but it has by no means been immensely successful. Due to the unstructured behavior of most of the data in the health industry, such as physician notes, clinical data, etc. and the requirement for real-time insights, data warehouses are typically not a perfect model.
Data lakes enable you for a mixture of structured and unstructured data that can be a better match for healthcare companies.
2. Education: Data Lakes Present Flexible Solutions
In modern years, the worth of big data in education modification has become extremely apparent. Data about student scores, attendance, and more cannot only help to weaken students revert on track but can truly, help to forecast possible issues before they happen. Flexible big data tools have also assisted educational institutions in modernizing billing, progress fundraising, and many more.
Most of this data is huge and exceedingly raw- so most of the time institutions in the education field, leverage the best benefits from the flexibility of data lakes.
3. Finance: Data Warehouses Pleads to the Masses
In the finance industry and other economic business setups, sometimes
a data warehouse serves as the most excellent storage model because they get
the ability to access structured data by the whole company not only a data
Big data has assisted the financial and economics industry take large steps, and data warehouses have been a top performer to help companies take that step. The only cause that can influence financial services company away from such a model is because it is more economical, but not as successful for other functions.
4. Transportation: Data Lakes Help To Make Predictions
A great amount of the profit of data lake insight is its capability to make predictions.
In the transportation business, particularly in supply chain management, the prediction ability that approaches from flexible data in a data lake can have enormous benefits, specifically cost-cutting reimbursements identified by analyzing data from forms within the transport channel.
Which Solution Is Right for Your
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 one alternative for your business often depends on your data sources. For example:
If you utilize an SQL database or
ERP, CRM, and HRM systems, data warehouses will fulfill your enterprise
If your data flows in from different
data sources, such as NoSQL, IoT logs and telemetry, mobile, social data, and
web analytics, data lakes are possibly a good option.
When you run a business, profit, or loss of your business depends on the decisions you made. So when it comes to making the right choice can be essential to ensure that the tool you choose delivers the optimal value to your business. However, the data you confine can only be valuable if you can transform it into actionable insights. Today, top-most software companies like Informatica, Tableau, IBM, etc. offer data analytics tools that let you make decisions easily concerning your upcoming plans and present actions.
Are you still confused about whether to choose an enterprise data warehouse or data lake solutions? Get expert guidance now!
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 Business can be beneficial.
Let’s start with Big Data…
What is big data?
We all use smartphones, but have you ever
wondered how much data it generates in the form of texts, phone calls, emails,
photos, videos, searches, and music? Approximately, 40 exabytes of data gets
generated every month by a single smartphone user.
Now imagine, this number is multiplied by 5 Billion smartphone users. That’s a lot for our mind to process, isn’t it? In fact, this amount of data is quite a lot for traditional computing systems to handle, and this massive amount of data what we term as “Big Data”.
Let’s have a look at data generated per minute on the internet…
2.1 Million Snaps are shared on Snapchat.
3.8 Million Search queries are made on Google.
1 Million People log in to Facebook.
4.5 Million Videos are watched on YouTube.
188 Million Emails are sent.
That’s a lot, right!
Uses of Big Data in the Healthcare Industry
There are significant uses of big data in the healthcare industry. Hospitals and clinics across the world generate a massive volume of data annually. Approximately, 2314 exabytes of data are collected annually in the form of patient records and test results. All of this data is generated at a very high speed, which attributes to the velocity of big data.
How can small businesses employ knowledge management?
Management applications in massive organizations. Afterward,
we will examine how small companies can revise and embrace those practices for
greater knowledge management in their companies.
Simple thought. When running a company, knowledge, and data are resources. Knowledge loss–for example reduction of any advantage –includes a price. Knowledge management is just the practice of taking actions to prevent knowledge loss.
A knowledge management Procedure is normally composed of three components:
1. Gather and preserve significant business knowledge and
2. Make gathered information accessible and simple to
3. Update gathered information regularly for continuing
Knowledge management is significant since knowledge And information are resources.
Imagine you run a business that makes all its gains From earnings on your site. Your site goes down, and also the individual responsible for handling the website is on holiday. Nobody else understands where the website is hosted, plus they do not have key query passwords or answers. Just how much money will the business lose if the site is down for one hour, a day, or weekly?
Knowledge management reduces profit loss within this
Scenario since the information required to repair the website is saved where
others may find it.
Knowledge management can also be significant for productivity
When a new employee begins and nobody understands the Wi-Fi
password, then that worker can not do some work, and yet another worker wastes
time by searching for the password or looking for a media cable.
If client support agents have to fix problems from scratch
each time they answer a telephone, these calls will require a great deal more
time than when they could discover the answers fast in a database.
In the event the workplace supervisor wins the lottery and
never returns to perform –but all her documents are saved locally on her
computer–somebody from IT might want to spend days or hours trying to obtain
access and recover important details.
Room for keeping significant contracts, or as complicated as
artificial intelligence technologies that gathers, stores, and retrieves info
such as an intern or personal helper. There are lots of different potential
Let us Look at how knowledge is handled at three
Knowledge management in Toyota
Dr. Philip Fung states that there are two Kinds of
understanding to He uses an instance of a chef to exemplify the gap.
Though a chef may Have the Ability to write the recipe down
for her Most renowned dish, she’d likely fight to convey how she developed the
recipe. There is a difference between what we understand (explicit knowledge)
and we do what we know how to perform (tacit knowledge); or as Fung
states,”We could do much more than we could tell.”
Toyota’s approach to understanding management caters to the
two Explicit and tacit understanding.
Completed by its workers in a work Education (JI) document.
The JI contains three pillars of advice:
1. Important Steps — Includes step-by-step directions for
finishing The endeavor.
2. specific step.
To talk about tacit knowledge, Toyota workers spend a few
months functioning New workers can follow the directions on the JI record, but
they’re also able to pull out of the tacit knowledge they obtained while
observing seasoned workers performing the jobs.
And when launching a new mill, Toyota not just sends the New mill workers to a present mill for training, in addition, it sends seasoned workers from a present mill to the factory to operate alongside new employees for a couple of months. This makes consistency in processes and knowledge across all Toyota factories across the world.
The way Microsoft utilizes knowledge management
Management plan for almost two years .
Microsoft built its initial knowledge-collaboration stage in
It was basically an intranet designed to gather information regarding customer
appointments and create the information available to everybody in the
corporation. From 2010, the system hosted 37,000 websites.
Finally, the Business realized it had a more contemporary
System for distributing and collecting information. This enabled team members
to share, access, and make knowledge resources from anywhere, with any device.
Nowadays, Microsoft’s teams utilize an Assortment of the
Organization’s Programs to locate and share information:
Employees save files into the cloud–maybe not anyplace on
private computers. This simplifies file sharing and prevents information loss
brought on by turnover, personal injury, and even theft.
Together with cloud-hosting, workers can nevertheless
produce knowledge databases just like they did back in 2006, but they are also
able to make websites for outside jobs and make people available to clients and
partners beyond the Microsoft network.
All of intranet websites incorporate with Microsoft’s
Rather than relying on workers to capture and upgrade data,
AI will catch and upgrade knowledge automatically by monitoring workers’
As a little or midsize Company, You Might Not Be able to Develop your personal suite of cloud-based cooperation software such as Microsoft, or an AI-powered call center like Amazon, and you might not have multiple ports for hands-on coaching of workers like Toyota. But that does not mean you can not embrace their knowledge management methods:
Adopt the If new applications are excessively complicated,
nobody will use them.
Find alternatives that have built-in integrations together with the applications and software employees are already utilizing.
Understand a fundamental source of advice is greatest. If
knowledge is dispersed across multiple applications, it will continue to be
difficult for folks to find.
Require Ideal solutions can automate the procedures of
upgrading knowledge or mechanically categorize and label fresh content to make
it a lot easier to locate.
Search for resources using machine learning how to improve
as information is accumulated. Machine-learning technologies learn how people
look for particular forms of information, becoming better over time in helping
users locate the specific information they’re searching for.
Document Important procedures:
Utilize Toyota’s JI record as a template, or produce your
standard process record. Put aside time once a month to allow workers to make
instruction about the jobs they are accountable for.
Save documentation about the cloud or any other shared
server so everybody has access to it and also to stop file reduction.
Locate Creative ways for workers to share tacit
set a mentor program that matches new hires with long-time
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
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
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.
What is Predictive Analytics?
Predictive analytics exploit the power of historical data with AI and machine learning technology to identify, monitor, manage, and optimize business processes. It also spots and identifies trends, forecast potential concerns, and provides suggestions to improve the process and make performance better. Industrial IoT platforms that empower predictive analytics gather and analyze real-time data to foresee and avoid forthcoming problems at the initial point.
When people talk about manufacturing, the first step to leverage predictive analytics is collecting, storing, and organizing the processed data produced by a variety of machines, devices, and systems within the factory. Generally, factories need almost three to six months of data to use predictive analytics efficiently. Although, this time interval can change depending on the volume of data generated and the targeted issues.
The analytic applications like predictive performance and predictive quality generate data rapidly because production runs on regularly. Sometimes equipment failures can take place, so it can take even months to produce the quantity of data required for specific applications.
Once accumulated together, the historical data can be used to withdraw insights and make efficient predictions based-on a broad range of variables like line speed, product quality, and more. It includes identifying key relationships between various variables, forecasting variables of interest, and leveraging decision-makers to take early action to lessen waste and boost efficiency.
Today, factories have become ever-more connected, so the predictive analytics technology will turn out as a key part of their digital transformation journey because it can help you become more efficient and competitive and gain more profit.
Why Should Manufacturers Use
It’s obvious that there will be rapid growth in the adoption of predictive technologies in the future. In the manufacturing industry, modern and advanced factories are leveraging predictive analytics to reduce the time to action considerably which saves time, money, material, and speeds up the time of marketing.
Manufacturers get alerts in advances, such as possible quality failures or unexpected downtime due to machine failure, and enable operators to take corrective action. For example, machine learning can foretell a quality failure that can occur in ten minutes because of dropping line speed and its past consequences, where products do not match quality standards.
Factories are also using these technologies to identify production trends, resolve issues faster, and handle resources more competently. The capability to recognize potential issues early on with predictive analytics facilitates factories to manage their processes and avoid the costs included material waste, high scrap rates, or downtime.
In the situation of an upcoming skilled labor deficiency, machine learning and predictive analytics technology also have the added benefit of helping manufacturers to attract digital-native staff to engage in their workplace. At a time when many factories find it difficult to employ and preserve talent, the opportunity to work with this cut-throat solution provides a value-added benefit.
How Predictive Analytics Works?
When deploying a predictive analytics solution, firstly collect data from machines and sensors and integrate this data with live operational data, data from MES and ERP systems, and offline quality data. After that, cleaning, merging, formatting, and structuring in the cloud takes place. For example, if one machine notes down the temperature in Fahrenheit and another machine take the temperature in Celsius, then the temperature needs to be converted into a combined metric.
Based on historical data, machine learning algorithms can find out the behavioral patterns that have earlier lead to problems. If the real-time event starts to pursue one of those problem patterns, then the system can predict the potential result and alert factory managers. Once the operator, engineers, or managers gets alerted, they can rapidly take remedial action and avoid issues from having an important impact.
Here are the four most important steps that are key components of AI predictive analytics...
Step 1: Access and Explore Existing Data
Step 2: Pre-Process Data With Precision
Step 3: Create and Validate Predictive
Models in the Cloud
Step 4: Set up Models and Implement Insights
from Predictive Analytics
Predictive analytics is vital for applications that allow manufacturers to classify problems at their very starting stages, so they resolve them before issues begin to unfold.
As the return on investment is a key driver of the industry, predictive analytics is competent to deliver insights faster and many factories even estimate measurable cost savings and opportunities for optimization after a few months only.
Detect Patterns to Calculate Performance
Predictive analytics go through a large volume of historical data much faster and more accurately than a human. Machine learning technologies are able to spot repeated patterns and further relationship variables. So when you modify these settings, you boost production by 10% without giving up the first-pass yield.
AI and machine learning can reach to patterns and discover a variety of combinations that help your organization recognize potential efficiency enhancements, forecast issues, and decrease waste.
Improve Operations in Real-Time
Predictive analytics provides agile real-time insights by evaluating data from past production runs with live production movement. These assessments that convert to predictive and prescriptive analytics both constrain suggestions and alerts to make operations better in real-time. A cloud-native hybrid system joins the power of the cloud with the business stability, allowing factory managers to improve their decision-making process faster.
Trim Down Costs
Quality failures can result in major losses in the product that increases the additional cost of labor and time. Predictive analytics will help factories to find out quality failures and take remedial action quickly to reduce impact and trim down the cost related to waste. Prescriptive analytics can also increase these cost savings by allowing you to repeat your most competent processes more consistently. In addition to this, predictive analytics and situation-based monitoring can help factories decrease unexpected downtime and lost productivity by informing manufactures about probable equipment issues.
Optimize to Precision
Almost all manufacturers are familiar with some Lean Principles that they are following for decades. Sticking to these best practices helps manufacturers attain the utmost production efficiency with the least waste. Predictive Analytics ultimately presents manufacturers with real-world data to help them optimize their operations to reach the precision.
Who In The Manufacturing Industry Can Implement Predictive Analytics?
Predictive analytics can be implemented in the manufacturing industry of approximately every size and any other industry. Some applications might be more appropriate to definite industries than others however since predictive analytics rely on the existing data and models can be used to forecast everything.
Let’s take a look at a few important roles within the factory:
Plant managers – They can benefit from predictive
analytics to optimize production and augment contribution boundaries.
Engineers – Predictive Analytics can help
engineers to solve problems faster. They can evaluate data faster than ever and
utilize analytics-driven procedures and quality recommendations to revise
guidelines and processes as well as clear up and root cause problems.
Operators – They obtain alerts about potential
failures, so they can take curative action quicker and avoid any downtime
related to quality or device failures.
To be more efficient, all you require is the right approach
to collect data, like sensors, a place to gather that data and data-skilled
staff to recognize what those insights mean.
How to Start Using Predictive Analytics?
Meaningful ROI depends on creating the right foundation. To make a predictive analytics solution to be successful in the manufacturing unit, you’ll need the following foundational elements:
A True Source of Data
The data existing within your organization is often complex and disorganized. The different data formats withdrew from ERPs, MES platforms, QMS software, and other basic sources only make it more difficult. If you want to drive real value from your inclusive data, predictive analytics can help you build a single source of certainty. Whether it is operations, quality assurance, or supply chain management, it provides the manufacturing industry a holistic approach to take a dive into your complete data.
Correct and Reliable
The correctness and reliability of data impact the capability of any organization to make valuable forecasts. In the manufacturing field, the variety of different data types from an assortment of sources makes data quality management the main concern and that there are apparent relationships throughout your master data. If not, you’ll be incapable of classifying differences or duplicates in your data that can overturn your predictions about all from future demands to employee needs. We can help you to expand dependable quality across your data system to make sure your insights are correct.
A Definite Data
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.
Why Is Business Intelligence Analytics So Important?
The main functionality of business intelligence and analytics is to help business teams, managers, top executives, and other employees make better-informed decisions based on accurate data. It will eventually help them identify new business opportunities, trim down costs, and recognize ineffective processes that need to be engineered again.
BI analytics uses software and algorithms to derive valuable insights from a company’s data and direct their strategic decisions. BI users evaluate and represent data in the form of business intelligence dashboards and reports, visualizing compound information in a simpler, more amicable, and logical way. Finally, business intelligence and analytics are much more than the technology used to collect and analyze data.
Top Benefits of BI Analytics
The benefits of business intelligence and analytics are abundant and diverse, but they have one thing in common, they give you the power of knowledge. Whoever they influence, they can convert your organization and the way you handle your business profoundly. Here is an overview of the top six benefits of business intelligence:
your customers more efficiently
performance and revenue
can rate leads
present tailored service experience
How Does Business Intelligence Work?
Business intelligence presents a scale of wide range of analytical applications, comprising collaborative BI, mobile BI, open-source BI, SaaS BI, real-time BI, and operational BI. The technology is not only about collecting intelligence but about forming a sense of data in a way that can be rapidly seized.
It is possible through visualization applications for making infographics and design charts. BI also provides dashboards and also present performance scorecards. In essence, you can understand the key performance indicators and business metrics in a much easier way when the data is displayed in the form of visualizations.
How BI Analytics Services Supports Your Business?
Many small businesses are reluctant to implement BI into their practices. It is not just because it is costly and time-consuming to install but, because they are not sure about the profits they can gain by using it. Here are a few reasons why it can reimburse its value:
It’s much easier to make well-versed data-driven decisions.
It’s a structured way of increasing revenue.
It augments the competitive benefit over other leaders in the industry, including bigger organizations.
It enhances the competence of its business operations.
It improves the quality of their customer service.
These benefits are the key factors that decide the success and prosperity of any business. Making efforts to analyze data without Business Intelligence and Analytics is clumsy. For example, information is often fed into Microsoft Excel spreadsheets, which is time-consuming in aspects of data collection and it’s tedious to put the information together in a way that’s easy to grab, analyze and share.
If you fail to analyze data, it can result in the difference between profit and loss or between a humble profit and offensive success. These are the two major things that can occur when analytics are done properly:
You can discover insights into industry trends and can identify marketing opportunities that you could have otherwise missed.
You get to know what customers want and demand from your company and this information can assist you in redesigning your business to obtain more customers.
Data Literacy in Today’s Digital Age
It’s hard to find a business that isn’t driven by correct
data. In fact, data is growing at lightning speed. Regrettably, smart business
executives don’t always have sufficiently skilled workers to make sense of the
constantly rising data- nor they have the right tools they need to collect this
data competently and extract it for insights.
Efficient data-driven operations that run across an organization can present a differentiating factor. It’s not easy to understand risk rapidly because the existing data is often incorrect. As its effect, a company can fail to choose smarter options and improve its bottom line. It’s hypothetically possible that a company can have high data literacy without Business Intelligence that is much harder.
Leading to ROI with Business Intelligence Analytics
Business intelligence is a key to manage business trends, spot significant events, and view the full image of what’s happening within your organization due to data. It is vital to optimizing carious operations, boost operational efficiency, gain new revenue, and make the decision-making process better for the company.
You’re living in the most competitive business market in history. Progression in technologies and a worldwide economy have mutually created a force of competition in the market, with weaker companies being buried in the crowd.
Considering the current situation, an organization can’t thrive without using BI tools. Particularly after examining some case studies, which have shown the unbelievable ROI that is only possible by using them and the endless benefits of business analytics. This ROI gained from business intelligence can come in various forms.
You have to understand what’s going on in the minds of your customers who can be your next best customers and how to collaborate with them in the most efficient ways. You can get answers for all these questions from the available data, which can be processed by implementing BI and analytics tools. However, you need to be aware of any indiscretion and don’t forget to consider some business intelligence best practices and some detrimental practices to stay away from!
How Can You Successfully Implement Business Intelligence?
A highly modified, customer-driven approach has ended up in a modern business approach, which needs a business analysis with definite metrics. Hence, a business intelligence strategy is essential for all organizations today.
If you implement Business intelligence properly, it can provide you a correct analysis that can help you to speed up and develop your business. It can help you to evaluate the customer acquisition cost, customer purchasing patterns, cycles, and help you to make informed decisions based on that analysis.
An appropriate business intelligence execution will not only help you know your customers better but, it will also help you to increase your sales multiple times.
So, what are the steps you should pursue a successful business implementation strategy? Here are a few key steps for deploying business intelligence within your organization.
Training the Staff & Stakeholders
It is human nature to oppose change and the first step to
reducing the resistance is through training. You can teach and educate the
staff & stakeholders, which requires immense efforts as it would need an
exceptional amount of expenses from the stakeholders’ viewpoint and shift to
new technology from the staff’s point of view.
Identify the Objectives
The second thing to do for a successful BI Analytics is to clearly identify the objectives you want to achieve through a business intelligence system. Having set up objectives will not only help your partners to recognize the expectations from the tool, but it will also assist you in strategizing the plan of action simply.
Set Up Key Performance Indicators
When you have defined the goals to set up a business intelligence system, the next step is to describe the key performance indicators (KPIs) clearly. They will help you to create helpful decisions to attain your objectives. These indicators should be assessable in line with your objectives and the key to accomplishing your goals.
Create a Team
Next, you have to create a team of people who will carry out tasks such as data cleansing, data input, data processing, and data analytics. It is one of the most crucial steps for a successful implementation of BI analytics, as this team will be the one to execute the ideas.
Discover the Best Software
The next step in the process of implementation is to discover the most suitable software that can perform all tasks within your organization. You also have to find out various options for software available for every task. The variety of tools will change depending on the requirement and budget. But, you need to understand the optimal tool required for all processes.
Develop an Execution Strategy
Once you have gathered your team, resources, and software, you need to concentrate on the implementation strategy for the successful implementation process of BI and analytics. It involves understanding whether you require a Top-Down Approach that is more of a strategic method or a Bottom-Up Approach, which is more of a tactical method.
Identify the Tasks & Allot the Resources
After creating a team, selecting software, and the suitable strategy of execution, you need to describe the tasks which the teams will perform. And then, you need to hand over the tasks to the related teams and assign the resources to complete the task.
Build the Data Cleansing, Data Processing, and Data Analysis Processes
Now when you have all the tools, strategies, and the team in your place, you have to generate a data cleansing process with the selected tool. There is a vast amount of data that is deficient in the quality to obtain your goals and you need to clean-up this useless database and produce a high-quality database.
You also have to make sure that there are checkpoints to estimate the data quality at set intervals. Having an efficient data cleansing process improves your chances of attaining your goals. Then, you have to integrate the BI analytics tools, such as Power BI, Cognos, or Tableau to be aware of the user behavior insights.
Execute the Process as a Proof of Concept
After completing all this, you have
to execute them for a single process as a proof of concept. Once you have
enough data to recognize the impact of BI on your business, then this approach
will help you evaluate whether you are meeting the KPIs or where areas need to
Implement the Changes to Meet the KPIs
When you have executed the changes based on the insights derived from the Proof of Concept, you can run another PoC to recognize how much difference you have covered between the outcomes of these two PoC. It must be a regular process, and it requires optimization at every stage. It is recommended to try some proof of concepts and analyze their results.
If you want to stay away from all the hassles you experience in implementing BI by yourself and analytics tools – you can hire professional BI Analytics Services that will do it all for you! ExistBI has offices in the United States, United Kingdom, and Europe.
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.
What is Data Management?
Data management concerns the complete journey of your data; right from collecting, storing, classifying, protecting, verifying, and processing necessary data and making it accessible to your employees in the organization.
Today, data is seen as a business asset that you can use to make more-informed business decisions, make marketing campaigns better, improve business operations and decrease costs, all with the motive of growing revenue and profits. But a lack of correct data management can burden organizations with ill-assorted data silos, conflicting data sets, and data quality issues that restrict their capability to run business intelligence (BI) and analytics applications or, even worse, it lead to faulty insights.
Importance of Business Data
A well-implemented data management strategy can support companies to gain potential competitive benefits over their business contenders, both by making operational effectiveness better and facilitating improved decision-making. Organizations having data that is well-managed can also turn out to be more agile, identifying market trends easily and moving forward to gain new business opportunities more rapidly.
A valuable data management system can also help companies to escape from data breaches, data privacy concerns, and regulatory compliance issues that could harm their reputation, add unpredicted costs, and put them in legal threats. Eventually, the major advantage of a concrete approach to data management can deliver is enhanced business performance.
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.
Lessen Security Risk
Today, a lot of personal information is available for people to access. When you save anyone’s credit card information, personal address, phone numbers, photos, etc., it is of the utmost importance that this data is confined by the most favorable security. If your data is not managed correctly, it can be accessed by the wrong people. Stolen data will also have strict allegations on the growth of your company; no one wants to give their details to people who cannot keep it protected.
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.
When all your data is organized and all departments know how to access it, then the quality of your decision-making progress should improve considerably. People have diverse techniques of processing information however, a centralized system makes sure there is a framework to plan, arrange, and allot data. In addition to this, an excellent system will ensure fine feedback, which sequentially will lead to essential updates to the process that will only profit your company in the long term.
To Wrap Up
The future of managing businesses lies in an organization’s capability to use data irrespective of its source, type, or size. When data is managed in the right way, you gain accurate insights through business intelligence and data visualizations. You can choose to get assistance from professional data management companies.
There are many advantages to hiring external assistance with your data management. Firstly, a proficient firm specialized in data management will be more of an expert than your inside staff. They can ensure proficient data security implementation within your organization. Moreover, it is likely to decrease the cost of having an internal staff member do it, as a data expert will require less time and resources to complete the task due to their experience.
If you are looking for specialized Data Management Services, ExistBI has consultants in the United States, United Kingdom, and Europe, contact us today for more information.
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.
The Silver Bullet
In the starting period of data warehousing, ETL tools were simply for code generation. Their elevated cost and small functionalities restricted their use. IT firstly custom coded all data integration applications. The best data integration coders had special knowledge of database growth, amendment, and optimization. Databases were never close to the self-tuning and optimization that people take lightly nowadays.
Now, ETL and database optimization are highly developed. Most of the people utilizing data integration today do not encompass the same consideration of data integration and databases and with today’s complex tools, they are not required to. So, when the business requires more information, IT searches for a silver bullet; buy more multifaceted data integration software and infrastructure.
Traditional Methods Are Not Good Any More!
There are two essential principles for designing data architecture and making the most of data throughput:
1. Process the least amount of data that is necessary to keep
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.
In bulk loading, the arcane and unglamorous database loading methods and other old approaches to rupture the data integration still stand to help stay away from purchasing new software and infrastructure. The rule is to extract the data out of your source systems and drive it into your data warehouse environment as soon as possible. Normally, this method is a fast and low-priced method to considerably improve data warehouse loading.
Bulk loading is only applied to your major concerns that are usually fact tables, which are generally about 10% of the tables or files that you are loading. It is interesting to note that even the high-end data integration tools have made space to bulk loaders, supporting the fact that it is indeed a feasible and priceless tool. There are further approaches, methods, or techniques that can also be implemented from the older days, where the laws of database and data integration still apply.
Leveraging New Age Data Warehousing
Data warehousing and business intelligence (BI) have been growing and getting more complicated over the years. As IT engineers, consultants, and analysts get more experience; they share these experiences with colleagues when they join other companies, publish articles, or carry out training. By sharing their understanding, they have helped to advance the overall intelligence of the IT industry. It has directed the formation of conventional knowledge about how to design, build, and implement Data Warehouse and Business Intelligence solutions.
But there are limitations to this conventional wisdom when
people consider it like fact. Sometimes, people blindly pursue the general
advice without making sure that it actually implements to their specific
situation. And there can be occasions where you have to challenge conventional
The IT industry is still in a phase of active and sometimes unstable development. It’s not always smart to put extra trust in conventional understanding, particularly when the industry is developing and growing in ways that could help you provide strong performance management, Business Intelligence, and Data Warehouse solutions.
Exposing Conventional Wisdom
Conventional wisdom claims that a Data Warehouse is independent of applications, which is not correct. It is beneficial in financial applications, especially in forecasting, budgeting, and planning. Business users require the elasticity to carry out a number of iterations on a group of numbers before approving a budget, forecast, or plan. They should also be able to scrutinize historical data to make their projections. However, business applications don’t have the ability to do this. And data warehouses can’t fulfill this need because they aren’t made to support applications. So, business users opt for the use of spreadsheets that dissipate their time and efficiency.
The usage of spreadsheets has increased the range of errors and made it unfeasible to document how the numbers were produced. With the present business and regulatory environment, this is not adequate for many CFOs. An effective method is to combine these financial systems with an application that has strong connections to a Data Warehouse. The Data Warehouse works both as a system of distribution that sends the data to every business process or a user needing it and as a recording system, where the business budget, forecast, or plan is stored. Firstly, data flows from source systems to data warehouses, then data marts, cubes, and can finally be utilized by BI applications.
All architectural diagrams display this one-way flow. The sources for the data warehouse environment have prolonged from back-end office operations to include customer-front applications, external data received from suppliers and partners, and many previous workgroup or desktop applications. The data flows from throughout the organization and often beyond. The Data Warehouse ecosystem is now an information hub that shares data across many applications and data stores. Data Warehouse is now the system of distribution for many business processes, applications, or staff that requires this information.
How Data Warehouse Fulfill Benefits Your Business?
As per a recent report by Allied Market research, the worldwide market for data warehousing is predicted to increase by up to $34.7 billion by 2025. It is almost twice its worth of $18.6 billion in 2017.
So what drives investment in enterprise data warehouse growth? Cloud data warehouse technology increased the value of innovative systems and practices that augment efficiency and lessen costs across company operations. Today, different departments such as marketing, finance, and supply channels, take benefit from a modern data warehouse exactly the way engineering and data science teams of the organizations do.
The Requirement to Access and Act on Data in Real-Time
Modern data warehouses make data viewable and actionable in real-time by supporting an extract-load-transform (ELT) method over the omnipresent extract-transform-load (ETL) model. in this model, data is cleansed, transformed, or augmented on an exterior server previous to loading into the data warehouse. With an ELT method, raw data is withdrawn from its source and loaded, moderately untouched, into the data warehouse, making it much quicker to access and analyze.
The Search for a Holistic Vision of the Customer
The assurance of a data lake strategy is that complete company data, whether structured, semi-structured, or raw data, can be quickly and easily mined from one place. Using this approach, an enterprise data warehouse can facilitate a 360-degree view of the customer, helping to advance campaign performance, reduce churn, and finally, raise revenue. An enterprise data warehouse also makes predictive analytics possible, where teams use conditional modeling and data-driven forecasting to notify business and marketing decisions.
Considering Data Lineage to Ensure Regulatory Compliance
A modern data warehouse follows compliance with the EU’s General Data Protection Regulation (GDPR). Without a prepared data warehouse, a company would probably have to set up a complex process to fulfill each GDPR request. It would include numerous functions or business units looking for pertinent PII data. When you have a data warehouse in place, there is basically just one place you have to look at.
Enabling Non-Technical People to Query Data Rapidly and
Building a data warehouse can also profit non-technical employees in various job roles beyond marketing, finance, and the supply channel. For example, architects and store designers can make the customer experience better inside new stores by drumming into data from IoT devices located in existing locations to recognize which division of the retail footprint is most or least engaging. Global amenities managers can support their decision-making on whether to enlarge plants or move product lines on a strong set of information, comprising of hiring and retaining data of employees, in addition to typical metrics such as cost per square foot.
The Need to Bring Data Together into a Single Place
Most of the data sets today are huge to transport and query rapidly and cost-efficiently. To control costs and latency, companies use local clouds. According to research, 81% of companies with a multi-cloud strategy results in data sharing across platforms from contending cloud providers. Removing these roadblocks is the main concern for organizations that struggle to be really data-driven.
Top-class data warehousing technology will enable organizations to store data across various regions and cloud providers, and view insights from a globally combined data set.
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;
1. 90% of Data Has Been Produced Since 2016
90% of all data is a lot—or is it? Wi-Fi, smartphones and high-speed data networks have become a part of everyday life for the last twenty years. At the starting of the 2000s, streaming was restricted only to audio, while broadband internet was utilized regularly for web surfing, downloading, and emailing. In that condition, device data was at the least amount and the actual data used was generally about interpersonal communication, particularly because videos and TV hadn’t been part of the process, which encouraged high-quality streaming. When the decade came to an end, smartphones had become commonplace and Netflix had transited its business priority to streaming.
It means the internet has experienced huge growth in
smartphone applications, social media, streaming services (audio and video),
streaming video game platforms, downloaded software rather than physical media
between 2010 and 2020, all creating exponential use of data. Is this period of
growth significant to business? Imagine how many businesses have connected apps
that are continuously transferring data to and from devices, for controlling
appliances, deliver instructions and specifications, or gently convey user
metrics in the background.
In 2019, deployment of 5G data networks broadly started, so the bandwidths and speeds only got better. Hence, the quantity of data will only get more as technology lets the world get even more connected. Is your data lake ready for it?
2. 95% of Businesses Hold Unstructured Data
In today’s digital world, businesses assemble data from all types of sources, and most of them are unstructured. Think about the data collected by a company that sells services and schedule appointments through an app. While several data streams come in predefined structured formats and fields like phone numbers, dates, time stamps, transaction prices, etc. still, the company has to archive and store a large amount of unstructured data. Unstructured data can be any type of data that doesn’t enclose an inbuilt structure or predefined model, which makes it hard to search, sort, and evaluate without additional preparation.
For example, unstructured data comes in a variety of formats. When a user makes an appointment, all the text fields filled make that appointment sum up to the unstructured data. Emails and documents are other types of unstructured data within a company. The social media posts of the company and photos or videos that are taken by employees as notes during the services are also counted as unstructured data. Similarly, any instructional videos or podcasts created by the company as marketing assets are also unstructured.
3. 50% of Businesses Trust Big Data to Improve their Sales and Marketing
Many people believe big data is beneficial in aspects of its technical usage. Undoubtedly, a company that works via a smartphone app or presents a form of streaming uses big data and is providing a service that just wasn’t possible twenty years ago. However, big data is much more than offering streaming content. It can generate a lot of important improvements in sales and marketing. Based on a report by McKinsey, 50% of businesses believe that big data is empowering them to modify their approach in these departments.
All You Need Is A Data Lake!
The above indicates one point that your organization needs a data lake. And if you don’t prioritize data management, it’s obvious that your competitors will overtake you in areas such as operations, sales, marketing, communications, etc. Data is basically a part of life today, providing precise data-driven decisions and unparalleled insights into deep causes. When collaborated with machine learning and artificial intelligence, you can also use this data for predictive modeling to forecast future events.
Data Lake Security Best Practices – How Can You Improve the Security of Data?
Data lakes are a competent and safe way to save all of your incoming data. Worldwide big data is predicted to rise from 2.7 zettabytes to 175 zettabytes by 2025, which means there will be exponential growth, all coming from a growing number of data sources. They are not like data warehouses, where structured and processed data is required. Data lakes work as a single repository for raw data across multiple sources.
Along with a list of benefits, a data lake also has some inbuilt risk of a single point of breakdown. Obviously, it’s uncommon for IT departments to identify an exact single point of failure in today’s IT world. Backups, redundancies, and other typical foolproof techniques are liable to protect company data from correct disastrous failure. It provides double security, so when enterprise data stays in the cloud, data delegated in the cloud rather than the local environment has the additional benefit of trusted vendors creating their own protection systems for your data.
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:
Temporal: Where transient data like copies and streaming reels remains before deletion.
Raw: Where raw data stays before processing. Data in this zone can also be further encrypted if it encompasses sensitive data.
Trusted: Where data that has been confirmed as reliable stays for trouble-free access by data analysts, scientists, and other end users.
Refined: Where enhanced and influenced data stays, generally as final outputs from tools.
You can create a hierarchy by using zones like these, when
joined with role-based access, can help lessen the prospect of the wrong people
using potentially sensitive or malevolent data.
Data Lifecycle Management: Which data is continuously in use across your organization? Which data hasn’t been touched for years? Data lifecycle management is the process of recognizing and segmenting stale data. In a data lake ecosystem, older stale data can be shifted to a definite tier designed for competent storage, making sure that it is still available whenever needed but not captivating the required resources. A data lake driven by ML can even utilize automation to recognize and process stale data to make the best use of overall efficiency. While this should not impact directly on security issues, a competent and well-supervised data lake enables it to work like a well-oiled machine rather than failing under the burden of its own data.
Data Encryption:The proposal of encryption is very important to data security
is not anything new, and most data lake platforms bring their own methods for
data encryption. Of course, it is critical to know how your organization
implements. In spite of which platform you utilize or what you choose between
on-premises vs. cloud, a powerful data encryption strategy that works with your
current infrastructure is completely vital to protect all of your data, whether
it is in motion or at rest.
Let’s Create Your Data Lake!
What’s the most suitable method to make a secure data lake? By selecting the best range of products, you can create a data lake in just a few steps. With cutting-edge data lake solutions, you get advanced capabilities to integrate it with best-in-class analytics tools. Are you considering creating a data lake? Contact leading service providers to get answers to your major concerns!
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.
Importance of Data Science
With the help of professionals, you can use their expertise to turn advanced technology into actionable insights and make the right use of Big Data. Today, a great number of organizations are unlocking their doors to big data and utilizing its power, which is growing the worth of a data scientist who understands how to withdraw actionable insights out of gigabytes of data.
It is getting clearer by the day that there is huge value in data processing and analysis and exactly where the need for a data scientist is. Executives understand how data science is a vast field and how data scientists are like modern superheroes, but many are still uninformed of the value a data scientist can provide in an organization. Let’s have a look at its benefits.
With the right guidance under Data Science, the companies can identify their client in a better and more informed way. Clients are the base of any product in an organization and play the most important role in their victory or failure. Data Science allows companies to connect with their customers in a tailored manner, and thus, proves the superior quality and supremacy of the product.
Data Science allows products to convey their story strongly and attractively. When products and organizations use this data collaboratively, they can share their story with their audience, which forms enhanced product connections.
One of the imperative features of Data Science is that the results it shows can be implemented to almost every type of industry, such as travel, healthcare, education, and many more. With the help of Data Science, the industries can evaluate forthcoming challenges easily, and can also confront them efficiently.
At present, data science exists in almost all the fields and there is a diverse range of data available in the world today. If used appropriately, it can direct the product to the way of success or failure. The data, when used properly will hold significance for attaining goals for the product in the future.
Big data is constantly budding and increasing. Using different tools that are developed frequently, big data helps the organization to determine complex concerns related to IT, human resources, and resource management competently and effectively.
Data science is gaining immense value in every business and hence playing an important role in the performance and growth of any product. Therefore, the necessity of data scientists is also increasing as they have to execute the important job of managing data and providing solutions for definite problems.
What is the result of including data science in your business?
Alleviating risk and fraud
Data scientists are qualified to recognize data that stands out in a definite way. They generate statistical, network, and big data methodologies for predictive fraud susceptibility models and use them to produce alerts that help make timely responses when abnormal data is recognized.
Delivering the right products
One of the benefits of data science that organizations can
exploit is they can discover when and where their products sell best. It can
help you deliver the relevant products at the right time and develop new
products to fulfill the customers’ needs.
Customized customer experiences
One of the most popular advantages of data science is its
capability to recognize their audience on a very coarse level, for sales and
marketing teams. With this information, an organization can generate the best
possible experiences for their customers.
Future of Data Science in Modern Businesses
The impact of Data science has impacted areas and industries differently. Its influence can be seen in multiple sectors like the retail industry, healthcare, and education. In the healthcare business, new medicines and techniques are being exposed constantly, and there is a requirement to improve care for patients. By including data science techniques in healthcare, you can find a solution that can assist in taking care of patients.
Education is another sector where you can notice the benefits of data science clearly. The most recent technologies, such as smartphones and laptops have now become an imperative part of the education system. By facilitating data science, better opportunities are formed for the students, which allows them to improve their knowledge.
Business Intelligence To Make Smarter Decisions
Traditional Business Intelligence has more expressive and static behavior. However, by associating data science in BI, it has modified itself to develop into a more dynamic field. Data Science has made Business Intelligence integrate into a wide range of business operations. With the enormous increase in the quantity of data, businesses require data scientists to examine and obtain meaningful insights from the data.
The meaningful insights will help the data science consultants to evaluate information at a big scale and grow essential decision-making strategies. The process of decision making involves the assessment and estimation of various factors included within it. The four-step process decision making involves:
Understanding the context and nature of the issue that you need to solve.
Discovering and measuring the quality of the data.
Executing the right algorithm and tools for concluding a solution to the definite problem.
Using story-telling to interpret your insights for a better understanding of teams.
This is how businesses require data science to facilitate their decision-making process.
Creating Better Products
Companies should draw customers’ attention to their products. They need to create products that meet the needs of customers and present guaranteed satisfaction to them. Therefore, industries need data to make their product in the best way possible. The process includes the analysis of customer reviews to come across the best fit for the products. This analysis is executed with the help of the most advanced analytical tools of Data Science.
In addition, industries make use of the current market trends to plan a product for multiple audiences. These market trends present businesses with hints about the existing need for the product. Businesses develop with innovation. With the expansion in data, industries are able to execute not only newer products but also different innovative strategies.
Managing Businesses Efficiently
Nowadays, businesses are data-rich. They hold an overabundance of data that allows them to obtain insights through a suitable analysis of the data. Data Science platforms uncover the unseen patterns that are existing inside the data and help to make consequential analysis and prediction of events. Data Science helps businesses to manage themselves more effectively. Both large and small scale businesses can benefit from data science to grow further.
Data Scientists help companies to analyze the wellbeing of the businesses. So the companies can forecast the success rate of their decided strategies. Data Scientists are accountable for transforming raw data into meaningful information. It helps in abbreviating the performance of the company and the health of the product. Data Science identifies key metrics that are essential for calculating business performance. According to this, the business can take important measures to calculate and access its performance and take suitable management steps. It can also assist the managers in analyzing and finding the potential candidates for the business.
Predictive Analytics to Forecast Results
Predictive analytics is the most vital element of modern businesses. With the arrival of highly developed predictive tools and technologies, companies have extended their potential to deal with varied forms of data. In technical terms, predictive analytics is the statistical analysis of data that encompasses several machine learning algorithms to forecast future results using the historical data. There are various predictive analytics tools such as SAS, IBM SPSS, SAP HANA, etc.
There are different applications of predictive analytics for businesses like customer segmentation, sales forecasting, risk assessment, and market analysis. Predictive analytics provides businesses with an edge over others as they can forecast future events and take suitable measures regarding these. It has its own definite implementation based on the category of industries. However, despite that, it shares a common function in foreseeing upcoming events.
Utilizing Data for Business Decisions
As explained in the previous section, data science is playing an imperative role in forecasting the future. These predictions are needed for businesses to be aware of their future outcomes. Based on these results, businesses make important decisions that are data-driven. Previously, many businesses would make poor decisions due to the lack of research and surveys or self-confidence on gut feelings only, which would result in some devastating decisions leading to the loss of millions.
However, with the existence of an excess of data and essential data tools, it is now achievable for the data industries to make thoughtful data-driven decisions. Additionally, business decisions can be made with the help of influential tools that can not only do faster data processing but also present accurate results.
Automation of Recruitment Processes
Data Science has performed a key role in driving automation into various industries. It has taken away the common and recurring jobs. Resume screening is one such job. Companies have to deal with a crowd of candidate’s resumes daily. Many major businesses draw the attention of thousands of candidates for a position. To making sense of all of these resumes and choose the right candidate, businesses exploit the power of data science.
The data science technologies such as image recognition can transfer the visual information from the resume into a digital format. Then, it processes the data using a variety of analytical algorithms like clustering and classification to find the right candidate for the job. Moreover, businesses learn the right trends and analyze the best possible applicants for the job, which allows them to reach candidates and have a profound insight into recruitment and job websites.
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.
However, when it is a matter of managing the data to speed up the value and bring ROI with an investment in cloud data warehouses, lakehouses, and data lakes, the usual approach that IT departments tend to choose, can have major implications like increased cost, project overruns and maintenance intricacy removing any benefits of modernizing analytics in the cloud.
Challenges in a Multi-Cloud and Hybrid World for Data Management
As IT companies begin sustaining cloud and analytics or AI projects, the inducement is to accuse their technical developers of designing, developing, and deploying the right solution. However, they hurriedly get into data challenges if they fall to the hand-coding path. In a lot of cases, these complexities exploit on-premises data warehouses and data lakes:
Varied and siloed data:
Many organizations have different types of data available in many dissimilar systems and storage formats, either on-premises or in the cloud. The data is every so often distributed throughout siloed data warehouses, data lakes, cloud applications, or third-party assets. Though, more data is created from online transaction systems and communications like web and machine log files and social media. For instance, in a retail firm, data is dispersed across numerous different systems. These systems include point of sale (POS) systems, including in-store transaction data, customer data in a CRM and MDM system, social and web click-stream data accumulated in a cloud data lake, and more.
Lack of data governance
Varied and siloed data often changes the values of data quality and governance. Policies are hardly ever enforced constantly. Data is discarded into data lakes creating swamps where data is hard to search, understand, manage, and defend. Even inferior is soiled data approaching a cloud data warehouse, where multiple business analysts and other data users rely on it for decision making, predictive analytics, and AI.
A Lot of Emerging and Changing Technologies:
As the amount of data is increasing, new vendors, technologies, and open source projects are coming into effect that changes the IT environment. There are traditional, new, and evolving technologies available for computing, storage, databases, applications, analytics, and even new AI and machine learning. Developers may efforts to stay on top of this varying environment, making it complicated to standardize or execute a methodology.
Why some organizations still using hand-coding?
There are still some organizations that choose hand-coding, supposing that it’s an easier approach than deploying a data integration tool, which may require some level of skills and knowledge. In addition to this, developers may think that integration tools can limit their creativity for a custom use case and practice. In many cases, these are some short-sighted doubts about a smart and automatic data solution. However, hand-coding may be suitable for faster proofs-of-concept (POC) with a low-priced entry.
Disadvantages of Hand Coding in IT
Initially, IT departments may find hand-coded data integrations as a fast, economical way to construct data pipelines. But there are important disadvantages to consider.
Hand Coding Is Costly
In due course, hand-coding is costly to execute, operate, and maintain production. Hand coding needs to be edited and optimized from growth to consumption. And with large IT budgets in operations and maintenance processes, the cost of hand-coding increases with time.
Hand Coding Is Not
With new and emerging technologies, developers have to
re-structure and recode every time when there is a technology change, an
upgrade, or even a modification to the primary processing engine.
Hand Coding Lacks
Hand-coding doesn’t extend for data-driven organizations and can’t maintain speed with enterprise requirements. There are basically too many requirements for data integration pipelines for IT users to contain. The only way to range the delivery of data integration projects is through automation, which needs AI and machine learning.
Hand Coding Lacks
It took many years for data integration hand coders to understand how important and essential data quality and governance are to make sure the business has reliable data. It is even more significant for data-driven companies for the development of AI and machine learning. Hand coding can’t present enterprise width for data integration, metadata management, and data quality.
Disadvantages of Hand-Coding for Businesses
The limitations of hand-coding aren’t limited to IT only. Eventually, hand-coding influences overall business outcomes. Here are the following key areas where hand-coding can have a harmful business impact:
Time to Value
Create that Illuminating Moment
with Cloud Data Management
After struggling for months in the initial modernization project, Informatica realized the need to re-evaluate their cloud data management strategy. By reconsidering the drawbacks of hand-coding, they improved their strategy to decrease manual work and make efficiency better through automation and scaling. Businesses require a cloud data management solution that comprises:
The facility for both business and IT users to recognize the data ecosystem, through an ordinary enterprise metadata establishment that presents end-to-end lineage and visibility throughout all environments
The capacity to reuse business logic and data conversion, which increases developer productivity and allows business stability as it encourages integrity and uniformity of reuse
The capability to conceptualize the data transformation logic from the primary data processing engine, which will make it long-lasting under the quickly changing cloud environment
The capability to connect to an assortment of sources, targets, and endpoints without any requirement for specialized code connectivity
The ability to process data competently with an extremely performant, scalable, and dispersed server-less data processing engine or the capacity to control cloud data warehouse pushdown optimization
The ability to work and continue data pipelines with the least interruptions and cost
Components of Smart, Automatic Cloud Lakehouse Data Management
As the organizations are joining and modernizing their on-premises data lakes and warehouses in the cloud or build up new ones in the cloud, it has become more important than ever to escape from the drawbacks of hand-coding. Especially, today, with the development of lakehouses 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:
Codeless integration with templates, suggested by AI for next-best transformations
Group ingestion of files, databases, changed data, and streaming
Pushdown optimization of databases, cloud data warehouses, and PaaS lakehouses
Serverless and expandable scaling
Spark-based functions in the cloud
Large and native connectivity
AI and machine learning growth to handle schema drift and complicated file parsing
Support for data and machine learning processes (DataOps and MLOps)
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:
Data profiling integrated with data governance
Data quality policies and automated rule creation
Data dictionaries to manage lists of values
Cleansing, parsing, verification, standardization, and de-duplication processes
Integration with your data integration tool
Data analytics for quality
Spark-based functioning in the cloud
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:
Value tagging and data curation
Perception of technical, business, functional and traditional metadata
Connectivity through on-premises and cloud for various databases, apps, ETL, BI tools, and other systems
Built on a Base of AI and Machine Learning
This component is foundational and performs under the other three. The components of data integration, data quality, and metadata management need to be developed on the basis of AI and machine learning to manage the exponential growth in organizational data. Always pick up the cloud-native solution that is multi-cloud, API-driven, and microservices-based and also look for the following features in it:
AI/ML-driven automation, like next-best transformation suggestions, data pipeline resemblance, operational notifications and auto-tuning
Minimum install and setup
Integrated full-stack high accessibility and superior security
Take a Comprehensive Approach to Smart, Automatic, and Modern Cloud Data Management
Many organizations require data to understand, process, and grow their business effectively, but data complexity is an obstruction. IT companies are searching for an intelligent, automatic data management solution that fills the space between on-premises and cloud deployments without requiring rebuilding everything from the start before they can garner the benefits of successful execution.
Without a united and wide-ranging data platform, organizations are required to exploit different point solutions together that were never intended to work together initially. It takes immense time to integrate these systems, which is also very expensive, risky, and inflexible to be amended later. If there is any change in one point of the solution, then you have to repeat and retest all integrations in the system.
You don’t need a big bang implementation to take an enterprise method. One of the major benefits of having intelligent and automated data management is that companies can compress the use of general methodologies, processes, and technologies increasingly, starting with one or two projects initially.
By choosing an enterprise data management platform for high productivity, IT teams can speed up start-up projects to bring instant business value. As the IT companies implement supplementary projects, it can exploit and reuse available assets, considerably decreasing the cost and time to bring new capabilities to the business and making consistency and control better.
With the leading metadata-driven cloud data management solutions in the industry, you get the power to leverage the complete features of your cloud data warehouse and data lake across a multi-cloud, hybrid ecosystem. You can boost the efficiency, ensure more savings, and can start small initially, and scale with top-in-class data integration tools for the cloud, on an AI-driven, intelligent data management platform.
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.
What Are The Major Changes?
Here are some of the major changes that are expected to cover in BI 4.3:
Elimination of the
old Launchpad /InfoView interface that will be replaced by a new tile-based
New Web Intelligence
New data-modeling roles
for Web Intelligence report builders
Dashboards functionality deleted
Some of these changes will impact customers
more than others, and those who have exceedingly invested in deploring tools
like Dashboards and Explorer will require thinking deeply about their next
The New Look Front End
Many of the end-users access BusinessObjects through its web-portal known as the Launchpad/ InfoView as it was called in XIR2 and 3. This portal lets the people log in, explore their reports and documents, interact with them, schedule them, probably produce new Web Intelligence reports or edit existing ones. This process still won’t change, but the web-pages will appear very different and several work-flows will change.
SAP named its web-design environment as ‘Fiori’, and it has slowly turned around Fiori-style front-ends throughout its product range. A Fiori-style Launchpad is already obtainable as an option in BI 4.2, from SP4 onwards, so you can view if you have that version or superior installed. Modify your normal URL from http://:/BOE/BI to http://:/BOE/BILaunchpad instead, and you can see it.
Fiori BI Launchpad in BI 4.2 SP6
This new-interface means that users of other SAP products like SAC or their CRM/ERP products will feel more native in BOBJ. But for those with BOBJ only, it is a big change. In Sap BI 4.3, as the single user interface will be available, it will bring new functions along with the requirement to learn its shortcomings and work-flows.
Removal of Explorer and
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
Re-design of the Web
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.
New Data-Modeling Concept/Role for Web Intelligence Report Builders
Along with new features and behaviors of Web Intelligence, there is one more new concept being added in BI 4.3 that is WebI as a data-modeling tool.
Presently the end-users who build the attractive, informative
elements of a report also need to understand how to make the technical data
aspects of a report, including the possible complex merging of multiple queries
and the formation of complex variables and calculations.
In 4.3, these two tasks can be more easily separated. Technical authorized-users can build datasets in Web Intelligence from multiple universes, multiple queries, including spreadsheets or CSV-based information, write freehand SQL, merge the data, write variables and calculations and then publish all of that as one precise package. These can then be utilized by other users as the data source for their reporting.
SAP Analytics Cloud is SAP’s main representation in the analytics environment. It is where the greater part of their development and investment resides, and it is their goal to help people to use it and gain the benefits from its powerful capabilities.
In BI 4.3, the interoperability between SAC and BOBJ is advanced a few steps ahead. Businesses having licenses for both tools will be enabled it to integrate users more easily, and there will be links to your SAC occupancy from the BOBJ Launchpad.SAC will now consume data through the new Webi data models, which could unlock great opportunities for easier dashboard design.
The front-ends of both systems will be more homogenized with Fiori designed structure, so users will be feeling more content while switching between them: and the redesign of Webi is in part planned to imitate work-flows from SAC story design.
How to Prepare for These Changes?
This change is predictable and inevitable in the long-run, so
how can you be better to get ready for it? Before you push yourself and update
your production system to BI 4.3, there are numerous things you could do to
mitigate the impact and train your users on the new opportunities it will
Ensure that your live system is updated to BI 4.2. Service pack 7 is the newest version, but SP8 will be available soon. Upgrading now gives you the best ever lead-time available and makes sure you have the most recent bug fixes and patches, as well as providing you access to the latest version of the optional Fiori Launchpad – to practically use it before it becomes the only option left.
If you have bought your BOBJ licenses after 1st July 2009, you can create development BOBJ systems for no extra license cost. Create a new environment to test the BI 4.3 water, install new software, and then migrate content to it for testing. Once it trains your users or reveals issues, you can conclude when to upgrade your live environment.
Want to practice new functionalities of BI 4.3? Join SAP Business Objects Training today! ExistBI offers on-site or online training with live instructors in the United States, United Kingdom, and Europe.
Make your Integration Platform Ready to Embrace the Latest Technology Trends
Some stimulating technology trends are rising that are predicted to strike the conventional systems over the next few years that will have a major impact on your data management systems. Is your system future ready with data integration? Will your integration platform be all set to sustain these new trends? If not, this is the time to seek advice from Data Integration Consultants so you will be ready to support a new age of business functionalities that your company will need to thrive.
Choosing the Right Data Integration Platform
If you think your traditional data integration platform is not sufficient to embrace the latest technology trends, changing over to a new platform can be a smart choice. It can be costly, but will surely bring good ROI if implemented correctly.
Selecting a data integration platform can be difficult, especially when your needs are complex. There’s a wide range of service providers to choose from, and not all will be suitable for your needs. You have to find answers to a few essential questions to help you through the decision-making process of choosing the right data integration solution for your business.
What is the use of data integration
software in your organization?
Where does your data stay?
What are your prospected data needs?
Who’s going to work with your data
What is your budget?
Have you had a trial and demo with
different data integration software retailers?
How to identify suitable data
How will the implementation and
ongoing process take place?
Finding the answers for every question on your own can be difficult for you without any technical guidance, so you can hire data integration consultants for helping you out in each stage of choosing and implementing the right software. But if you want to hold using your legacy system, there are some circumstances that can interrupt the adoption of new technology trends in your organization.
Emerging Technology Trends Hovering to Interrupt the Conventional Systems
The IT industry is varying rapidly, and the needs of every business are also changing drastically with their growth. Hence, there are four key emerging technology trends that data management and IT experts need to monitor closely.
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.
API Led Integration
Similar to applications based on the events, API led
integration is a new model for carrying out IT capabilities collectively.
Applications are imagined as pseudo-black boxes, and the thing that is managed
in a structured way is the interfaces that lie between them.
From a data management point of view, this scenario lifts the
need to manage data that is in motion and flowing between apps over APIs, and
also for data at rest within every application. Your integration platform will
need to comprehend the differences between these two types of data and should
be able to ingest, convert, and load them together in your data warehouse for
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.
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.
What Is Data Integration?
According to IBM, data integration is a combination of technical and business processes used to connect data from different sources to extract meaningful and valuable information. In general, data integration creates a single, combined view of organizational data that is used by the business intelligence application to create actionable insights based on the completeness of the data assets, without concern about the original source or format. The huge amount of information generated by the data integration process is sometimes collected into a data warehouse.
A Combination of Theory and Practice
If it seems that this is something only for the enterprises that have huge data flows, you might be amazed to learn just how fascinating data integration is across different industries and sectors. In 2016, Capgemini surveyed that 65% of business executives said they fear to get inappropriate or uncompetitive if they fail to make use of big data. After the years of the survey, this percentage is continuously rising as executives across the world have realized the harmful impact of not including a data strategy and solution in place, which will affect every aspect of their business operations.
Today, staying competitive, work more capably, reducing costs and growing revenues means finding ways to collect, evaluate and optimize data to the fullest extent of its value. Data should not be treated as someday goals down the road, but as today’s driving initiative.
Data integration works throughout your organization to carry out numerous types of queries, from the coarsest questions to the overarching concepts. You can apply data integration to many detailed use cases that impact all teams and departments of your business, including:
Business intelligence – Business intelligence (BI) comprises everything from reporting to predictive analytics to operations, management, and finance. In addition, it depends on data existing in the whole organization to discover inefficiencies, gaps in processes, missed profitable prospects, and much more. Data integration provides you with the right BI tools and technologies that your company might need to make further strategic decisions.
Customer data analytics – Understanding who your customers are, what behaviors they show, and how they are expected to remain loyal or look somewhere else is vital to good business. Data integration allows you to extract information together from all your individual customer profiles into a unified view. From there, you can discover what the complete trends are and complement your existing customer retention strategies with real-time world insight.
Data enrichment – Fight against data decay by constantly updating contact lists like names, phone numbers, and emails. Merge this information with definite sets of exclusive information about every customer to create a much richer and more precise image of your buying audience.
Data quality – It is a challenge to manage the quality of data, it is important to ensure that your data requirements are reliable, that you understand how the data is generated and the tolerance for errors your organization is willing to accept. However, making the data integration process automatic eliminates many risks that are not conforming to your company’s data governance policies, growing both the accuracy and the value of the data available to teams across the organization.
Real-time data delivery – Businesses cannot wait days to provide actual numbers or insights; they have a few hours or sometimes minutes only. That’s why real-time data delivery is important for many businesses to adapt to customers, markets, vendors, and even general and compliance changes faster. Data integration allows you to check data from any point in the collection process anytime to find minute-by-minute insights into processes, workloads, and communications.
Integration Consultants Plan Successful Projects?
Integrating various systems involves integrating different existing subsystems and then producing distinctive and new value for the customers or end-users. To make your efforts for integration planning successful, you must include a wide scope to make sure that the plan meets all specific business needs. A business analyst should start and direct every integration effort of systems to boost the success rate and reduce recurring tasks.
The process of integrating all data existing in different internal and external sources has become more complex in the last few years – typically because of a continuously growing massive volume of data handled by companies. And this process does not get any easier as new potential data sources continue to appear. The success of a data integration project does not only depend on the available systems, but also the third-party products you choose. Here are the most vital criteria to make your data integration successful...
Ensure that Data is of Good Quality
With the evaluation of Big Data, data quality has become a major concern in data-driven organizations. Any data integration task can be negotiated by bad quality data. Keeping it straight, if you keep the trash at one end, you will get nothing but trash at the other end also. Data integration projects without a company-broad strategy on data quality before, during, and after the data integration implementation process will certainly fail.
Good data quality is the only thing that will guarantee user-adoption and accordingly, the success of your data integration project. If you provide your users with poor quality data, they will begin to doubt the data existing in the system and will start using the old, idle processes. A successful data integration project should always have a dedicated data quality range.
the Impact of System Customization
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.
Opt For a Consolidated Approach
When you adopt a data integration approach as a multitude of end-to-end custom integration scripts, without a general direction, then your data integration plan is considered to fail in delivering the required critical unified view of business data. Data must be coordinated in an automated and dependable manner across multiple platforms for a company to get a single version of the truth. Errors created by inconsistent data and manual data entry can prove to be very expensive for the organizations and interrupt business activities.
Future Upgrades into Considerations
Many ERP or CRM providers have developed an onetime integration between the systems for their consumers. Some organizations have already implemented this process for themselves. Although this might appear like a great idea initially as they have a good understanding of the complete processes and data models in the company, it can prove to be an error in the long term. Why? Because these integration solutions are not actually developed as a complete long-run project with future considerations.
So, what will the result be of upgrading the integrated systems? What will happen if you want to expand the use of your integration tools and integrate with other systems? When you select your data integration solution, always ensure that it is long-lasting, and you can keep using it when the integration collection changes. Personalized interfaces typically require development, which reduces the flexibility of the upgrades and makes maintenance more expensive.
Top Management Support
Data management can be a challenging concern, some departments might consider that they own the data in their system and therefore be hesitant to allow other systems to access what they think to be their important information. Here is where wide executive support will help you. Although IT plays the most important part of your data integration project, it would be a big mistake if you do not involve more of your managers and executives.
Executive-level drives bring cooperation between data owners, user adoption, and are actually very important. Why? Because the data integration project you are implementing will not only affect your IT team but also have a broader impact on your overall organization. Don’t forget that a data integration project is all about sharing data and automating various processes. The best CRM-ERP integration projects cannot only be successful if they involve a CIO or IT director, but it also needs to include CEO-level support and participation of top management from the Sales and Marketing teams.
How is data
A diverse number of methods, manual and automated both, have been used for data integration earlier. A lot of data integration tools today utilize some form of the ETL (extract, transform and load) method. As the name suggests, ETL works by taking out the data from its host environment, converting it into some consistent format and then loading it into a target system to be used by applications operating on that system. The step of transformation generally includes a cleansing process that is executed to correct errors and insufficiencies in the data prior to its loading into the target system.
Various types of data integration tools are available out there, comprising master data management, data governance, data cleansing, data catalogs, data modeling and other tools that have a number of data integration features. Here are some of the generally used data solutions that businesses need to understand:
ETL Tools- As explained above,
these tools extract data from one application or system, transform it into a
fresh format, and then load it into the new application.
refers to Application Programming Interface, which provides a programmatic
approach to one application for sharing data with another.
Integration Platforms- It includes a broad range of diverse
features, like ETL, ELT, data governance, data quality, data security, etc.
These tools can incorporate data from an extensive variety of different sources
and are appropriate for use by business users.
Platform as a Service (iPaaS) – It offers cloud-based tools
for the data integration process. They generally provide very effective ease of
use features and the ability to integrate data from cloud-based sources, such
as software as a service (SaaS).
Migration Services – They tend to migrate data from one
place to another and may provide some limited features for data transformations
as well. Most of the major cloud service providers present migration services
for shifting data to the cloud.
Want more? There is so much more you need to know as a business user. As you have read, handling a data integration project is not as easy as it seems, so you will always require the guidance of data specialists who are experienced in handling such projects easily. ExistBI have experienced Data Integration Consultants based in the United States, United Kingdom and Europe. Contact us today to support your data integration project.
Data integration presents a wide range of important information that allows your business to deploy new modern services, but data integration services won’t be successful without overcoming several challenges. Your data can hinder your business intelligence, analytics, and modernization techniques if you don’t work with the right attitude, tools, or strategy. So what will be the result? An inactive organization that lags behind its competitors and fails to satisfy the client’s demands. Let’s discuss the top 6 challenges in data integration services:
Well, firstly, you need to understand what data integration
challenges are and what steps you should follow to avoid them.
What Challenges in Data Integration Services Are There?
A data integration challenge can be anything preventing you from attaining control over the processes and outcome of your data integration. It’s one of the major obstacles in your way of receiving a single, unified vision of your data.
Data integration includes capturing data from different
sources and combining it to generate a single, unified view of complete data.
This merged data makes it easier to depict insights from your existing data is
processed to deliver faster, more impactful business growth.
What are the Top Data Integration Challenges You Shouldn’t Ignore?
When you have got a complete overview of what data integration challenges can be ahead, it’s time to explore some more specific common examples. Below are the six challenges your business can encounter while implementing data integration services alongside, possible solutions.
Incorrect Data Availability
You want to keep your data at one centralized place but, you
are putting efforts into its execution. This data integration challenge is
generally an outcome of relying on human resources alone. It takes developers
time to collect data from various sources and combine them manually. Here, your
organization has to spend time on evaluating data insights and execute valuable
business best practices.
So, it will be better if you trim down the middleman and consider the assistance of a smart data integration tool and accelerate your innovation objectives. In this way most of the heavy work is managed for you. Opting for an automatic data integration platform is a great approach to solve your data integration concerns.
Latency in Data Collection
A few processes need immediate and real-time data collection.
For example, if you’re a retailer owning an e-commerce business site, it would
be preferable to display customized, targeted ads to every customer based on
their search history.
But, if your data isn’t gathered at the required time, you won’t be able to meet these demands. Regrettably, depending on your team to assemble data manually in real-time, is clearly impossible. Probably, you don’t have the right resources or manual power to embark on such a hectic task. If you want to drive real-time data ingestion, your only effective approach is the acquisition of a proficient data integration tool.
Incorrect Data Format
Irregular data sets that are jumbled or do not exist in the correct format will not be actionable and therefore will lose their value. While manual formatting, validating, and correcting data is common, it requires a lot of your developer’s precious time. Data transformation tools remove these concerns by analyzing the original basic language, finding out the correct formatting language, and automatically building the change. This process eliminates the stress out of data integration and restricts the number of errors, particularly when your data team can identify and examine code at any end in the transformation pipeline.
Poor Data Quality
Poor quality of data in your organization can lead to a loss in revenue, missing important insights, and damage to the reputation. That’s why data quality management is a necessary component for empowering modernization, keeping compliance, and driving more precise business decisions. And it is not as tough as you might be thinking.
You can restrict and reduce the amount of bad data flowing into your systems by dynamically validating your data as early as it’s ingested. Above this, you can also examine your data pipelines for outliers and identify errors automatically before they create bigger issues.
It’s estimated that more than 92 % of businesses have
duplicate data in their systems. At first, the existence of duplicates in
systems may seem harmless, but they can create severe long time concerns. So,
the greater number of duplicates you have, the bigger the risk to your
Usually, these duplicates are the outcome of a ‘silo mentality’ issue. The duplication and unnecessary variations become normal in the data integration pipelines if the employees don’t share data and correspond with each other effectively. To restrict the creation of duplicates and eliminate data silos:
a data-sharing culture in your organization and spend time in training
data after validation and make sure that everyone understands it
in technology that helps in team collaboration
regulatory reports that encourage transparency and keep an eye on data lineage
Lack of Understanding
The communication between technical and business teams regarding data sharing plays an important role in data integration. But setting up a general vocabulary of data definitions and permissions is equally essential.
You can create a common understanding of data among the users
Data governance- This process focuses on the procedures, rules, and regulations that are covering your data strategy.
Data stewardship- A data steward is the person who supervises and coordinates your strategies, executes policies, and aligns the IT department with the business strategists.
Without a managed executing plan and clear ownership of your
data, you will continuously struggle during the integration processes.
Defeating the Data Integration Challenges!
Today, the quantity of data generated by businesses each day is growing rapidly and has a critical impact on organizational success. However, until you overcome these six top data integration challenges, you won’t be able to make the most out of your applications, activities, and processes. With professional Data Integration Services, you can get things right by empowering an automatic data integration platform to use data as a keystone for accelerating the business transformation and ensuring the growth and development of your organization.
ExistBI offers Data Integration Service throughout the United States, United Kingdom, and Europe.
The IT industry has become enormous and companies have transformed the way they handle and manage their data. Now, data integration is not done by using specific tools optimized to tackle particular data. But the organizations are deploying a lot of tools from different vendors to access and recover business intelligence spread across a number of databases and applications. As data integration plays an important role in business operations, it is vital to understand the reasons to hire Data Integration Consultants to manage their data needs.
Merging of data flowing from different data sources is done to form it into meaningful and actionable insights that can be used for technical and business processes, this is known as data integration. Data integration is now considered as a significant approach to improve the accessibility, suitability, and quality of mission-critical data in an organization. Consultants help you through the complete implementation process, from understanding, selecting, implementing, cleansing, monitoring, transforming, to delivering consistently reliable data, and governed information in real-time.
What are the steps involved in a data integration project?
There are three steps involved in a data integration project:
i. Accessing Data
The data is accessed from all sources and places, whether it is available on-premises, provided by related partners, in the cloud or a mixture of both.
The integrated data is delivered to the business as and when
it is required. Data can be delivered in batches, close to real-time or in
Why should you choose data integration for your business?
1. EASY AND FAST CONNECTIONS
Generally, building connections have been a hectic job that could take months. End-to-end manually coded integrations are not only prolonged but also unsafe. When you try to make more than three connections, it can be difficult to adjust them. Even applying small changes in one of the integrations can develop errors or a virus in the others.
In iPaas architecture of modern businesses, data access is easy and fast due to in-built adapters and connectors that can be imitated easily.
2. AVAILABILITY OF THE DATA
It is not easy to manage data siloes and batch processes. You
must keep the existing data of all the right stakeholders in one place and it
should be made available in real-time. That’s why it’s necessary to join all
the data sources quickly to get the necessary information in a single location
While it is enough for some industries to transfer data once or twice times a day, many industries are faster than that, and you must be able to make data available in real-time.
3. INTEGRATE DATA FROM MULTIPLE SOURCES
In an organization, you use a number of applications, systems, and data warehouses. As long as these data sources are distinct and siloed, it is hard to make existing data meaningful. For better cooperation, it is important to join all the dissimilar data sources with one another to make out the value of insights. When the right information is made available on a single location in real-time for all the stakeholders, you can utilize the information to improve the processes and provide superior customer service.
So, here is the need for data integration becomes very important. Generally, B2B integrations are very complicated and need integration experts to manage data. Sometimes, hundreds of data sources need to be integrated. The thing that is more difficult is that some data sources are on-premise and others are in the cloud, so there can be several firewalls, protocols, and data formats.
With an efficient data integration tool under the assistance of the right consultant will help you combine all data sources proficiently.
4. BETTER INSIGHTS BRING IMPROVEMENTS
Once you get the all data in one place, you can finally make use of the available information. You can utilize the raw information, or your data analysts can formulate insights from the existing information. Whether you can use these insights or some data tools that need to be deployed on the information and the result will be positive. There will be better intelligence on your processes and customers and you can make better decisions according to the available data and directly improve your processes.
Generally, there is a hidden value in every sort of data. The businesses that realize it early and unlock the unseen information in the data can considerably take advantage as compared to other competitions.
5. BETTER COLLABORATION
When you need to make internal collaboration better with your
trading associates, you can only make this possible by integrating data.
Fortunately, you can get amazing benefits of automating the flow of information
on the way how you handle your business.
It’s critical to provide relevant data to significant stakeholders. This way, they can view better insights, and implementing data integration can automate a few processes that have been handled manually before. Whether you do internal or external integration, your employees and partners can make better collaboration, because they will get more information at their end.
As you are already aware, data is a valuable asset in any business, either you can make the most out of it by handling the data safely and securely or you may lose an important deal by disclosing important details. Therefore, it is vital to combine all data to form actionable and meaningful insights. More than these benefits, you get improved data quality that increases your competitiveness in the market.
Build a data integration strategy for your business and plan what measures you should take to improve the accessibility of data both in external and internal processes. The overall objective is to create more profit by utilizing the power of data. One of the most important parts of the business is attracting customers, so offering superior services than your competitors shouldn’t be ignored. If you are planning to strategize your business processes, contact Data Integration Consultants. ExistBI have experienced teams within the United States, United Kingdom, and Europe.
The quantity of data existing in the world today is truly remarkable. According to the estimation of the World Economic Forum, 463 exabytes of data will be generated worldwide daily by the year 2025. Data is no longer considered as just a result of running an organization—now it has become the lifeblood of every operation of any business. Today, businesses depend on high-quality data to function and be successful. The right Data Governance Consultingteam will help you achieve high-quality data from multiple sources in various structures to be analyzed to cultivate success. In this blog, let’s discuss how organizations can ensure success with data governance consulting…
A powerful CRM (Custom Relationship Management) comprising of clean, correct data enables companies to create stronger customer relationships, provide smooth customer experiences, and formulate more efficient sales and marketing campaigns. It can assist you in discovering important insights and encourage the growth of new products and services. But the most important factor that leads to success is to make certain the data is available to the people who need it, at the right time, in a format that is accessible. That is exactly where the need for data governance arises.
What is Data Governance?
Data Governance includes all the people, processes, and technology that have been deployed by an organization to manage its own data. You need to establish the standards for data that fulfill the custom needs of the organization and its processes. Although security and governance are two different scenarios, still a comprehensive data governance policy should conduct a security check. This type of security checks ensures that the correct people have suitable access to the right data that is compliant with rules and storage.
A Data Governance policy needs to fulfill numerous grounds to become efficient and successful. For each data set, define the following:
Where is the data residing?
To whom is the data available?
What is the structure of the data?
How accurately the important terms and articles within the data are defined?
What are the expectations of data quality in the organization?
What is the usage of that data in the organization?
What process should be followed by the data to meet these objectives?
Actually, finding answers to these questions is not a simple task. So, what’s the option for the organizations to set up and deploy an efficient governance policy? Here are a few vital points you should consider while implementing a data governance strategy.
Engage the Right People
At first look, people often think that Data Governance is a job only for IT. But actually, Data Governance reaches far beyond the capacities of an IT team. The IT team is important for fulfilling all the technical concerns of data management, however, the whole company must work together to identify the day-to-day processes to build a broad and effective strategy for delivering data to the people who need it.
A data governance strategy should be agreed throughout the organizational structure, including sales, marketing, tech support, product development, legal, management, compliance, finance, and also the IT people. This way, you can ensure that multiple perspectives and priorities within a large enterprise are reasonably characterized and make Data Governance policies and procedures to fulfill the needs of all departments across the company.
Don’t Just Focus on Processes
Particularly, the ultimate objective of Data Management and Governance should be to make data access and use easier as much as possible by generating rational, meaningful standards and processes that the data users can follow easily. Processes need control, and control is entirely significant when it is a matter of data.
The practice of generating those standards, processes, and policies can be pretty complicated. You need to all members of the organization to understand that Data Governance is a tool to make more effective business decisions, not to create hurdles in the workflow. Stay away from going deeper into a lengthy process-building and follow the below tips to ensure your Data Governance efforts give the desired outcome:
Firstly focus on the key objectives for the business and then move further to create more defined goals.
Choose the right people for the task, then generate the right processes and define the most appropriate technology needs.
Create clear objectives and evaluate your progress. Ensure they are measurable.
Characterize roles and responsibilities, so everybody is aware of why they are included in the process and what is expected from them.
Make the processes simpler and automate wherever possible.
Keep in mind that effective Data Governance is a continuous process.
Customize the Technology to Suit Your Needs
The exploration for suitable technology solutions gets less intimidating when you have a clear vision of goals that you need to extract from your data and how the established team is going to interact with this data. Once the set up is done, your Data Governance policy will serve as a roadmap to find the right technology.
When you start analyzing some prospective solutions, you
might notice that some components of your data policies and processes need to
be modified. Modifying the technologies to meet your need is normal today.
Start tailoring your processes and technologies to interrelate with each other
to meet your stated goals.
It is doubtful that you’ll find a tool that matches all of
your needs therefore, your policy should include the strategic implementation
of a centralized solution that can be integrated with third-party tools. There
are a few key functionalities you should consider in governance plan:
Reporting and Analytics
Similar to the emerging technologies like automatic vehicles that rely on exact data for optimal performance, the success of operations in the businesses also depends on the quality data and effective Data Governance. Once organizations identify this inbuilt value of the existing data, they can start taking the steps necessary for making the most out of their data investments. With data Governance Consulting, you can get help in employing the right people, processes, and technologies in place to ensure that the right data assets are available at the right time. ExistBI offers experienced Data Governance consulting in the United States, United Kingdom, and Europe.
What’s the first thing that you consider when planning a data migration project? It should be the design of a successful strategy for migration. The various stipulations presented available from data migration services have made it possible for users to easily upgrade their operations. Here are a few important things that are proving helpful for users and also for web hosts – benefits of data migration services:
What are the Benefits of Data Migration Services?
The transfer of databases to all new environments is a trending process in the world of big data and data analytics, however, the solution is not appropriate or possible for everyone. Making decisions quickly will not be to your advantage, as firstly you have to know all the potential approaches to take full advantage. Here are the following benefits of data migration;
It upgrades existing applications and services dealing with data within your organization.
It helps you to scale your resources to meet the growing needs of increasing business data.
It boosts competence and efficiency while maintaining overall IT operating costs as low as possible.
The professional services offer a pay-as-you-go model.
Data migration projects have three major categories that are;
Host-based software, which is suitable for replication (Copying files or any other upgrades of platform)
Array-based software, which deals with the data that is migrating between two similar systems
Network-based appliances, which helps in transferring the computer volumes, files, or blocks of data
When you start migrating the database, ensure you analyze the complete concept, and find out what’s right for you.
What use cases you should look for within data migration services?
Several companies, especially the larger organizations, have devoted a great amount of time and investment in recent years to discontinue explosion in their IT environments and update their environments simultaneously. Therefore, centralization and standardization are the key main factors here.
For migrating multiple diverse legacy systems to a new
modernized and centralized environment, you should seriously consider data
migration services. The related workload for larger organizations rapidly
scales up to maximum numbers.
Other than the elevated costs and long-term projects, these types of broad data migration projects also have other drawbacks to the loss of the companies’ policies and innovation. At the same time, they can put at risk conformity with important limits.
Data migration is perfect when it reduces the quantity of data to be moved and provides it in the best quality for use. This is made possible with a contemporary, centralized, and neutral platform for managing the information. The solution to these types of challenges is system-based independent management for the complete life cycle of legacy data and documents.
Data Staging Area
Data and its quality can be evaluated and optimized at the data staging area with the use of duplicate cleansing, enhancement from others, business-applicable sources, and management. This area is essentially important not for migration projects only, but also for every rapid business use case such as mergers and acquisitions, digital transformation, and the resultant digital business models and operations.
The professional services allow you to manage the data and
documents that are no longer required for various operations. It includes their
business strategies across their complete life cycle, from their movement, from
the production systems to legally appropriate storage and closing points.
The most important part of assistance, however, is that past data residual will always be accessible, which enables the business users of the company to migrate that specific part of the data from the target systems only when they need to in their daily business activities and operations, such as post orders.
Once the complete database has been moved from the legacy systems to the new environment, you should start analysis to find which data the customers should need, which they don’t need and why this data is important. A specific criterion for Data Reduction Potential Analysis (DRPA) would comprise of various units in the organization, different master data, and types of transaction data or some definite business objects. The result of DRPA takes a form of reports for management, whitelists or blacklists, which states the specific areas and fields in tables that are moved and no longer required there.
After clarifying the operational and historical information
of the organization, the next step to be followed is detailed planning for data
selection and migration in the design stage. The selection criteria from the
identification stage are then additionally refined and tested so that the
alternations in the data store should be done by software automatically.
After the design phase, the service providers should set up accurate and predefined filter rules such as blacklists and whitelists, which enables the customers to choose the tools which they want to utilize for transformation and migrating information. On the other hand, the provider can transfer the comprehensive data package to their own Extraction, Transformation, and Loading (ETL) solutions.
Modern generations of software are instruments and drivers of
digital transformation in various organizations. However, their success depends
importantly on the quality of the business data available there. The Analysis
made on the basis of incorrect or incomplete data directs to inaccurate results
and ultimately to wrong strategies and actions being taken. On the other hand,
the business that is using digitization to cleanse and constantly keep its data
maintained, staying ahead of the competition.
Web hosts must ensure that their migration service serves as an enabling point, not an obstacle for all the customers. They should promote service efficiencies like convenient demands and make sure to execute the migration quickly. There are different approaches available out there to proceed further towards the data transfer, including planning, analyzing, and strategizing the project. However, you would need an ideal infrastructure that can endorse the seamless functioning of various processes.
Therefore, if the service provider plans to proceed with its data migration services with all the essential features and use-cases, it can let more users drive into the process while also develop you as an impending competitor. Contact ExistBI for data migration support, with specialists in the United States, United Kingdom, and Europe.
From the term governance, it highlights taking control of data and how it moves and is used within the organization. It is about taking control and ownership of your data as an asset and how it can be used to attain corporate goals. So let’s get into the 4 facts you need to know about data governance…
1. Difference between Data Governance and Data Management
The concept of Data Governance and Data Management may sound similar but they refer to different things. You can think about Data Management as the umbrella term that refers to the specific detailed programs that an organization handles the data it produces, receives, and stores. It is mostly focused on handling data from the Information Technology (IT) perspective and practice.
On the other hand, This is the blueprint that lays these programs down and ensures that they are in line with regulations within the organization and the law and considered a part of the broader data management strategy. It is focused on handling data from the Corporate Management perspective, as a business strategy.
are very important concepts for today’s organizations, as it sets the tone for
the critical management of data in today’s economy. It is not just an issue for
the IT department but should be a concern across the organization – whether
you’re in Sales, Marketing, Human Resources, or an Executive, you need to know
about how data is being handled in your organization.
2. Benefits of Data Governance
Business Intelligence is a huge market. According to a report by Grand View Research, the industry was valued at US $24.9 Billion in 2018 and will have an expected annual growth rate of 10.1% from 2019 to 2025. Many companies have made the strategic implementation of data governance in their day-to-day operations.
As a result of these investments, plus getting the right data governance consulting as they roll out changes, companies have been able to get the following benefits:
compliance with internal and external regulations
Many companies have created the positions of Chief Data Officer or
Chief Information Officer as a response to the growing focus on data. Since
GDPR was rolled out, any companies doing business with Europe needed to have
this representative. Dedicating a person in this role can help manage the task
of maintaining data governance in
data from breaches
With so many large corporations becoming vulnerable to data
breaches, such as leaking information about their employees and customers,
securing the system is critical. This is a hit towards reputation and in turn,
revenue. Putting up the proper security measures is a key activity.
the data architecture across your organization
Connected to the protection of data, having all employees from top
to bottom understand the importance of data
security and having a standardized process to receive, store, and process
data will help support the initiative.
quality and accessibility for everyone in the company
Helping the whole company understand the data infrastructure will
make them invest their efforts to maintain the data integrity for their own
activities because they can see how it affects their colleagues and the
company’s bottom line.
transparent about data within and outside the company
Outlining the sources and destinations of relevant data helps you
trace where any aberrations and mistakes may have occurred, allowing you to
address and mitigate any possible breaches.
smoother analysis and reporting processes
As part of business intelligence strategies, implementing
dashboards and enterprise-wide software that can analyze data without the need
for specialists. This returns quantitative results that can help explain any
inconsistencies and point your company to the direction of better strategies.
and increasing revenue through higher efficiency
Proper data governance
helps you use the information more efficiently. Because you invested in the
right tools, your reports and data
analysis are more reliable and help you make better decisions with your
3. Data Governance Challenges
Data Governance has been around for
some time, businesses still find it a challenge to implement these frameworks
understanding about the importance of data
The concept of data has been understood as the domain and
responsibility of the IT department. While this is no longer the case, you may
have to explain to your other departments that keeping data integrity is in
everyone’s best interest.
and rolling out enterprise-wide tools
There are a lot of business intelligence tools out in the market, and there are popular options and support systems that you can rely on to have a smooth user experience. Of course, this still requires money and effort to install.
4. Does My Business Need Data Governance
Given the challenges that surround the implementation of these frameworks, it can be difficult to get things running for your organization. However, getting around to these changes is not a question of if, but when. Soon, to comply with regulation and survive in a technology-reliant ecosystem, you will need these systems in place.
businesses can benefit from a sound data
governance strategy, but of course, your needs may be at a different scale
from other companies. This is where data governance consulting can help you. If
you aren’t sure where establishing authority over your data starts, there are
several experts on the field that can help guide you through the process. Your
company will be equipped with the right technology that your business needs
without the danger of over-investing.
Investing in Data Governance consulting is going to ease the transition. You will not only address the technical questions but the human challenges that will arise in the process as well. Choosing this direction will move you forward as a progressive organization, ready to exist and thrive in the new generation of business.
Data migration is the process of transporting data from one place to another. While it is easy to understand the concept of data migration, it is a tough task to implement the process of data migration. In fact, data migration services are one of the most complex tasks in the field of data engineering. Please check our Data Migration Tips below…
What are the most common use cases of data migration?
Before getting into Data migration tips, let’s know three common use cases for data migration:
(a ) Application migration
(b) Storage migration
(c) Cloud migration
It is the transfer of an application from one storage or
server location to another one. Either you can migrate an application from an
onsite server to a cloud-based server, a cloud-based server to another
cloud-based server, or shift data from one application to a new application,
which only acknowledges data in a particular format.
It is the migration of data from legacy storage systems that are isolated and have become walled-off into data silos to storage systems that allow improved integration throughout all the information systems belonging to a business. Transferring data into a more integrated data warehousing system provides considerably better processing, flexibility, and economical scaling. It might also offer advanced data management capabilities such as snapshots, cloning, disaster recovery, backups, and more.
Cloud migration is the process of moving data from onsite servers or on-premises servers to a cloud-based data warehouse. This is the most important element for large organizational data systems right now. According to Forbes reports, 83% of businesses will be transferring their data systems to the cloud by 2020.
Can Lead to Delay of Data Migration
There are various steps an organization can take to finish a data migration process effectively.
Conduct a migration impact assessment to analyze the levels of data quality and the probable cost of project delays. It defines the approach to be used for migration, creating a timeline and evaluating each level of the process. Additionally, it is essential to understand how to solve some of the most common challenges in data migration.
All data migration processes are different and regardless of the fact that projects will differ according to their scope, time limit, type of migrating database, and other significant circumstances; there are three major concerns that can delay the process of migration.
Insufficient Planning for Data Preparation
Data migration is not the same as copying information, so transferring data to a particular cloud storage solution requires good preparation. The time allowed for it must be measured in the data migration plan and the budget as well. If you ignore this step, you may lose the chance to filter out redundant data, like backups, old versions, or draft files that are often available in data sets that would not be required in the cloud workflow. The key is to discover an automated approach to choose what data will be moved and then save the important records without overlooking that various cloud workflows may need the data in a diverse format or enterprise than on-premises applications.
Lack of Data Integrity Assessment and Protection
Data validation is a vital step and also the easiest to however, it should not be assumed on the basis of thoughts and opinions but on confirmed facts. There is a valid concern that unauthorized access can occur during data transfer. It is the preparation and transfer of the data where the information is most vulnerable to lose or hacking.
Underestimating Cloud Scaling
Once the data reaches its target location in the cloud, the process of data migration is in the intermediate stage, the project is only halfway there. You have to make sure that the data transferred is true to the existing data source. Verifying that it can make storage cache layers complex. After the sent data has been verified, it is essential to extract, reformat, and dispense it so that it becomes ready to use by cloud-based applications and services.
Approaches to a Potential Journey to the Cloud
As organizations are adopting data analytics services
and applications in the Cloud, the intricacy of their data management grows. As
the Cloud itself has numerous environments and applications within its hybrid
Types of Hybrid Approaches
To compete in this multi-cloud environment, organizations require an end-to-end hybrid data management platform, enabling them to provide business data rapidly and safely in the cloud, hybrid platform, and on-premises ecosystems. It can be made possible based on various approaches:
• Simple hybrid integration- The most suitable for companies searching for a platform that supports them with integrating all of their cloud-based SaaS applications with all available local data to get a holistic view. For simple hybrid integration, an Integration Platform as a Service (iPaaS) would fulfill the needs to integrate applications, data, and processes in the cloud, hybrid and on-premises environments. The approach works well when companies are starting with a similar approach to integrate cloud applications and data sources.
• Advanced hybrid approach– As the organization grows, the intricacy around data management also increases, not only from the data sources or data volume but also related to new use cases. It needs a more developed platform to manage growing complexity, an advanced hybrid integration form. Businesses longing to see their requirements fulfilled should shift to next-generation iPaaS, which are exclusive, modular, metadata-based platforms, integrating big data, cloud, and on-premises systems. It even conducts advanced integration use cases, such as the Internet of Things and other compound data management solutions for business and IT users both.
The latest cloud, big data, and IoT technologies can be overwhelming. To take advantage of all these technologies, we must learn, modify our processes, and adapt our approach to data. This way, you can manage to master the complicacy and leverage the benefits. Migrating to the cloud is not only a matter of data but also of processes, this is vitally important when planning to conduct any data migration project.
If you want to avoid challenges, to ensure the final cloud infrastructure supports the required workflow, follow these data migration tips, if possible hire professional Data Migration Services to get expert assistance to complete your project. ExistBI has specialist teams in the United States, the United Kingdom, and Europe.
According to a recent report by Allied Market Research, the global market for datawarehousing is expected to rise to $34.7 billion by 2025, which is almost double its worth of $18.6 billion in 2017. What fulfills the needs from investment in data warehouse development in an organization?
The role of innovative applications and practices in an enterprise has increased the need for Cloud data warehouse technology, which boosts the efficiency and lessen the costs across company functions. Today, various departments like marketing, finance, and supply chain operations take advantage of modern Data Warehouse Consulting as much as the engineering and data science teams do.
Types of Data Warehouse
There are three main types of data warehouses that the users have been using worldwide, which are:
Enterprise Data Warehouse
Operational Data Store
Why is Data Warehouse Development Necessary?
Here, explore the list of five business needs that can be fulfilled with bigger investments in modern enterprise data warehouse development.
1. Need to Access and Act on Data in Real-Time
Nowadays, businesses can do data processing and detecting signals in real-time that had much higher latency in traditional systems. Identifying the stock levels at retail stores, for example, lets a retailer respond to customer trends and solve key concerns before they negatively impact the business. Superior yet, by merging a real-time vision of supply chain data and weather, the retailer can restock stores running vacant before it goes empty.
Modern data warehouses make data visibly understandable, meaningful, and actionable in real-time by implementing an extract-load-transform (ELT) method over the single omnipresent extract-transform-load (ETL) model, in which the cleaning, transformation, or enrichment of data on an external server before loading is done into the data warehouse. With an ELT approach to working, raw data is explored, drawn and analyzed from its source and loaded, comparatively unchanged, into the data warehouse, enabling it to be a lot faster to use and analyze.
2. Search for a Holistic View of the Customer
In the past, the information existing in a company about its customers was collected in siloes. The data from one source would be stored in a data silo, and data from another source is saved in a data lake or stored in an on-premises traditional system. Without a simpler approach to connecting the dots, it was complicated to make sure that high-value customers were getting the best experience that’s possible.
The assurance of a data lake strategy is that complete information of your company, whether it is structured, semi-structured, or similar to raw data, can be rapidly and easily queried from a single place. With this approach, a data warehouse for an enterprise can facilitate an absolute view of the customer, supporting to improve campaign performance, reduce churn, and eventually, to develop revenue. An enterprise data warehouse also enablespredictive analytics, where teams use situation modeling and data-driven predictions to inform marketing and other business decisions.
3. Recognizing Data Lineage to Ensure Regulatory Compliance
In huge organizations, it becomes tough to discover the origin of specific data. This situation can give rise to problems, particularly for the finance and accounting department, when they conduct audits. Traditionally, the only recourse they have to file is a support request that can be expensive and slow. A modern enterprise data warehouse allows its data customers to audit and examine data sources directly and locate errors rapidly.
You can also implement compliance through the General Data Protection Regulation (GDPR) presented by the EU with the use of a modern data warehouse. When you don’t have a data warehouse in your system, it is likely that your company would have to set up a difficult process to fulfill every GDPR request. This process would engross various functions or business components, looking for relevant PII data. Therefore, you’ll essentially have to search in only one place with a data warehouse.
4. Allowing Non-Technical People to Query Data Rapidly and Cheaply
Developing a data warehouse can also profit your non-technical personnel in job operations beyond finance, marketing and the supply chain. For example, architects and store designers can improve the experience of the customer within new stores by digging deep into data from IoT devices located in available locations to recognize which parts of the retail stores are most or least engaging. Global service managers can support their decision-making on whether to extend retailing outlets or move product lines on a powerful set of information that includes data regarding hiring and retention of employees, in addition to typical metrics like cost per square foot.
5. Need to Join Data Together into a Single Location
Nowadays, many data sets are simply too huge to move and
query faster and in a cost-effective manner. To restrain expenses and latency,
some companies use regional clouds. According to research, for companies that
use a multi-cloud strategy, 81 percent of them end up with data spread across
multiple platforms from contending cloud providers. Getting rid of these
obstacles is the main concern for organizations that struggle to be
With a modern data warehouse, users can integrate various data sources, applications, and departments together to combine all information in a single location, allowing authorized users access anywhere, anytime. Therefore, managers can leave the worries of maintaining the data on their own and making it available to all business users in real-time.
Data Warehouse technology has made things easier and allowed the organization to create a single storehouse for their data and providing a unified view of data to its users. In this way, the security and privacy of data are also ensured as only the legal users are allowed to access sensitive information, including important credentials, which are kept safe from hackers.
Top-class Data Warehousing Consulting will help you to understand how companies can store data across various regions and cloud providers and query it as an inclusive unified data set. Thinking of developing your enterprise data warehouse? Get advice from the data experts and make your data easily manageable and accessible! ExistBI offers Data Warehouse consulting services in the United States, the United Kingdom, and Europe.
Digital transformation is now involved in every part of the business. Demands for round-the-clock and self-service have extended beyond consumers and impact business-to-business (B2B) companies as well. That’s why; the consumers who make the purchasing decisions have become familiar with the ease of digital interactions. Whether they are at home or in the office, consumers want a good experience each and every time they connect to your business. People opt for Informatica Consulting to discover in-depth information about connecting to digital transformation with Informatica Cloud B2B Gateway.
Companies who work within traditional B2B models have previously focused purely on the price or products and have ignored the customers, missing out on the powerful human contact at various points of interaction. As per B2B Marketing, 96 percent of B2B buyers mold their decision to buy again based on the experience they had. And 83 percent of people who have had a good experience with your business will refer you to a friend. B2B companies have to make important connections at each touchpoint to improve and personalize the customer experience (CX).
Here are the basic five strategies that can help you to provide a better experience to your customers:
Focus on Customer
Be Good at Data
Predict and Act
Get Inspired to Make a Big Impact
B2B marketers who have the aim to be more customer-centric should rethink how they can effectively obtain new customers, cultivate relationships with existing customers, and create long-lasting loyalty. Start with implementing the above five strategies and adopt Informatica solutions for more effective results.
Ensure Savings with Informatica Cloud B2B Gateway
Moving further from customer experience, another vital aspect is managing your partner community. There is no doubt that customer experience will increase your sales, but managing your trading-partner community is also extremely important for your business. Simplifying integration with your trading partners can accelerate partner on-boarding, speed up data exchange, and decrease operational costs for all parties.
looking for B2B solutions find trading-partner community management to be
challenging, protracted and costly. B2B business users have the questions that:
How can I accelerate the partner on-boarding
How can I present additional control to my
partners and enlarge my association with them?
How can I meet various service-level
agreements (SLAs) for partners and owners?
How can I enlarge my community of partners to
embrace smaller businesses?
The more you focus on these needs and challenges with customers, the more you’ll understand that meeting these objectives requires collaborating partners in a self-service mode. So how can you do that? With Informatica’s B2B partner portal!
a new trading partner is an essential step in the partner-management life
cycle, and businesses exert immense efforts and resources into modifying,
simplifying and accelerating the process. A self-service-based, joint
onboarding portal can considerably cut down that step and reduce operational
you get your trading partner onboard, you can then start your ongoing daily
interactions. You can exchange multiple hundreds of files and messages, which
you want to ensure, are processed successfully as per agreed-upon SLAs.
B2B portal is a strategic tool that can help you find the answers for your
needs and develop a smooth partner relationship.
Partner Community Management
This new self-service Informatica B2B Partners Portal allows you to simplify and accelerate the partner-on-boarding process. Informatica Cloud B2B Gateway, a component of partner community management service, helps to improve integration and collaboration among all trading partners.
Once you have set up the partner portal and given access to your partners, they can log in to track and verify the status of data exchanges by using the B2B event-tracking and monitoring system. Partners obtain a sophisticated view of the status through a dashboard. They can further explore the status of particular files and messages with the events screen. The administrator of your organization has the authority to control the portal and can brand it to your organization’s needs.
Making yourself able to connect with your partner community is a crucial task, which should always be put on high-priority. But, what can you do when your partners do not have a suitable EDI or file-transfer solution at their services? Then how will you exchange files and messages with them? The upcoming release of Informatica’s Cloud B2B Gateway will come up with a solution for you, enabling your trading partners to use Partners Portal to send and receive files to and from your organization for their reference.
With a simple online login, they can access the Partners Portal, and feed the data and files for processing, and can also download the files that are waiting for them to collect. The process of sending and receiving files takes place on the basis of HTTPs for better security and to maintain the files on your premises’ domain. Informatica file servers take care of the management and set up of the HTTPs server.
The new Informatica B2B Gateway Cloud AS2 was tested and certified by Drummond as a Suitable AS2 solution for its great compliance, security, and interoperability.
Providing the best customer experience (CX), data integration, and collaborating with partners is the key to success in the B2B market. And Informatica has always put the best efforts to provide the best solutions for changing and improving the way you handle your business. When it comes to online business solutions, Informatica Cloud B2B Gateway is a single solution that will help you manage all the aspects of a B2B business. It helps you to modernize your data integration processes, enhance customer experience, and manage community partners.
Informatica’s Next-Generation Cloud B2B Gateway provides Informatica Intelligent Cloud Services that help the customers to expand their organization integration platform to the integration with all of their external business community partners.
For leveraging a software solution for your company, you must need thorough guidance and directions to help you identify and understand the needs of your business in the first place. Then you can select what and how to implement it in your business. Contact leading Informatica Consulting partner for comprehensive guidance throughout your digital transformation journey. ExistBI offers Informatica services in the United States, the United Kingdom, and Europe.
Do you know which data governance strategies best fit your organization? One of the big decisions of your organization that you will make on your transformation to become data-driven is what type of governance you should put into practice for your data and content. Preferably, the governance model you choose should be secure, while also allowing your employees to utilize the available data to make improved decisions. In Tableau Bootcamp, you’ll come across three governance models that the Tableau blueprint represents.
Here are the three types of governance models that are in real-time practice in different industries:
Generally, the IT department is only one central department that holds your entire data and controls the analytics environment. They categorize the data sources, collect data and reports, and make them accessible to your analysts and other business users across the organization. You can opt for a centralized data governance model because of the following reasons:
Users can handle the project with fewer data literacy or skills in the entire organization
The data needs are enormously sensitive and require profound control and monitoring over the users who have access
You have an existing traditional top-down IT or data strategy that isn’t varying anytime presently
Here are some of the possible drawbacks of a centralized strategy:
The owners get loaded up with access requests from throughout the business that results in lengthy procedures and time-consumption for business decisions or sometimes gets made without the correct information.
The word never gets out to the rest of the business that the reports or data exist for business owners to use because they haven’t been engaged in preparing it. Your investment is never fully utilized.
You can never embark upon the data and analytics skills-gap existing in your organization.
In a delegated model, the possession and liability of the data are given to personnel outside of the central IT team, which hold positions of Site Managers or Project Leaders in Tableau Server for changing permissions. Delegated requires wide-ranging processes in a position to authenticate and verify data that is published. In a number of delegation models, it may come under the centralized team to verify finished content by the delegates.
There are the following reasons to have a delegated model of data governance in your organization:
Data literacy is required in several areas, but still needs enhancements in other areas.
Some of the data is sensitive and is still required to be handled responsibly by a central team only.
Your organization is conducting a gradual transition towards self-governing or independence.
You have to verify the user’s content earlier than certifying it as data expertise is still being constructed.
Reporting and data requests are surpassing a centralized team’s ability to produce.
Here are some latent difficulties of a delegated strategy:
You would need an inclusive method of certifying and validating data and content that is marked by users saying they understand the process.
A special training scheme for users is necessary to allow them to generate good content. A little or no training will result in the generation of poor content or a combination of poor and good content, without any appropriate data literacy.
Site Administrators or Project Leaders need to take training to make sure that they understand the tones of the roles in Tableau Server.
The various employees of the organization produce content and data regularly, either by the creators on the desktop or by explorers in web edit. Every user, including viewers, has some level of data-literacy. Ad-hoc or sandbox content vs. certified content is distinguished, and the procedure of endorsement to certification is apparent and definite. Analytical skills should be good throughout the organization among all business users.
There are the following reasons to have a self-governing model in your organization:
Data literacy is good throughout the organization, and users need to be able to find answers to their own questions by using data.
Require fast reporting to exceed the supply by a centralized team.
Your company holds an open data policy in your organization where all employees are allowed to view most of the data sources, excluding sensitive data.
Here are a few shortcomings of a self-governing strategy:
Generating custom admin reports on the basis of the Tableau Server data to track who has accessed what may be essential for directive requirements
Needs suitable and frequent training for all users at all stages, either at a Creator, Explorer, Viewer, or Admin level
It is really important to understand the value and role of data that exists in your organization, and moreover, it is required to keep the sensitive data safe and protected from the hands of the viewers and users who can misuse the role of this data. Therefore, you should always startup by building a suitable data governance strategy for your organization that fits perfectly with the needs of your business.
For doing so, you need to have a complete understanding of your existing data and business needs. Then only you can evaluate and examine different types of models to implement in your organization. The three above explained strategies are mostly used by companies to tackle their data needs and manage them efficiently. Evaluate your needs and find the most suitable one for you.
Tableau puts tremendous effort into helping people to change the way they use data and provide a unified view of data on a single screen with a different range of dashboards. If you are not aware of all the policies and regulations that are associated with various governance models, you have to understand it’s the usage and practical implementation in a real-time world.
Join Tableau Bootcamp and get complete training to understand and analyze data effectively with a wide range of data dashboards. ExistBI offers a unique Tableau Bootcamps in the United States, the United Kingdom, and Europe.
Challenges are inevitable, how you face them is your choice. In big data projects, you can rarely avoid a few challenges and obstacles to success. However, it is not impossible to tackle them, having an awareness of such obstructions as soon as possible enables you to defeat them.
Similarly, in the world of data integration, a few challenges will inevitably come up along the way. Having Data Integration Consultants on your team will help you to identify and understand what those barriers to success are and how you can overcome them to achieve results.
Challenge 1 – Defining Data Integration
One of the major challenges of data integration is defining its significance. Data integration is often confused with business integration and system integration, however, they are very much separate entireties. Typically, data integration is a collection and integration of complete data sources from internal and external systems and devices in a separate data structure for the purpose of cleansing, managing, and analyzing the data. Data integration can take place in a data warehouse and requires dedicated software to handle large data repositories from internal and external sources.
The software extracts data, merge various data assets, and then delivers the information in a unified form during this process. When you understand the right words for defining this process, you will find yourself one step closer to getting the results you want.
Challenge 2: Data Diversity
Another difficulty that can arise during the data integration process is that the information in the data sources is available in different forms. Traditional legacy systems hold data in different forms; however, a single data integration platform cannot deal with diversity. Therefore, it must all be in a similar form for data analysis.
You need to be aware of the heterogeneity of data formats at the initial stages to overcome this challenge. When you start your project, firstly evaluate your information to identify the variety of information you have. Then, convert the whole information into a similar format, so that the data integration platform can analyze it.
Challenge 3: Extracting Valuable Insights
A common hindrance that everyone confronts about data integration is its complexity to extract value from your data after the integration of a variety of other sources. It is not as simple as you think because there is a huge diversity in the data available out there and it is getting more complex and sizeable with the growth of the industry that captures information via sensors, mobile devices, and social media. Your data analytics tool must be efficient at connecting with the data integration platform seamlessly.
Always remember one thing, when you are going to exploit data integration software; check whether it connects to your analytics tool or not. So if you make the right tech solution decisions, you can escape from various hindrances that can make your data useless.
Data integration can face several challenges during the implementation process if you don’t approach it the right way. You require knowledge and thorough planning for successful data integration. To guarantee success let the experts do their work and hire Data Integration Consultants to handle your data integration projects.
Do you know what is available in the content store of Cognos Analytics? When planning to upgrade your Cognos Analytics, you should check whether your content store is performing well or not. The content store holds a massive amount of important information regarding Cognos report specifications, packages, report outputs, scheduling, data source connections, etc. Through your Cognos Training, you will discover how to cleanup Cognos Content Store is like a garage that continually receives increasing amounts of items that eventually need clearing out.
Reason for Content Store Bloat
One of the biggest reasons for the overstuffing of the content store is the bloating of custom folders accessed by end-users. Self-service was a hallucination from a few years back, and maximum end users didn’t produce their own content, but only ran specific reports. Today, IBM Cognos has become more advanced and user-friendly. However, the end-users continue to fail to utilize it thoroughly and create multiple versions of their own content, which results in content store bloat. So, if the size of the Cognos Analytics content store increases, it’s performance will also slow down and create unsteadiness.
Cognos enables full control of users over their content in a personal and public environment. If content retention rules are not followed, there is a huge possibility that content will stay un-used and start collecting in the Content Store. With the passage of time, this content will get congested and become unorganized to preserve a managed environment.
Here are the few side effects of the overflowed content store:
Locking of the Content Store
Corrupt files, etc.
Clean Your Cognos Analytics Content Store
All Cognos admins should follow these few steps to avoid performance issues in Cognos Analytics Content Store:
Turn on Cognos Analytics Auditing that will help you to know what data is in use constantly and what is not used for a long time.
Identify orphaned content with thorough and routine checking of your website. This type of content stays in the system when a system user with a particular folder leaves the company. Then the user is eliminated from the security, but the existing content remains there and gets orphaned. So, for handling such concerns, either delete the content or transfer the data to another user.
For the various version created, produce definite guidelines. The results of any saved reports always remain in the database, so it is advised to keep minimum copies. You can ensure this by adjusting the report output version’s setting in its properties.
To boost up the performance and trim down the size of the content store, IBM Cognos Content Archivalenables you to save archived data in external storage.
Cognos Admins get a Content Removal option to delete output versions in the Public Folder or Custom Folders.
Cognos Analytics has now come up with an automatic Content Store Cleanup Solution. Therefore, by using this solution, you can identify large and outdated content through various aspects, such as by pre-defined size, data that is not used for days or months, data that is not having any user, etc. So, if you require a smooth upgrade to the latest version of Cognos Analytics, shift to Cloud or following these best practices specified above. Cleaning of the Content Store is a routine task that you must execute on time to avoid performance issues.
Join ourCognos Training to know more tips and tricks about keeping your content store clean and handy features in the latest upgrades of Cognos Analytics.
Salesforce lighting has empowered data managers in terms of providing a customized layout and experience suitable to match the specific business profile. Managers are able to change the layout with standard Lightning components, which imparts a unique Org experience and Salesforce Consulting Partner provides the users with multiple features to get a customized layout. In this blog, we are going to discuss 5 Salesforce lightning navigation tips by Salesforce consulting partner.
These features can be used in lightning Org quickly. You can also create a combination comprising two or more features to create an experience for your specific needs.
Similar to the browser, you get a star icon at the top right of your screen that permits you to add your Favorite pages you want to access quickly. This feature not only allows adding documents and records but also implements Favorite Reports, Dashboards, List views, etc. When you need to work daily on any page and would need to access it frequently, press the star icon and add the page to your Favorites list. The Edit Favorites button can also be used to revise, delete or rename your favorites list.
2. Pinning List Views
You may have observed that Salesforce sets the default according to the ‘Recently Viewed’ list view. But with new lightning features, there is a solution. There is a new addition to every object in Salesforce that is a pin icon, right next to the list view. To skip the list view, click on this pin icon whenever you visit an object’s home page.
3. Customized Navigation Bar
Lightning has also introduced a comprehensive customized navigation bar, with which you can monitor
Sequence of tabs
Insert extra tabs
Add documents, records, dashboards, list Views, Reports, etc.
Editing navigational items
With all these features, you have the ability to entirely modify the typical view of Salesforce you will use every day.
4. Density Settings
Salesforce has added a new feature last year, which lets you adjust the amount of data that you can view on your screen. So, if you have an Organization that generates a massive amount of data, which requires frequent scrutinizing, then this feature will improve efficiency. It comes with two options, Comfy or Compact. Compact provides a Classic experience having immense information available on your screen view.
5. Kanban View
Kanban’s view on the Opportunities object is a game-changer for Sales users, which enables them to get a logical view of their opportunities, not only in a simple data list view. This view is also applicable to other records influenced by any back-processes, such as Leads or Cases. Not just offering a different layout, a Kanban view also presents the following functions:
Allows viewing the total amount of data in each step
Let’s drag and drop Opportunities to various stages
Notifies about the appropriate details you should know, like a number of stages completed, pending forms or task completion, etc.
Here you got an overview of features of Lightning that will help you get a more unique and customized experience with Salesforce. To get the most out of the software and access more tools, contact ExistBI Salesforce Consulting Partner today.
For any MDM (Master Data Management) task, data cleansing and standardization is an essential part. Informatica MDM’s Multi-Domain Edition, MDE, offers an extensive amount of really out-of-the-box cleansing functionalities. But sometimes these out-of-the-box features are not sufficient, and it requires a complete function to accomplish the process of data cleansing and standardization, like address validation and sequence generation. Here, Informatica Data Quality(IDQ) presents a broad range of cleansing and standardization functions that can be used easily with Informatica MDM. In this blog, we are going to discuss the integration of informatica data quality tips (IDQ) with MDM.
Let’s check out assorted options for integrating Informatica MDM with IDQ, with the pros and cons of each alternative, and highlight the best options.
Integration of Informatica IDQ with MDM can be achieved by using these three options:
Informatica Platform staging
IDQ Cleanse Library
Informatica MDM as target
Informatica Platform Staging
Informatica released new functionality in MDM’s Multi-Domain Edition (MDE) version 10.x that is Informatica Platform Staging used to do the integration with the developer tool, IDQ. This function allows staging and cleansing directly with IDQ mappings to Stage tables in MDM, avoiding the landing tables.
Quick Availability in Developer tool after synchronization
Automatically highlights the changes in the structures into the Developer tool
Load data into MDM’s staging tables by sidestepping the landing tables
Not easy to maintain because of its connectivity with each base object
Hub Stages like hard delete detection, Delta detection, and audit trails are not available
Manual Broadcasting for system-generated columns required
Invalid search records are not discarded when loading data to stage
IDQ Cleanse Library
IDQ enables to develop functions for operation mappings and consuming them as a web service to transfer it for Informatica MDM hub usage, creating a new cleansing library called IDQ cleanse library. This function permits the use of received IDQ to cleanse functions as with other out-of-the-box cleansing functions. Informatica MDM Hub operates as a Web service consumer application, utilizing IDQ’s web services.
Ability to easily build transformations compared to complicated java functions.
Hub Stage processing options like hard delete detection, delta detection, audit trail are available.
Require manual creation or updating of physical data objects for each staging table.
Only synchronizes the web service citations
Informatica MDM as Target
To load data into the landing tables Informatica MDM, IDQ can be used as an ETL tool, or these IDQ mappings can also be used to load data directly into the staging tables.
Standardizing data in the Hub Stage Process is not required
Hub Stage processing options are available
Ability to process on a lower version of Informatica MDM
Need to create and update physical data objects manually
Hub Stage options are not available.
Need to produce System columns manually
Invalid search records are not rejected when loading data into stage
According to the client requirements, you can now have a multitude of options that are available for integrating Informatica Data Quality (IDQ) with Informatica MDM. You can select the ideal options after analyzing the needs, pros, and cons of each option. Join ExistBI’s Informatica Online Training to understand in greater detail data cleansing and standardization.
A company’s main objective is to establish well-versed data driven decision making framework, a Business Intelligence solution processes challenging company decisions in a fast, intelligent, and efficient manner. Companies that ignore a single element of the data analytics process will see a significant impact on performance and profitability. Business Intelligence Consulting has developed a BI framework that creates a data-driven culture and covers all the functionalities of each BI solution so that their customers get the greatest return of income from their investment.
Let’s check out below four key components of a BI Framework:
The planning component includes trend recognition, forecasts generation, determining business performance, and scrutinizing plans.
In Trend Analysis, BI users are able to categorize various models in past statistics and recognize the opportunities the company can successfully attain alongside, identifying the challenges that the company could face in the future. For example, business users can check the behavior with which the company’s profit is growing in a specific duration.
Forecasting enables the users to predict future insights that help in setting profitable targets by analyzing the complete historical data of the last few years. Forecasting would be processed by the data analyst within the company.
Performance Analysis comprises of internal and external standards analyses the overall sales reports and finds out the highest-selling products that are imparting the greatest profit. Then the company can acquire more valuable insights into the production and profit in the industry.
The plan is being analyzed from different perceptions by evaluating the records related to the service like various targeted marketplaces, different rates, and existing similar products, etc. Then, the final plan is distributed in various departments for execution.
2. Plan Execution
The plan execution component is used to evaluate the success goals of a plan against the actual achievements. It allows you to conduct root cause analysis to recognize and analyze the drawbacks in your executed plan that affected your target profit.
In change analysis, you can identify the actual and actionable points requiring adaption in your plan. You can process various scenarios by commencing new products or services to understand the impact they will have on your business. Based on analysis with BI solution, you can better understand your business needs.
The purpose of the optimization component is to put the data of BI solution into practice to optimize the internal processing of the business. So that you can utilize the insights to enhance marketing after evaluating the changes in the plan for launching a new product in the industry and getting better sales in the areas which are underperforming.
It is a fact of this business generation that a company with a BI strategy and solution in operation are far more likely to succeed than one without. The BI approach plays a foremost function in the organizing, implementing, and enduring execution of BI solutions. Business intelligence Consulting provides a blueprint that facilitates businesses to evaluate their performance, discover opportunities, and apply data reports and statistics to navigate business success.
So what type of strategies are you implementing? If you are not using BI solutions yet, you need to develop a BI strategy for your business to ensure your growth. It doesn’t matter, which type of industry, product or service you have; digital transformation is the core need of this competitive business environment.
This summer has seen the release of MicroStrategy’s Update 3 which has the first-ever HyperIntelligence component to support users with advanced functionalities. This upgrade is openly available through Amazon Web Services and Microsoft Azure. The revolutionary data intelligence accessible within the platform will have powerful benefits for business users, analysts, administrators, and developers. Today, we’re going to summarize what is new and what you can expect from this award-winning upgrade.
What’s new is MicroStrategy 2019 Upgrade 3
1. Firstly, users can expect advanced analytics and data intelligence within their daily calendar. The update provides the user with all past, present, and future information to ensure they are fully prepared for every meeting or event. This is also accessible within the HyperMobile app and the iOS calendar.
2. MicroStrategy has also enhanced the workings of Mircosoft Office, web browsers, business intelligence tools, and even Salesforce. With new HyperIntelligence technology emails can be enriched effortlessly. With what MicroStrategy calls ‘Zero-click analytics‘ users can have access to data analytics that can improve business productivity and growth. The user hovers the cursor over words and HyperWeb produces data related to this term. This contextual insight can be voice-activated using natural language to provide the answers needed. This HyperIntelligence functionality is truly next-generational. As previously mentioned, these benefits are not isolated to your desktop, the advantages of HyperIntelligence have been integrated into their mobile applications. This, therefore, provides accessible, fast and relevant information on-the-go.
3. The update also provides the designer with enhanced flexibility when designing their workstation cards. There are now multiple options available to customize the cards built to meet the requirements of the business user. For those who are new to MicroStrategy, there is still the pre-built template cards offering drag and drop metrics and attributes. However, customized cards have a variety of widgets to provide control and freedom.
4. MicroStrategy can now be seamlessly connected to several other platforms providing improving performance, scale, and security. These platforms include; Tableau, Qlik, Power BI, and Office. This upgrade enables these platforms to be prompted to generate reports resulting in potential federated analytics.
5. The update supports data integration with Teradata assets. Teradata has recently announced the launch of the Teradata Vantage platform. The Teradata platform allows users across an organization to use their preferred analytics tool across a large-scale data source.
Ready to update? It is also good to know that you will not need a metadata update to do so, neither will you need a full platform installment. For more support with MicroStrategy training or data integration consulting contact our expert team.
SAP BusinessObjects BI.4 is an invention of SAP that produces reporting applications and SAP business objects tools, using data from SAP BW and SAP HANA for data analytics. The tool kit of SAP BusinessObjects includes the tools for reporting and dashboards that business owners can use by consuming the data available in the form of tables or data structures in SAP HANA.
Check out the list below and explore the tools of Business Objects BI.4 that you would learn about in your SAP Business Objects Training;
1. SAP Lumira
SAP Lumira helps businesses to create visualizations, stories, reports, transform data, and to do ad-hoc dashboards. It is a self-service data visualization tool that connects to the SAP HANA database directly. BICS connection driver and SQLDBC language are used for forming the connection to the SAP HANA database.
2. SAP Crystal Reports
This window-based tool is used to generate reports using an in-built data structure for printing and publishing, for example, generating sales invoices, purchase orders, work orders, client reports, etc. JDBC/ODBC connectors and SQL language are used as the interactive language for the connection. It provides a crystal clear and high-pixel visual print, that’s why it is named Crystal reports.
3. SAP Design Studio
It is an advanced-level designing tool that helps business users to design influential reporting applications and dashboards. SAP Design Studio also uses BICS connection drivers to interact through the SQLDBC language. This tool has the capability to do server-side programming, requiring full compatibility and sustainability with SAP NetWeaver BW and SAP HANA platforms.
MS Excel is the most popular Microsoft tool that is used by non-expert users; it facilitates business owners to explore data in analytical and calculation view (hierarchical data and data in cube models) in SAP HANA. Direct OLAP connection with the help of ODBO connector using MDX language connects directly to the SAP HANA database.
5. Analysis Office
This is also a self-service analysis tool and leverages multi-dimensional data analysis. BICS connector through SQLDB language is used to form OLAP type connection with SAP HANA database or SAP BW. Business users can access and integrate the information available in the OLAP data sources.
Users of the whole organization can use this discovery-tool for searching or exploring fresh information and can access it from anywhere. The Explorer tool forms the connection with the SAP HANA database using an OLAP connection with a JDBC connector and SQL language.
7. Universe Designer
When indirect connections are formed with reporting tools like WebI (Web Intelligence) and Dashboard Designer, Universal Designer builds a transitional layer above the SAP HANA database. This helps to transform the relational and OLAP non-SAP data sources into important business information. For connecting to the database through SQL, IDT or UDT connects uses JDBC or ODBC connections.
8. Web Intelligence
This advanced reporting tool has the ability to ad-hoc and detailed reporting, utilizing query panels, etc. Web Intelligence uses the data available in semantic layers created by the IDT tool by using universes. Multiple resources can be accessed with IDT, while UDT allows accessing only one data source at a time.
9. Dashboard Designer
This Reporting Tool in the SAP BusinessObjects BI4 package is used for creating dashboards. It offers pre-designed dashboard templates to business users, which they can use to create powerful static and dynamic charts and visualization.
As already specified above, all the tools use separate connection drivers and database languages to connect with the data source platform. These tools are very beneficial to minimize the working operations of your business and to prepare better reports and documents.
The biggest reason behind people’s obsession with Tableau analytics is its amazing engine, Tableau Hyper cluster Database (“Extract”). By enhancing its multi-node capacity, separating and then distributing the query process, Hyper can act like original business MPP data, which can run on modern infrastructure like Kubernetes. Here, we will discuss how to build an MPP database with Hyper. If you are new to this technology, join Tableau Bootcamp to practically implement these aspects.
Fundamentals and Background Tableau’s Hyper database was built from Scratch with standard functionalities (LLVM Code generation, Columnar data store, in-built capabilities, etc.), SQL dialect compatibilities, and Postgres network. It is a fast, neat, and convenient database. You can assign a full set of Postgres SQL statements with the new Extract API including copying and moving data in huge amounts.
It’s great that we can approach the Hyper database with just minor adjustments to the Libpq based apps like PostgresODBC and PSQL by accessing the core potential of the engine.
MPP- (Shared-nothing Use Case)
Commonly MPP (Massive Parallel Processing) architecture provides a database to run multiple worker nodes, partial datasets and aggregators combining the results from processing nodes. But if the horizontal scalability is missing, it won’t be able to leverage multi-server nodes to accelerate single queries.
By adding twice as many nodes you then see a two-fold increase in the performance level. Take an example of a webshop, where you store all your transactions in a single extract file. For evaluating the overall customer value, firstly you need to process the transaction for a specific customer, and then you can view the output in your Tableau report for all customers. And, if you have multiple computers to calculate the customer value, which is located in the same node, the algorithm will independently work on separate servers for each customer’s transactions. Multiple nodes will get more performance.
Converting Hyper Database to MPP Architecture
So how would you do this? Let’s check out a few things to make this conversion successful:
1. Build independent worker nodes on generic hyper Database
Create a docker image from Hyper Database that can be monitored from different sources
To manage it’s flexibility, deploy it on Kubernetes as a Service
2. Build an aggregator that will be the master node. In Postgres 11, there is a link-like facility that diverts the queries to other databases (Hyper also acts like a Postgres). So firstly deploy the Postgres 11 on Kubernetes, and set-up foreign-data wrapper with the help of Hyper. Then, import and synchronize metadata across master nodes.
3. Finally, the aggregation will be done on shared-nothing data, and then you can validate it easily.
After a thorough study and practically using it’s various aspects, you will be able to build a distributed Hyper MPP database cluster that supports horizontal and vertical scaling, ingestions and allotting queries between servers in a Kubernetes cluster. There is a single limitation of custom SQL based data source as Postgres that it drives back on partition tables.
So, if you want to leverage more benefits for your business by using tableau software, study and solve more practical use-cases. Join ExistBI’s Tableau Bootcamp to gain knowledge and hands-on experience to fulfill your company’s needs.
SAP has joined forces with the supermodel and superpower Karlie Kloss in support of women in the technology industry. Entrepreneur Karlie Kloss has established a non-profit company called STEAM (science, technology, engineering, arts, and math). Her mission is to support the next generation of innovative women entering the workplace. SAP wants to help young women access Karlie’s ‘Kode with Kloss’ project that provides training and experience within the technology used in businesses today. They plan to raise awareness of the benefits of SAP BusinessObjects training within the STEAM community.
With some studies quoting only 13% of the data developers being female, there is a common goal within the profession to improve this. You don’t have to look far to see this innovative being adopted to improve the industry’s diversity. Stanford University has held an annual Women in Data Science (WiDS) conference since 2014.
This conference includes a Datathon to challenge and showcase skills. WiDS now supports over 150 regional events worldwide. The project has also recently started an exciting podcast with talks from leading women in the big data and data analytics profession. Similarly, in the United Kingdom, there are a Women in Data organization run by a former Managing Director in Strategic Analytics for Barclaycard Europe. She too holds an annual conference, with workshops being fully booked within thirty minutes of release.
With the rapid growth of the technology industry, there is a pleather of jobs available to both men and women. Yet statistics show an actual decline in the number of women in the data science environment. Some, have attributed this to lack of early exposure to the subject, workplace environment culture, inherited bias and sense of isolation.
If you would like to encourage your female employees to purpose the data science or if you yourself would like to know more, contact our enthusiastic team of expert trainers. Below I have also included some helpful and inspiring resources;
If you’re in the market for a new job, you know how competitive today’s job search can be. Not only is it extremely competitive when applying for roles, but job openings also seem to outnumber qualified applicants. According to Forbes, “the existing talent shortage will reach its worst levels in 2030 when an expected 85.2 million job openings will go unfilled worldwide.”
It’s a race to see who can apply the fastest, set themself apart from other job seekers, and wow recruiters with a CV, LinkedIn profile, or portfolio.
One cause of the talent shortage can be attributed to the need for technical skills like data analytics. Harvard Business School reports the business and society potential created by big data is “disrupting a wide range of roles, from engineering to functional analysts to executives.” Across many organizations, functions, and industries, people will need to develop their data skills.
What do US campus recruiters want to see?
We asked the campus recruiter at Tableau about her point of view on skills that current or returning students should be acquiring to boost their professional profiles.
“We view data skills as more of a mindset than anything. Regardless of the information you’re analysing, we see someone with this skill set as naturally curious and passionate about solving problems. Whether you’re looking to solve a critical issue or you’re more interested in personal data, data analysis skills are extremely transferrable.”
Data skills are important for anyone starting their career. When LinkedIn reported hard and soft skills companies to need most in 2019, “Analytical Reasoning” was ranked number 3 under hard skills. Digging deeper into this conversation, I asked Kari what makes a strong candidate and she listed a few examples of skills that demonstrate competency:
Transferrable skills like data skills! Recruiters like to see projects that demonstrate leadership, flexibility, and humility. Show us how you’ve applied data to make decisions.
The ability to code in Java, C, C#, C++, Ruby, and other languages, as well as showing proven success and ability to meet deadlines with a project.
Experience working with customers. Seeing that you have past success in customer-facing roles, possess technical aptitude with tools, and are goal-oriented tells us you’re an applicant to consider.
Whether you’re still in school or a recent graduate, it’s never too late to pick up data skills, learn how to code, or gain experience working with customers – all things that make you a more compelling candidate in the job search.
Hear from students – the Data Generation
One new Miami University graduate, Buchi Okafor, had a passion for sports and landed a finance internship at Under Armour where he first learned Tableau. Buchi quickly learned that “whatever job you’re doing you’ll be looking at data. The people that separate themselves from the pack are those who can gain insights from data pretty quickly and share their insights with others in a way their business partners can understand.” When a new analytics team at Under Armour formed, Buchi decided to focus on his love for data and now works as an analyst for the global pricing strategy and analytics team.
Another new grad, Harpreet Ghuman, began his data skills journey unconventionally. He saw a Game of Thrones visualization in Tableau Public which intrigued him, leading him to teach himself Tableau. At the time, Harpreet was in a master’s program at the University of Maryland for business administration and management.
Harpreet said, “Once I started making visualizations with Tableau, it didn’t matter to people that I didn’t have a background in data. Visualization, like curiosity, is a skill you can translate.” With his newfound love of data analytics, Harpreet decided to also pursue a master of science degree in marketing analytics. Now, he has his dream job as a senior consultant in data analytics at EY, putting his Tableau skills to use with his job’s focus on data visualization.
Systems administrators have a complex job today, as companies adopt multiple business intelligence platforms and cloud solutions. The admin staff has to manage upgrades, new content, patches, fixes, and security developments. This includes the impact of these changes on existing data, dashboards, and reports. This requires a significant amount of time, skills, and manpower. Any undetected effects on the data can lead to the poor performance of the software, inaccuracy of the data, and reliability of data-guided solutions throughout the organization. This will all reflect negatively on the systems administrators.
Typically, the systems administrator would review data for inconsistencies following fixes manually. This involves selecting a subset of data and reports and checking their accuracy. However, this would not be realistic on large-scale upgrades therefore, automation is critical to ensure the long-term success of the applications. Here are our tips to support the automated maintenance process;
Ensure you’re testing realistic use of the application. It is important to test the application following an update in the way the customer or user would require it to function to meet their everyday needs.
Replicate the user’s environment. It is important not to affect the current live working environment. Therefore, the tests need to be run a replica of the company’s live system. This step is easier with a cloud platform, as a replica environment is easier to develop.
When creating an automated testing system we would recommend using a platform approach rather than disjointed separate scripts. This will allow you to test multiple scenarios at various scales within the application.
Examine your test results to a granular level. These tests should not purely result in a pass or fail, all detailed statistics and data should be gathered and examined to help predict future potential issues and to improve the automated testing practice.
Store results in an optimized format, such as a data warehouse. This allows easy analysis, enables the performance of the application to be reviewed and historical analysis to be monitored.
This is all time consuming and challenging tasks for the systems administrator, who is already in high demand. MicroStrategy has developed a testing platform that can be used to analyze over 20 different customer applications. They even use this single platform to test their systems before releasing updates, in an attempt to minimize disruption to the data. For more information on managing application upgrades and developments contact our team of experienced business intelligence consultants.
This May the annual Data Science Conference was held in Boston. This conference is unique as it’s developed by professionals for professionals, there are no sales pitches, booths or paid advertising. The mission of the conference is to allow data science practitioners to interact as scientists. This year saw speakers like; Alessandro Panella, Data Science Manager at Facebook, David Harkness, Data Science and Predictive analytics from National Geographic to Yoni Halpern, Software Engineer at Google. Subjects discussed ranged from the demanding forecast for the future of data analytics to automated index management.
Robert Grossman, Analytic Strategy Partners and Jim and Karen Frank Director, Center for Translational Data Science from University of Chicago gave an essential talk covering the management of data analytics projects in the work environment. This addressed the management and integration of a new analytics model throughout different departments, to then be integrated into services and operations. This is an aspect of all our training and consulting programs that we ensure we cover. With a consistent approach to data management throughout the company you are guaranteed to get the most from your business intelligence investment.
As with Grossman’s talk we would like to use a case study to illustrate the importance of this point. In a recent Salesforce consulting project, we were engaged to not only manage workflow but also, staff enablement and integration. This was a challenging assignment for our certified Salesforce consultants who had to first assess the current landscape of the Salesforce platform integration by the in-house team. In this initial assessment gaps in knowledge, strategy and implementation were identified. This required careful but concise management by our team of experience professionals to ensure the client’s CRM initiative was a success. The client also benefited from our data warehouse consulting services, involving the design and architecture development for new data warehouse capabilities using Microsoft Power BI. Our team of data specialist worked seamlessly with all members of the company from data analyst to CIO. This open and clear communication ensured everyone is now working with a common goal and the outcome has seen a benefit to customer service and increase in sales.
It is important to recognize the importance of ensuring a universal company ethos of data driven decision-making. This should start from adequate training of your chosen platform, supported integration and frequent report analysis throughout the company. These are some of the key points raised in Boston at the Data Science conference, and we couldn’t agree more. To discuss your company’s data analytics requirements, challenges and staff integration needs contact our team.
Last month we saw the launch of the SAP Partner big data podcasts, ‘From Coffee To Cloud’. The first episode was a relaxed twenty-five minute chat with guests Ekaterina Kruse, the Technical Partner Manager, and Henning Heitkoetter, a Product Owner. The main topic discussed was the SAP Cloud SDK. Ekaterina explained how SAP Cloud SDK provides developers with the tools to develop extensions with more ease.
This Cloud application communicates with other SAP solutions, allowing them to build additional functionality. This differs from previous approaches from SAP as it makes communication more consistent throughout applications. Future posts promise to cover general company updates, tips and tricks, and open discussions with guests on the latest hot topics in the industry. You can hear the whole podcast here.
Podcasts are a great way to stay up to date and inspired, here are six podcasts that we would recommend you check out;
IBM Big Data & Analytics Hub– This weekly podcast covers a broad range of topics related to data and analytics, making it relevant to all in the Business Intelligence services regardless of your company size.
BIFocal – Is hosted by two Microsoft MVPs and offers monthly updates on the business intelligence industry, hosting regular guest speakers, as well as handy tips and tricks.
The 10 Minute Business Analytics Podcast – This weekly podcast knows it’s audience well, in the business world we’re all short on time. They offer around three podcasts a month, lasting around 10 minutes. They update the listener on the business intelligence industry, big data, analytics applications, etc.
Data Stories – This podcast is focused on data visualizations, with an impressive library it posts once a month. Through this, you will gain insight into the best way to tell a story with your data.
Liner Digressions – This weekly podcast covers topics such as; data science, machine learning, and artificial intelligence. The host helps answer common queries and give insight into future technology.
The Digital Analytics Power Hour – This informal podcast posts once a month. With three hosts and the occasional guest they tackle the hottest topics in digital analytics over a few drinks.
Since last year’s Tableau conference in New Orleans, we have been looking forward to the launch of their ‘Ask Data’tool and it has now arrived in Tableau’s latest software 2019.1 updates. This task-based tool is a union of natural language processing and expert data visualization. This new tool expands the accessibility of this platform, allowing any user regardless of their background in data analytics to ask questions of the data to create insightful visualization reports. The tool is navigated via keyboard and mouse however, the company state that they hope to release voice-activated control in the near future.
The great news is, if you already have Tableau, this will automatically update to include the Ask Data tools. This means that all your current database will be natural language enabled. In addition to the natural language functionality, the upgrade introduces; advanced data preparation, the ability to export data visualizations into PowerPoint files, user alert customization and a complete redesign of the mobile interface. The mobile app is available to iOS and Android users. Changes to the app include; improved search, favorites experience and interactive previews offline.
There is an additional cost for a Data Management Add-On for those using Tableau server. This provides access to Prep Conductor, allowing the data prep workflow to be scheduled and monitored. Tableau announced that later this year this Data Management Add-On will expand it’s capabilities to include new cataloging functions to enable the user to search for data across all data sets from a single point.
If you are new to Tableau or want to get more from your current software, contact our team of certified Tableau training experts. We offer a range of Tableau classes including our unique Tableau Bootcamps.
Since its introduction cloud-computing has fast become part of not only businesses every day but, everyone’s day today. So, it really should be no surprise that we are now discussing multi-cloud operations. The question we’re asking today is will these complex operations help or hinder business?
The benefits to multi-cloud strategies are simple; performance optimization, budget-saving, reduce the risk of DDoS attacks and avoiding vendor lockdown, just to name a few. Not every department or business function has the same requirements, therefore, using different cloud platforms allow their various data and application needs to be met. However, if you asked CIO to provide details on internal and external cloud layers used by the company and answer can vary significantly.
For those smaller operations, they may only use two or three cloud providers. Such as Google for its users in the United States and Azure for European users. For larger companies, however, the cloud operations are a complex web of data and services. With interlinking data flow, some even from cloud to cloud.
Alongside, the clear benefits mentioned above there are equivalent challenges. Such as; cost, expertise, resources, complex management, just to name a few. Managing and monitoring these ecosystems is a real challenge for CIOs today. Due to this, we have seen an increase in the adoption of management fabric systems. These management programs span multiple cloud systems to give the user a detailed understanding of their data architecture.
Examples of this are; MapR’s Global Data Fabric and Pivotal Cloud Foundry. I don’t need to tell you what an expense this multi-cloud operations amounts to, let alone the need for a management program to run them all. It is, therefore, important to choose the right cloud providers for your business to generate revenue.
It is clear to see the advantages of this multi-cloud approach however, there must be a disaster recovery plan in place to protect the company. When planning out your cloud operations we suggest visualizing it as a tree, each additional branch being another cloud system. The reason for this is, you must have a contingency plan for the interlinking cloud services that often rely on one cloud source. Everyone had to learn from the Amazon outage incident a couple of years ago, which really impacted on their SaaS services.
With the right fabric management and the right cloud services, many companies have seen a significant return of investment. Companies must also ensure they choose the most efficient app for their multi-cloud environment. Traditional apps can be inflexible and difficult to manage and scale. By utilizing the most appropriate cloud-native app you will ensure the most service-oriented outcome. We would also recommend automating low-level tasks within monitoring and maintenance. By applying a standard automating policy to all cloud services throughout the company, you reduce time spent on maintenance, human oversight and allow for seamless updates.
In conclusion, there are substantial benefits to multi-cloud operations as long as you have the strategies and systems in place to manage and maintain them. If you want to get this competitive advantage let us help you develop a strong architectural map with a well-crafted future management plan. From migration, security, change management and working progresses, our data integration consultants have a wealth of experience in this field.
As we start to see an increase in flexible working and working remotely, we also see an increase in workers using mobile devices to view their data. The data analytics software industry has had to pay attention and provide mobile-friendly presentation and apps.
This is something we have definitely seen in the latest software releases over the past six months. Today’s workforce conduct business from multiple locations at any time. Employees therefore expect mobile capabilities from their software and organizations have to deliver. However, to provide analytics on small devices can be challenging. We are not talking about tablet devices, as these translate across from the original web application effectively.
Now the data visualization reports have to support all size screens from laptops to mobile devices. Vendors are divided by their approach to addressing this issue. Some software’s have invested in applications that are functional on both IOS and Android systems. It is important to note that the majority of these mobile adaptions have had to simplify their interactive functionality due to the small display. This can range from allowing the developer to study the user interaction and leverage this data to guide development to being more user engagement focused.
Therefore, if you have a mobile workforce you may want to choose your data analytics platform based on its mobile capabilities. So, here is a summary of the most popular data analytics software mobile applications for you;
Vizable – This Tableau mobile application allows the user to view and interact with their data. This app provides feature animations to improve the process of analyzing the data.
Itunes rating: 4.5/5 Stars
SAP Roambi – Cloud-based mobile application from the SAP analytics portfolio. This advanced design transforms data from its source to rich interactive visualizations. Dashboards and reports can be published in a similar way to web applications.
Itunes rating: 4.6/5 Stars
Informatica Cloud – This is an iOS app is designed to manage your task flow and give the user the ability to troubleshoot any problems.
Itunes Rating: 4.3/5 Stars
PC-MobiMon – Informatica PowerCenter mobile monitoring brings functionality from the PowerCenter platform to your phone.
Itunes Rating: None available
IBM Cognos Mobile – IBM Cognos mobile app allows the user to view and interact with reports and dashboards.
Itunes Rating: 3/5 Stars
MicroStrategy Mobile – Interactive interface from the MicroStrategy platform. This app provides reporting and analysis capabilities.
Itunes Rating: 5/5 Stars
For more support with your data analytics platform contact our team of experience. We provide training on all the above platforms from Informatica training to Tableau classes. Should you have already adopted one of these data intelligence platforms, yet require support on a complex project or feel you’re not getting the most from your investment, contact our consultants.
The term Customer Relationship Manager(CRM) was first coined in 1995, within just a few years it moved from customer relation focused on being a key component in Enterprise Resource Planning (ERP). In its infancy, CRM tools were enterprise products that quickly evolved to web-based then cloud-hosted platforms. Today, businesses benefit from integrated social media applications and mobile technology. At every step, the common goal remained the same; to use customer data to generate sales.
This March Salesforce celebrate their 20thbirthday. This CRM software was designed by Marc Benioff at just 35. He worked for Oracle at the time but, was also involved in several start-ups. Benioff wanted to create a web-based software that could manage contacts for the sales team. The coding was developed by a three-man software developer team in a one bed apartment in San Francisco. Fast forward 20 years and Salesforce.com has an annual revenue of $13.3 billion and employs over 36,000 people worldwide. This revolutionary cloud based technology is used by a vast variety of industries and throughout companies of any size. Today there is Salesforce Community Cloud that is a digital experience platform. If you’re looking for a CRM that provides a personal experience, increases customer satisfaction and allows you to out-perform your competitors then this is the tool for you.
Here are our top four advantages we believe you will discover from Salesforce. Is this the software for you? Are you getting the most of it in your organization?
1, Organized customer information – The better you understand your customer the more likely you are to increase sales. By using this advanced organizational technology, you can accurately categorize client data for future access by any department. This advanced CRM software also allows you to access this data from any device through any internet connection.
2, Improve customer communications – By allowing customer data to be available across departments, customers don’t have to start from scratch each time they contact the company. Any member of the team that speaks to the customer can instantly be updated from the data and create a positive customer experience. Where we’ve seen this significantly benefit the customer journey is in the instance of a customer problem or complaint. Resolutions are reach quickly and efficiently retaining the loyal customer.
3, Automation completes routine sales tasks – There are so many tasks that require completion prior to completing a sale. Several of which are repetitive in nature; form-filling, data reports, and legal issues. This CRM takes the burden of these tasks and allows the user to focus their energy on closing the sale.
4, Improved quality of analytical data and reports – Not only can this tool generate reports on your data but, the system can be easily integrated with software or plugins. Users dashboards can be personalized with automatically generated reports such as sales goals and performance reports.
Our team of certified CRM professionals offers a broad range of Salesforce consulting support from; project implementation, integration, development, and optimization to ensure you get the most from Salesforce. Get in contact for further details.
In today’s business world, there is a huge demand for experienced and enthusiastic data analysts and developers. So, if you have these sought-after specialists on your staff, how do you keep them? We have some tips on how to retain and motivate these team members to continue to see your business grow.
1, Compensation –Unsurprisingly, the most well-known and effective tool to motivate is compensation. And, while it is unarguably very effective it can bread a mercenary culture within your working environment.
2, Self-Development –What is surprising is how compensation is closely followed by the opportunity to develop. Any IT specialist wants to have access to the latest technology to allow them to do their job as efficiently as possible. They also have a thirst for knowledge and learning that supports continual professional development. Investment in training has been shown to energize and motivate staff significantly.
3, Company Culture –People thrive in positive environments. When you are managing a team of people in a work space it’s so important to create an environment the promotes research and development, creativity and enthusiasm. Technology teams want to feel their value within the company, this can be provided through open communication of organization status. By doing so members of the team can see how their work impacts the greater business.
4, Encourage Risk takers –Creating a creative culture where there is not fear of failure will often lead to unprecedented success. Allow these intelligent minds to explore different processes and technology to find the best benefits for the company. Provide space, autonomy and tools and you will soon see the return of income.
How we can help? We offer a wide range of big data and business intelligence training for all abilities. Our experienced and enthusiastic trainers deliver training onsite or via a live virtual classroom. These classes are either fit for purpose or the official vendor curriculum.
So, come on and develop your staff’s skills while motivating them and even benefiting your business!
Some of our most popular Analytics & Insights courses include: Microsoft Power BI, IBM Cognos BI Analytics Essentials, MicroStrategy Essentials and a variety of Tableau classes including unique 3-day Bootcamps.
As many of us know IBM has produced some of the leading business intelligence and data science tools on the market. Such as IBM Cognos Analytics, IBM Planning Analytics, IBM Watson Studio, etc. All these tools allow the userto gain insight into their data, with impressive visualization and reporting abilities that can be shared throughout an organization. We asked our experienced trainers for a few tips and tricks on two of IBM’s most popular software solutions (IBM Cognos BI).
IBM Cognos Reporting
– Always send published reports as fixed content, that way the data cannot be changed by the recipient. If you send it as a Microsoft document then anyone can open it regardless of whether they have IBM Cognos or not.
-Try and avoid list object embedding within list objects, as this may cause limitations to the target. You can however, import repeaters in objects and tables.
-Aim to keep your tables size to a minimum as anything above set size may not be displayed properly.
-Do not rely on the report context to define the size of your images, define image properties.
IBM Framework Manager
-Don’t mistake this for a ETL tool. This tool is excellent at complex calculations, table joins, managing data security etc but, it will not convert data or improve data quality.
-Make sure you utilize the calculations functionality when doing the same calculations on multiple reports, this creates reports more efficiently.
The majority of Hadoop experts believe an integrated data warehouse (IDW) is simply a huge pile of data. However, data volume has nothing to do with what makes a data warehouse. An IDW is a design pattern, an architecture for an analytics environment. First defined by Barry Devlin in 1988, the architecture quickly was called into question as implementers built huge databases with simple designs as well as small databases with complex designs.
“Subject-oriented” means the IDW is a digital reflection of the business. Subject areas contain tabular data about customers, inventory, financials, sales, suppliers, accounts, etc. The IDW contains many subject areas, each of which is 250 to 5,000 relational tables. Having many subject areas enables cross-organizational analysis – often called the 360-degree view. The IDW can answer thousands of routine, ad hoc, and complex questions.
In contrast, a data mart deploys a small fraction of one or two subject areas (i.e., a few tables). With only a few tables, data marts answer far fewer questions and are poor at handling ad hoc requests from executives.
Integration in a data warehouse has many aspects. First is the standardization of data types. This means account balances contain only valid numbers, date fields have only valid dates, and so on. Integration also means rationalizing data from multiple operational applications. For example, say four corporate applications have Bill Franks, William Franks, W. J. Franks, and Frank Williams all at the same street address. Data-integration tools figure out which is the best data to put in the IDW. Data cleansing corrects messed-up data. For example, repairs are needed when “123 Oak St., Atlanta” is in the street address but the city field is blank. Data integration performs dozens of tasks to improve the quality and validity of the data. Coupled with subject areas, this is called “a single version of the truth.”
Does Hadoop Have What it Takes?
Hadoop was engineered to rely on the schema-on-read approach, in which data is parsed, reformatted, and cleansed at runtime in a manually written program. But Hadoop (and Hive) have limited to no ability to ensure valid dates and numeric account balances. In contrast, relational database management systems (RDBMS) ensure that input records conform to the database design – called the schema. According to Dr. Michael Stonebraker, “This is the best way to keep an application from adding ‘garbage’ to a data set.”
The current rage in the Hadoop community is SQL-on-Hadoop. Those who have committed to open-source Apache are playing catch-up to databases by adding SQL language features. SQL-on-Hadoop offers are a subset of the ANSI 1992 SQL language, meaning they lack features found in SQL 1999, 2003, 2006, 2008, and 2011 standards. Therefore, the business user’s ability to perform self-service reporting and analytics is throttled. This, in turn, throws a substantial labor cost back into IT to develop reports in Java.
Additionally, the lack of a database foundation also prevents SQL-on-Hadoop from achieving fast performance. Missing from Hadoop are robust indexing strategies, in-database operators, advanced memory management, concurrency, and dynamic workload management.
A consistent – sometimes angry – complaint from Hadoop experts is the poor performance in large table joins, which the SQL-on-Hadoop tools do not fix. Remember those subject areas above? Some subject areas have two to 10 tables in the 50-1,000 terabyte range. With a mature analytic database, it is a challenging problem to optimize queries that combine 50TB with 500TB, sort it, and do it fast. Fortunately, RDBMS vendors have been innovating the RDBMS and cost-based optimizers since the 1980s. A few Apache Hadoop committers are currently reinventing this wheel, intending to release a fledgling optimizer later in 2014. Again, self-service business user query and reporting suffers.
Hadoop, therefore, does not have what it takes to be a data warehouse. It is, however, nipping at the heels of data marts.
How Many Warehouses Has Hadoop Replaced?
As far as we know, Hadoop has never replaced a data warehouse, although I’ve witnessed a few failed attempts. Instead, Hadoop has been able to peel off a few workloads from an IDW. Migrating low-value data and workloads to Hadoop is not widespread, but neither is it rare.
One workload often offloaded is extract-transform-load (ETL). Technically, Hadoop is not an ETL solution. It’s a middleware infrastructure for parallelism. Hadoop requires hand coding of ETL transformations, which is expensive, especially when maintenance costs pile up in the years to come. Simple RDBMS tasks like referential integrity checks and match key lookup don’t exist in Hadoop or Hive. Hadoop does not provide typical ETL subsystem features out-of-the-box, such as:
* hundreds of built-in data-type conversions, transformers, look-up matching, and aggregations
* Robust metadata, data lineage, and data modeling capabilities
* Data quality and profiling subsystems
* Workflow management, i.e., a GUI for generating ETL scripts and handling errors
* Fine grained, role-based security
Because migrations often come with million-dollar price tags, there is not a stampede of ETL migrations to Hadoop. Many organizations keep the low-value ETL workload in the IDW because:
* The IDW works (it ain’t broke, don’t fix it)
* Years of business logic must be recoded, debugged, and vetted in Hadoop (risk)
* There are higher business value Hadoop projects to be implemented (ROI)
Nevertheless, some ETL workload migrations are justifiable. When they occur, the IDW resources freed up are quickly consumed by business users.
Similarly, Hadoop provides a parallel platform for analytics, but it does not provide the analytics. Hadoop downloads do not include report development tools, dashboards, OLAP cubes, hundreds of statistical functions, time series analysis, predictive analytics, optimization, and other analytics. These must be hand coded or acquired elsewhere and integrated into projects.
Hadoop Was Never Free
Where does this leave the cash-strapped CIO who is still under pressure? According to Phil Russom of The Data Warehousing Institute: “Hadoop is not free, as many people have mistakenly said about it. A number of Hadoop users speaking at recent TDWI conferences have explained that Hadoop incurs substantial payroll costs due to its intensive hand coding normally done by high-payroll personnel.”
This reflects the general agreement in the industry, which is that Hadoop is far from free. The $1,000/terabyte hardware costs are hype to begin with, and traditional vendors are closing in on Hadoop’s hardware price advantage anyway. Additionally, some SQL-on-Hadoop offerings are separately priced as open source vendors seek revenue. If you want Hadoop to be fast and functional, well, that part is moving away from free and toward becoming a proprietary, priced database.
Hadoop Jumps in the Lake
Mark Madsen, President of Third Nature, gives some direction on Hadoop benefits: “Some of the workloads, particularly when large data volumes are involved, require new storage layers in the data architecture and new processing engines. These are the problems Hadoop and alternate processing engines are equipped to solve.”
Hadoop defines a new market, called the data lake. Data lake workloads include the following:
Many data centers have 50 million to 150 million files. Organizing this into a cohesive infrastructure, knowing where everything is, its age, its value, and its upstream/downstream uses is a formidable task. The data lake concept is uniquely situated to solve this.
Hadoop can run parallel queries over flat files. This allows it to do basic operational reporting on data in its original form.
Hadoop excels as an archival subsystem. Using low-cost disk storage, Hadoop can compress and hold onto data in its raw form for decades. This avoids the problem of crumbling magnetic tapes and current software versions that can’t read the tape they produced eight years earlier. A close cousin to archival is backup-to-disk. Again, magnetic tape is the competitor.
Hadoop is ideal for temporary data that will be used for a month or two then discarded. There are many urgent projects that need data for a short time then never again. Using Hadoop avoids the lengthy process of getting data through committees into the data warehouse.
Hadoop, most notably YARN from Hortonworks, is providing the first cluster operating system. This is amazing stuff. YARN improves Hadoop cluster management but does not change Hadoop’s position vis-à-vis the data warehouse.
Apples and Oranges
Bob Page, the VP of Development at Hortonworks, weighed in on the Hadoop versus IDW debate: “We don’t see anybody today trying to build an IDW with Hadoop. This is a capability issue, not a cost issue. Hadoop is not an IDW. Hadoop is not a database. Comparing these two for an IDW workload is comparing apples to oranges. I don’t know anybody who would try to build an IDW in Hadoop. There are many elements of the IDW on the technical side that are well refined and have been for 25 years. Things like workload management, the way concurrency works, and the way security works – there are many different aspects of a modern IDW that you are not going to see in Hadoop today. I would not see these two as equivalent.”
Hadoop’s success won’t come as a low-priced imitation of a data warehouse. Instead, I continue to be bullish on Hadoop as we witness the birth of the data lake with predictable birthing pains. Over the next couple of years, the hype will quiet down and we can get to work exploiting the best Hadoop has to offer.
Not sure if you need a BI consultant? What does a business intelligence consultant do and do you really need one? The process of gathering, analyzing, and presenting visual data reports is now central to nearly every organization and company. With a plethora of data visualization tools available to make managing data easier, the benefits to understanding your data are now accessible to all. So, if these next-generation platforms are so fantastic why do you need a consultant? Firstly, you need an understanding of where this data comes from, therefore support from an experienced statistician is essential. Then comes the visualization tool, as mentioned there are multiple tools on the market and it’s important to pick the appropriate one for your business.
A specialist consultant can help you navigate this investment. They can then be utilized to train your team on data preparation, data analysis, and innovative visualizations. The training needs to be thorough to give delegates the skills and confidence to get the most from their chosen tool. However, a good consultant will then offer on-going support following the training as the delegate applies their new skills to their own data set.
When a BI consultant really comes into their own, is with big, complex projects. Such as; data strategy and assessment, project implementation, data warehouse consulting or data integration consulting. These assignments see substantial benefit to the customer and their business. So, whether it’s big data consulting, cloud adoption or technology specific consultingyou require contact our expert team. Don’t take our word for it, let our customers tell you;
‘Exist Business Intelligence have delivered a number of Informatica PowerCenter and MicroStrategy trainings for our global BI teams. Due to the success of the trainings, we procured ExistBI to help with various Informatica Consulting and MicroStrategy engagements, helping a number of teams with Informatica installs, upgrades, configuration, high-level bug fixes and various deployments. Our team have been extremely satisfied!’ Senior Project Manager – Atos
‘Thank you for providing us sharp SAP BW / ECC and Informatica Consulting resources for our enterprise data warehouse project here in Idaho. They have succeeded where numerous other resources have failed! Thanks again!’ Senior Project Manager – Idaho Power
‘We have implemented our key reports in production and are going fully live with MicroStrategy on Monday morning. Your MicroStrategy Consultants Nic, Aleix and Eric have been a great asset to the project – we couldn’t have done it without them!’ Senior IT Manager – Riverstone
Yet again we see an example of how sports data analytics platforms are being used in a wide variety of industries, with significant impact. The US Tennis Association are using IBM Watson analytics to gain advantage on the court. The tool is used to help improve training sessions and match performances. Watson is used to capture data filmed from training and matches to provide insight into opponent’s habits to be exploited, tendencies for fatigue to be avoided etc.
It is now common place for data analysts to be employed full-time in the National Basketball Association (NBA) to analyze performance, tactics, habits and even fatigue. These teams have been shown to improve scores and reduce occurrence of injuries. The Golden State Warriors were the first teams in the NBA to embrace data science. This team have been leading the championship for years. They first adopted SportsUV cameras, high speed cameras that take 25 frames a second. These cameras were used to utilize the Estimated Possession Value (EPV), enabling the use of a statistical tool to track all the players on the court to predict the probability to score.
The National Football League (NFL) are even using data analytics to monitor and maintain players health. With teams wearing electronic health recording equipment on the pitch, that can be monitored pitch-side via a tablet device. In a culture where the detrimental risks of concussion are high, this technology could be lifesaving.
Data is not just being used to improve players ability to win but also the fans experience. Rugby Football Union (RFU) have adopted IBM software to analyze their multiple streams of data, in an aim to improve the customer experience and encourage more people to enjoy the sport. The union’s aim is to engage and inspire new audiences and deepen their relationships with their volunteers and fans. Firstly, they have seen a substantial increase in open rate from their emails to fans. From their data, they are now able to send information that is more relevant to the individual customer and not inundate them with unsuitable emails. The platform has allowed them to provide fans with information related to their visit to the stadium on match days, travel disruptions, weather, events in and around the game. The IBM Watson software allows the marketing team to analyze their success rate, RFU has seen their reach double since the use of IBM CRM. Executives are extremely happy to see marketing cost decrease and engagement and tickets sales increase since implementing the software. In a recent blog, we also discussed how the San Francisco 49ers used SAP Business Objects in a similar way to nurture love of the game and improve the fans experience.
ExistBI is here to help your company choose and implement the most appropriate AI, Big Data and Business Intelligence solution by offering strategic consulting, project Implementation and support. We also offer software enablement from Informatica, Tableau and IBM Cognos training. Contact us for more information.
In today’s fast paced business intelligence world, organizations have to place continual professional development as a priority. For companies to get the most from their BI tools their must ensure their team are well trained and up to date with upgrades and developments in the specialty. If a skill gap is allowed to form, this prohibits the organization from earning the rewards of their digital investment. The approach towards data management and the team of specialist that analyze and report on this insightful information has to change. The business leaders have to change their mindset, enable skillset development and provide the most appropriate tools and solutions.
Help the data tell a story
Business executives are now asking data analysts to answer their company’s questions by telling a story from the data. Companies such as Narrative Science and Automated Insights are fast creating software to generate a story from the data provided. These story-telling skills should not be underestimated, well trained analysts who provide a clear and visually stimulating story can ensure investment in the most appropriate area to guarantee return of investment.
Skills beyond the office four walls
For many years now the importance of a company’s data has been increasingly acknowledged. However, in recent years the required skills of the data engineer have rocketed. The specialist has to analyze, report, manage data on site, on private cloud space, public cloud and across eco-system partners. This adds the additional element of privacy and security supervision. These staff members have to become digital architects, allowing access, protecting relevant content and ensuring a suitable user interface design.
New Age IT team
Gone are the days the IT team were never seen and had to be emailed for any data-related queries. The data analysts are now a critical part of the daily workings of any successful business. They are at any important meeting, involved in decision-making and any changes to operations. This role demands approachable skills that foster a learning environment for all company members to understand and utilize the data they generate. They are visual, inspirational and keen teachers of the skills and knowledge they possess.
How can you find an employee with all these skills or how can you keep your current team skilled up? Let us help, ExistBI has over 10 years of experience in Business Intelligence training and consulting. We offer training on-site or virtually through live instructor-led classes. Our global team offer 100’s of classes, including: IBM Cognos, Informatica PowerCenter training, SAP Business Objects and unique Tableau classes just to name a few. Our team of BI consultants solve our customers toughest problems through data warehousing consulting, Informatica consulting to Tableau consulting. For more information on how we can support you and your team to get the most out of your data contact us.
How data can save lives? AI technology in healthcare is now being applied to patients to diagnose diseases well before symptoms even begin. The well-versed term ‘prevention is better than cure’ may finally come to realization. Technology is being developed to track potential health conditions in at risk populations. With the use of AI a patient’s heart rate, breathing, the way they move and much more can be analyzed to identify health issues, health professionals can then offer health promotional lifestyle advice to allay or prevent the illness. An incredible example of this is from researches at Google who applied AI algorithms to retina scans of almost 300,000 patients and discovered a predictor for cardiovascular disease. Their findings suggest that regular eye scans can provide indications for risk of heart attacks, allowing professionals to intervene and offer preventative medication and lifestyle changes.
Dina Katabi, a Professor of Electrical Engineering and Computer Science at MIT and the director of the MIT Wireless Center goes that step further. Her technology sits in the walls of your home and transmits low-level wireless signals that reflect off a person’s body returning vast amounts of data back. This data is said to provide cues for future illness before the person even knows themselves. Katabi suggests that such technology is essential to support our aging population, often living alone, currently placing a significant demand on emergency medicine services.
A Company called Face2Gene also utilizes deep learning AI technology to predict rare genetic disorders based on a person’s face shape. FDNA have created an app that has learned to identify facial hallmarks of conditions often missed by doctors. This early diagnosis of these often-misdiagnosed genetic syndromes can ensure correct treatment is provided and spares families unnecessary testing and interventions.
Ben Franc a radiologist at Stanford University has been using AI techniques in a recent study using PET scans to analyze brain metabolism. Current results from the study have diagnosed Alzheimer’s six years before a human doctor. Franc explains that computer algorithms can find associations in patient data that would take medics a lifetime to find. This exposure to millions of patient’s information allows early diagnosis and timely treatment. His team have also been using radiomics to identify breast cancer using PET and MRI scans. This raw data allows the essential early diagnosis and has led to multiple cases of recurrence-free survivors.
Need I say more? If the medical world embraces the significant impact of AI technology on their ability to protect and care for their patients, the annual check-up could be obsolete. The real question is, how much trust can we put into a computer algorithm and do we need the bedside manner of a human doctor? With 91% of Face2Gene facial recognition software being correct and AI technology being trained to respond to human behavior, these will soon be worries of the past!
ExistBI provide AI, Machine Learning, Big Data Analytics and Enterprise Cloud Data Warehousing consulting, support and training services. They have a passionate and experienced team of strategic consultant and data driven implementation specialists who provide solutions and services to customers in the US, Canada, UK and Europe.
This new data management tool from SAP enables the user to analyze large volumes of data from multiple different sources and systems. The software does not require the data to be pulled, centralizing only the processing not the storage. Although, others have coined the ‘Sap Data Hub’ term before SAP are taking an alternative approach by leaving the required data in situ rather that importing it all as with Cloudera and MapR. They also boast being able to draw from a wide variety of sources such as; CSV files, cloud services and web API services creating a data pipelines for the data to flow through. The software has replaced the traditional workflow scheduling system with a graphical tool to orchestrate jobs, restart and roll back on tasks when needed. This system accelerates the user’s ability to deliver intelligent data with metadata management strategies that improve data visualizations and data consumption. The system delivers trustworthy enriched and refined data, on time to the right user.
This all sounds so streamline and efficient but, ‘my company don’t use SAP’ I hear you say. No problem. The SAP Data Hub is compatible to be integrated with third-party ETL processing products. The only things you will have to purchase at a small additional cost is SAP’s in-memory database engine HANA. For those companies built of SAP then they can use their existing HANA capacity.
Want to know more about SAP products, SAP Business Intelligence consulting or SAP Business Objects training, then contact our SAP team today. Our SAP BI consulting team can assist with all your data strategy and implementation needs. In addition, our certified SAP BI training team offer classic or fit for purpose SAP BusinessObjects training classes onsite or via a virtual licensed environment. All training consists of at least 50% practical hands-on labs to practice your acquired skills. Our experienced team have over 20 years business intelligence and big data experience to help with any challenges you face in the competitive information economy.
In the big data world, artificial intelligence functions are quickly integrating into our working day. Now we can even see it protecting the data we store and analyze. In the industry of cloud computing there has already been two incidents where AI has identified a hacker within a larger retailers’ cloud space, the company was notified and the attack was stopped. This is an example of how the traditional tactics of detecting an intruder are no longer sufficient. With AI you can adapt the algorithms to the hackers continually evolving strategies. Machine-learning technology designs can process massive volumes of data related to logins, behavior and attack history and hunt out a hacker.
This year we are seeing the big companies adopt such techniques, such as; Microsoft Azure, Google and Amazon. This technology will hopefully tip the balance in our favor. Microsoft pride themselves on their new customized security that has moved away from the blunt generic approach that limited user flexibility and developed into understanding the client’s typical behavior to highlight suspicious activity. Google security software continues to analyze behavioral data past the log in phase and through the user session, tracking for intruders.
Machine learning security systems do not always work, as we know are enemy is an ever-moving target. However, we need to take into account which software and cloud enterprise has the most advanced technology to protect our data. This is where ExistBI can help, we offer Data Science, Big Data and Data Warehouse Consulting solutions and servicesThe company has recently established a division focused solely on infrastructure consulting services to support cloud adoption.
We have a passionate and experienced team of strategic data driven specialists who provide consulting and training around the globe.Let us help you choose the most appropriate software and storage to meet your businesses data needs.
In 2018, we saw a significant shift in the utilization of data analytics and data warehousing environments throughout all industries. Companies are now becoming data-driven and are confident to store their data in the cloud. This has led to a substantial increase in demand for business intelligence platforms, data management and storage solutions. This will require data warehousing to modernize their approach to keep up with the trends of 2019. ExistBI is already ahead of the game to ensure all our customers are too, here are our predictions on the use of data analytics and warehousing in 2019;
Data Lakes may replace data warehousing –
With the number of businesses using big data implementations tripling in the last few years, this approach has become mainstream. The most commonly used optimization is data warehousing. This practice of big data architecture may soon evolve into the use of a data lake. Within each company there are now extending the use of business intelligence throughout the enterprise. This results in large volumes of data; often live streaming data being dealt with. These common situations suit big data architectures.
Streaming data analytics –
When choosing a BI solution companies are looking at data streaming analytics as a priority. This is the ability to stream significant volumes of data and analyze it in motion. From this data businesses can improve operations and customer service considerably, minimizing loss.
Natural language analysis –
Today people are familiar with voice activation such as Siri, Google home and Alexa. It is only natural to expect the same of your analytics platforms. BI solutions have fast been releasing natural language analytics functions and this demand is set to increase. This is another step forward to improve accessibility to data analytics for anyone in an organization.
Cloud enterprises –
As the volumes of data increases, as does the need for storage. Businesses are now far more trusting of public cloud services and see them as an attractive, safe and cost-effective solution. Today over 50% of organizations planning to or utilize a cloud solutions. This year we will see infrastructure consultant services in great demand.
Need help choosing a BI platform or cloud solution that best suits your business or training on an analytics platform ExistBI are you here to assist you!
Looking for reasons to have Cognos training and want to know what’s new with their IBM Cognos Training Version 11.1? Cognos is IBM’s web-based business intelligence software solution. The aim of this BI suite is to allow people with little IT knowledge to prep analyze and report on company data. We are here to give you a summary of the finest features in their new software. Although there has been much excitement about the artificial intelligence properties incorporated within this new software, there is far more that we can’t wait to tell you about.
– Administrators can now have more options to customize their screens profiles and palettes. It has been made easier to change system settings to avoid the need for complex cryptic codes etc. Your distribution list is now easy to access and manage, with contacts being grouped with accounts.
– Improvements to the reporting tool, with really useful changes. Such as, the ability to dock the floating context bar, so it no longer gets in the way. Frequently used settings and tools that were previously buried deeply are now available on your screen at all times through the gear icon.
– Growth of the data modules functionalityimpacting data preparation and tables etc.
– Significant advances in data explorationthrough data profiling and the use of natural language to analyze the data.
Get in contact with our skilled and experienced team of certified Cognos trainers and consultants to discuss your upgrade path. Our team offer class based or virtual IBM Cognos training in the US, Canada, UK and Europe. We offer official Cognos training, Bootcamps or fit for purpose curriculum to meet the needs of your organization. The consulting department architect, design and implement industry-focused, integrated solutions and services leveraging IBM software.
In this data driven world it feels like the traditional Customer Relationship Management (CRM) tools are inefficient and unreliable. Now predictive analytics and even prescriptive analytics are what the sales and customer service teams need. When picking a BI platform for your business you need to ensure it has;
Dashboard visualizations ensure you can see key performance indicators across the organization, allowing for greater analysis. This enables decision makers to quickly see trends forming that can impact on customer interactions. By analyzing this data, managers can forecast potential negative outcomes to implement preventative customer service tactics.
The ability to drill down into the data
Pick a platform that facilitates deeper analysis on your data, allowing all areas of the customer services process to be reviewed. The tool should help measure industry sentiment, geographic and product based trends and identify areas where feedback levels could be improved.
Measurements of customer fulfillment
Your tool must have the ability to gather data related to delivery times and customer fulfillment. This allows you to keep track of any delays in delivery leading to reduce rates in returning customers etc. Gathering data on customer satisfaction and fulfillment after receiving a product or service is crucial for business growth. By using a BI platform, you can identify patterns in the data.
SAP have come up with a solution for proactive customer service; SAP Customer Experience Suite, SAP C/4HANA. This platform collaborates customer information to predict the customer’s needs, maybe even before that are aware of them. This cloud-based solution shares customer information within a business at one time. This tool can be run alongside existing marketing, sales, service systems to provide the sales staff with detailed information on their customer and the customer journey.
New Orleans recently hosted Tableau’s annual conference, which saw the release of an impressive array of innovative tools using natural language. They unveiled ‘Ask Data’, this allows the user to use common language to ask questions about their data. This expands the accessibility of the data analytics tool to everyone in the company, not just the IT department. Tableau have also launched an add on tool to resolve the issue of the vast task of data preparation. Tableau Prep Conductorallows self-service large scale data preparation, to free up more time for data analysis. Their aim is to allow the user to interact naturally with the data, regardless of data structure knowledge etc. The Ask Data program has a specialize algorithm that understands the user’s intent, predicts needs and provides smart visualizations.
Alongside, these advanced tools Tableau introduced new data modeling capabilities within their software. This again, helps the user analyze more complex data without having an understanding of complex written coding or calculations. Customers can simple use a drag and drop function and the software will build relationships within the data using common data warehouse standards like star and snowflake schemas.
Tableau also announced a new partnership with Amazon Web Services (AWS) to support customers in the adoption of cloud based services. AWS cloud services allow large scalability, flexibility and reliability combined with Tableau’s intuitive visualization tool providing a modern approach to data analytics.
Next to be revealed was the new Tableau online development sandbox, that is free for all developers and members. This unique environment provides access to sample codes, specialist engineers and developer forum support. This program offers more access and support for developers to build powerful solutions, dashboard extensions, custom data connectors and more.
Finally, Tableau have developed Desktop Specialist Exam, to provide the successful candidate with a recognized certification. Tableau is acknowledging the increasingly valuable need for data skills in today’s business environment. Forbes cited Tableau as one of the 10 most valuable technical skills sets to have from their Glassdoor survey.
As you can see, Tableau have a lot to offer in the coming year. ExistBI are here to help with a seamless integration of their software tools into your business operations. We offer certified Tableau training on-site or via virtual live instructor-led classes. Alongside, Tableau consulting providing dashboard development and support. Contact one of our skilled and experienced specialist for further details. and Tableau consulting.
SAP BusinessObjects have been procured by the San Francisco 49ers in what they are calling the ‘Executive Huddle’. This notorious American football team are going to use this real-time data analytics and data visualization tool to improve efficiency within the Levi’s Stadium. The main aim from the 49ers is to improve the fans experience. The organization plan to fully utilize the real-time properties of this software to make more informed decision on the day of play, rather than a day later. The platform is able to take data from a variety of sources e.g. parking, ticketing, retail, social media and more. This information can then be analyzed by the Executive Huddle team and actions can be taken immediately to improve the fans experience and the stadium operations. In the early roll out of the Executive Huddle the data analyzed the way fans enter the stadium, the team could then relocate resources and reduce waiting times.
The use of data analytics in the sports and entertainment industry has now become common place. We have seen a huge increase in demand for our training and consulting services in the field of business intelligence, big data and predictive analytics. Every business and organization now recognizes the benefits for growth and profitability from understanding it’s data. We offer SAP Business Objects training and consulting, from our skilled and experience team. Our team can help you navigate the SAP Analytics Cloud and the SAP HANA database to gain the most from the platform.
Our data specialists provide Industry specific consulting, project implementation, training and support services. Our trainers are certified and experienced in BI & Data Science technologies including: Big Data & Hadoop, Informatica, Tableau, SAP BI & BusinessObjects, IBM Information Management & Cognos, Microsoft and MicroStrategy just to name a few.
With a New Year comes new analytics and business intelligence trends. 2019 is set to see the further development of predictive analytics technology, data discovery and quality management techniques, Artificial Intelligence and much more. Leaders in the field expect that businesses of all sizes will be onboarding a form of business intelligence solution, the only question left now is which one? Here are a few highlights of the BI trends in the year ahead;
1, Time to maximize the returns on investment in BI
The importance of Data Quality Management has become crucial to be implemented company-wide to gain a competitive advantage. A strong trend from this year that is set to continue is the impact of data discovery. This requires a greater understanding of data preparation and advanced analytics. Data discovery brings business value.
2, The new world of AI
The world of Artificial Intelligence and Machine Learning is revolutionary and steadfast. This has completely changed the way companies receive and analysis data from static to live dashboards. Year 2019 will see businesses being for more data-driven from proactive live analytics.
3, The future is Prescriptive analytics
Predictive analytics was a huge trend in 2018, in 2019 we go a step further with Prescriptive analytics. This analyzes data to determine decisions to make and actions to be taken to achieve a particular goal.
4, Shared Business Intelligence
BI tools emerging on the market for 2019 have a common goal, collaborative business intelligence. It is predicted that next year will see BI become more connected to larger systems and greater sets of users than ever before, allowing decision-making processes to thrive.
5, The most important job in the company
2019 will see an increase in Chief Data Officers/CDO and Chief Analytics Officers/CAO. It is safe to say that in the last year every industry has now acknowledged that data management and analytics is at the core of business growth. This has led to the emergence of new powerfully influential roles emerging in the data profession.
ExistBI are ready for the demands of next year’s trends and are well equipped to train and support their customers through their toughest challenges in 2019. Allow us to help you pick the most appropriate BI platform for your business with the support of our experienced consulting and training staff.
Our data specialists provide Industry specific consulting, project implementation, training and support services. Our trainers are certified and experienced in BI & Data Science technologies including: Big Data & Hadoop, Informatica classes, Tableau training, SAP BI & BusinessObjects, IBM Information Management & Cognos, Microsoft and MicroStrategy just to name a few.
The Digital Change Survey conducted by the enterprise application software firm IFS found that 34 percent of companies felt unprepared to deal with digital transformation, the main reason given was lack of knowledgeable and experienced staff. When asked further the survey found that as high as 40% of companies believed the main area they would expect deficiencies would be ‘business intelligence’. The other main area of concern was cyber security. This survey highlighted a skills gap of concern for many industries working towards the digital transformation. We all now know the excellent return on investment from digital technology such as, big data, analytics, the internet of things (IoT) for our businesses. There is however, several obstacles to overcome to gain the profitable benefits. Here’s three tips to help your company with a smooth transition;
1, Keep it simple
Antony Bourne, President of Global Industries at IFS advises the key to success in the integration on digital technology into your business is to ‘Keep it simple’. He suggests starting small and using Key Performance Indicators to monitor progress. He believes it’s not appropriate to roll out digital transformation technology throughout the entire business operations immediately. Bourne recommends introductions of new ways of working on a project by project basis to ‘secure small wins and big learning’.
2, There will always be challenges with change
One of the main obstacles in complex journey of digital change was actually found to be a human one, “aversion to change”. The other common concerns were having the absence of organizational policies and procedure and management of cyber security. To address human reluctance to change we must offer support and training to give the users confidence in these new technologies. They will very soon see the benefits to a more efficient way of working.
3, Build your talent
The Digital Change Survey also discovered that over one in three companies believed they did not have the trained staff to manage the introduction of new technologies in their working processes. Bourne suggests that having the right talented staff is vital for a successful transformation. Businesses need to create a clear talent investment plan from, recruitment, consultancy and existing staff training.
How we can help
We have a large team of trainers and consultants that are experts in the field of Digital Transformation. Our talented staff offer; Tableau training, Informatica Classes, SAP BusinessObjects training just to name a few. Our skilled and experienced team can train your employees of the latest data analytics software to get the most out of the data your company generates. The consulting department offer data warehouse consulting, Informatica consulting, tableau consulting, plus many more business intelligence services for data migration, data extraction and big data analytics. To find out more about how we can help support your transition to the opportunities available to you within digital technology contact us.
Let us help you keep up to date with the latest news and insights in the fast-paced world of business intelligence & big data. October 19-21 saw the NASA’s Internal Space Apps Challenge, an impressive 69 countries participated in this 48-hour hackathon. This challenge invites people, regardless of background and skill to tackle a challenge using robotics, data visualization, hardware, design and many other specialties. This year’s six challenge categories were; can you build, help others discover the Earth, Volcanoes, Icebergs and Asteroids, What the world needs now, An Icy glare and a universe of beauty and wonder. We look forward to hearing the results from the award winners.
October also saw an increase in the use of Artificial intelligence in the business intelligence industry. Tableau announced their new ‘Ask Data’ tool in their latest version of the Tableau Software 2019. This function was demonstrated in New Orleans, showing how this automation function works to prepare data. The aim of this AI tool is to use natural language to achieve statisticians work to analyze data. This can be spoken or typed-words. This function follows the launch of Microsoft’s PowerBI tool ‘Ask a question about your data’. Business Intelligence and data analytic tools are fast adopting AI technology to make their software accessible to all. Our team are excited to test out this new function to help support our clients in our Tableau training and Tableau consulting.
Late in the month we saw the launch of North’s Focal Smart Glasses, the company has just rebranded from Thalmic Labs. These are designed for everyday use; each pair has a tiny camera in one arm of the smart glasses that displays information from your phone. They also come with the essential Loop ring, wore on the user’s finger with a joy-stick mechanism that allows them to swipe their display. The glasses work by using retinal projection, allowing the images to shine on the back of the retina so everything will be seen in focus. You can view messages and reply, view calendars and maps and even order an Uber. The glasses have a speaker inside so commands can be given to Alexa. This is a reflection of the investment Amazon has place into the Focal Smart Glasses. To own a pair, you have to be fitted in the North Stores currently located in Brooklyn and Toronto. We have yet to see how these compare to their predecessors attempts at Smart glasses.
In other news, Intel have announced that they have achieved their diversity in the workforce goals two years early. They pledge to have a true representation the US population by 2020, including women, black, Latinx, Asian, LGBTQ+ and Native American workers. Intel have been focused on developing access to educational programs for those in underserved populations. Alongside, investment into internal programs to develop their staff and nature each staffs experience and authentic selves. This is something the company takes great pride in and continues to plan to put central to their working ethos. This is an area within the tech world that requires a great deal of improvement, let’s hope other companies in the industry take notice.
Join us next time as we round up on the business news that will interest you. To keep up to date follow us on Twitter, LinkedIn and Facebook.
Spending all your time prepping your data, with little time left to analyze it? Adam Selipsky, CEO of Tableau talks on Mad Money about the research that suggests that people spend 80% of time cleaning and prepping data and only 20% of the time analyzing it. Tableau has come up with a solution software to aim to reverse these figures, Tableau Prep. The company’s mission is to ‘help people see and understand data’. Adam Selipsky recently joined Tableau from Amazon and is passionate about making data accessible and visually digestible. He explains that this Prep software connects to data on premises and in the cloud. It then combines and cleans the data allow you to take fast advantage of the data available to you. Learn more about Tableau Prep through Tableau https://www.tableau.com/products/prep
Selipsky also discussed the Tableau subscription programs available. These are all set at varied price point to suit each user. Whether you’re a large or small organization, you can purchase individual or enterprise licenses. Tableau has shown undebatable success when introduced into companies from a wide range of industries. From the Texas Rangers, Macys, Princeton University to Barclays Bank. If you are looking for an experienced Certified Tableau trainer, just contact our Tableau training team. ExistBI offer classic and custom Tableau classes complimented by a range of Tableau consulting and support capabilities. We provide unique Tableau Bootcamps for beginners to advanced level users. As part of our Tableau training classes our team also covers the Tableau Prep program, teaching users to be skilled in cleaning and prepping their data for maximum analysis benefit. We provide onsite and live instructor-led virtual training. All delegates receive materials, access to our licensed training environment and work through extensive hands-on labs.
Let us help you keep up-to-date with the latest technology news round up and insights in this fast paced world. This week there is a lot of talk about the impact of data on politics. Did you know that data has overtaken oil as the most valuable resource in the world? We now live in a world where data is more available and easier to understand than ever before and this is forcing politican to be more accountable. Politicians are heading towards a data driven culture as predictive analytics and machine learning algorithms have contributed to some significant changes in policies and procedures. We are beginning to see that the interpretation of data can make or break a politicians campaign. Just look at the impact of the Cambriadge Analytica Scandal. The power of data analysis on politics is undeniable, we now have to make sure we are voting for the leader who uses this data for the greater good and not for personal gain.
This week we are also being warned to approach RPA (Robotic Process Automation) with caution. RPA is a new approach to machine learning technology that is an application used in businesses to interpret existing software, transactions, data and generate an appropriate response. Examples of RPAs are Blue Prism, WorkFusion and OpenSpan. Experts are recommending companies approach this with caution as the systems will only be effective if properly designed, planned and governed. If you are considering this technology to free up you workforce, research each option and have an expert integrate, analysis and plan the deployment and future management.
Tibco have just announced that they have opened a research lab to design and develop new products in evolving areas such as blockchain, machine learning, artificial intelligence and the internet of things. Blockchain has emerged from the Bitcon and cryptocurrency, it is a techonology that provides a highly secure record of transactions. The aim of Tibco’s research progects is to develop software and applications that can be used by those who are not technology experts. https://www.tibco.com/solutions/blockchain
Are your company working towards KPIs or OKRs? KPI (Key Preformance Indictators) were extremely popular at measuring the progress of a companies iniatives. However, they are seen as less goal oriented and more indicidual employee focused. OKR (Objectives and Key Results) sets an organizational framework to define objectives to achieve goals with daily reviews. This is concept adopted by companies such as Google, LinkedIn, Uber etc. OKRs have fast become the new vogue, companies finding their keep the staff motivated and focus. To know more check out http://okrexamples.co.
The world of data is dramatically changing at an unprecedented rate and if businesses don’t keep up it could lead to significant losses. From the cloud to social data the sheer volume and speed at which it is generated require efficient software, storage, and analytics to gain knowledge to drive decision making. Informatica is one of the most prominent leaders in the field of business intelligence, specializing in cloud data management and a vast range of BI and Big data tools. Informatica works seamlessly with any cloud provider, including Microsoft Azure and Amazon’s AWS. They have recently created a new data dynamic strategy to help you approach this daunting task. They encourage you to see data as a powerful calculated business asset. Suggesting you move from our current application-centric approach to placing data at the center of our organization and be app-aware.
Understanding Data through Informatica
This data is coming in volumes we have not seen before and from thousands of different sources. This multi-structured data can come in the form of a tweet to a thumbs up and is continuously streaming live from various sources. This is why Informatica says we should not restrict our data to applications, but, move the apps to work with the data as a more flowing arrangement. Working towards a more hybrid world where SQL and non-SQL systems work in harmony.
Informatica technology helps provide businesses with actionable insights and exponentially speed-up the decision-making process. Moreover, it offers a range of data integration, mining, discovery, analytics, architecture, and management tools to help a business improve their operations.
Does your Company require a live product demonstration or training on the new Informatica platform?
ExistBI is Authorized Informatica Training and Informatica Consulting Partners. We have a strong team of experienced data integration consultants that offer best practice advice, product demonstrations, POC’s and Informatica classes (if you already own the software). Some of the popular Informatica training classes we offer include:
1. Informatica PowerCenter Training for Developers
The Informatica PowerCenter can be more accurately referred to as the nerve center of Informatica. It actually consists of development tools that help troubleshoot processes and oddities. The classes will help you learn everything from mapping design to processing at the operational level. You can even schedule PowerCenter processes and understand connectivity requirements for loading data from various sources to allow for effective integration.
Target Audience: Developers
2. Informatica Axon training
With Axon, you can learn to define, explore, and document important organizational business structures. With the bulk loading of data, you can learn to define business glossaries, as well as how to maintain systems, datasets, and attribute information. Moreover, you can discuss data governance, tailor its use to a company structure, navigate the Axon interface for more options and perform searches across several inventories.
Target Audience: Data and Business Analysts, End Users.
3. Informatica Big Data Management training
In Big Data Management you Learn the mechanics of Data Integration using the Informatica Developer tool for Big Data Development. This course takes you through the key components to develop, configure, and deploy data integration mappings.
It was James Dixon, the founder and CTO of Pentaho who coined the term “Data Lake” he explains it as; “If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake and various users of the lake can come to examine, dive in, or take samples.” Is this just a new trend replacing the Data Warehouse or is it something your company could benefit from? Here are four differences between Data lake and data warehouse to help you…
1. Format Of Data Stored
Warehouse stores large amounts of structured data. However, a Data Lake can store large volumes of structured, semi-structured or even unstructured data. When uploading data to a data warehouse you must schema-on-write, model/structure the data. When you upload the data to a Data Lake you process it in its raw form and then schema-on-read, model it when you are ready to use it.
2. Storage Costs
Data Lake costs have been found to be reduced in comparison to Data Warehouse. If we take the Hadoop platform as an example, it is an open source software, therefore, licensing and support are free. In addition, it is designed to be installed on low-cost commodity hardware.
3. Data Security
Because Data Warehouse technology has been around for decades it’s security systems are far more mature than that in the newer Data Lake platforms. However, this is something of great importance to the Big Data industry, with Data Lake technology fast improving.
The Data Warehouse ethos has always been an ‘all are welcome’ and has actively encouraged all in BI and analytics to be users. However, at this point, a Data Lake has been recommended to be more appropriate for a Data Scientist to gain the most benefit from this solution. An example of this would be that data within a Data Warehouse is difficult and time-consuming to change, however, it is easier for the user to reconfigure the data within a Data Lake.
Many people ask if there is a difference between a Data Warehouse Solution and a Data Lake Solutions, as you can see there are some significant differences. This can make it challenging to know which is the most appropriate storage platform for your company’s big data needs. This is where we can help, with experience in Data Warehouse Consulting and Data Lake Services we can assess your needs and devise the best fit for your project. To find out more contact us on the link below.
When it comes to deciding on a career, students often have a very restricted view of the options available to them. The truth is that the field of IT is so large and expansive that a person can find their calling in a number of ways. In many cases, when these students finish college, they come to realize a lot of what they studied in their educational institutes was not even remotely related to the kind of knowledge and skill set they would need at their workplaces.
Employers nowadays demand that candidates possess a number of skills and have the ability to think beyond the limits of the box that is set in many formal curriculum. Luckily, even for students who are unable to meet these criteria, there are still ways in which they can propel their career forward and achieve the success that they have been working so hard for. How may you ask? Through tableau consulting.
The use of data to benefit business growth has been on the rise for the past decade and it is still soaring at a speed that is quite unprecedented. This is why it is absolutely imperative for businesses to be able to visualize and present this data in meaningful ways so as to better understand its trends and patterns. To do this, they need to first learn which data analytics tool is appropriate for them and how to use it to the best of its ability. This is where a business intelligence trainer and consultant is required.
What is Tableau and Why Is It Needed?
Before you can begin your journey as a tableau consultant, it is first essential to understand just what tableau really is. Tableau came into existence in 2003 as a data visualization software, and has since, evolved with its customer’s needs to provide one of the most cost-effective and superior data-solutions on the market. Companies are at a point where advanced data management is the key factor that can propel them forward, and as such, high-quality visualization and efficient report distribution has become their number one priority.
How Can Someone Begin Their Journey as a Tableau Consultant?
People are often quite intimidated by jobs in the field of IT, especially people who have limited past experience in it, but it does not have to be daunting. With dedication, commitment, and the willingness to learn, you can transform your career from that of a rookie to one of a professional. One way of doing this is to enroll yourself in professional Tableau training classes to learn about the intricacies of the software and its various different modules. You will be taught by experts who have a vast amount of experience during their years in the industry and are willing to teach you everything they know. You will also be provided with a professional learning environment in which you will be able to attain knowledge, apply it, and interact with other learners in a productive way. For any more information on Tableau Training and Tableau Consulting, feel free to get in touch with us. For free taster videos, check out the Tableau learning pages https://www.tableau.com/learn/training#getting-started.
What do you think is the key to successfully running an organization? Is it the human resource, the technology, the management or something else altogether? Any of the above may be the right answer, but one of the most important factors that contribute to the success of an organization is the departmental integration. No matter how efficient the technology, how good the human resource or how well the management gets everything done, unless there is a collaborative culture or a system that requires joint efforts, there may as well not be an organization at all. With IBM Cognos training, your organization and its employees can learn the art of working together towards a common goal.
Why Has the Demand for Business Intelligence Spiked in this Century?
As we move towards a smarter, technology-driven era, people become more and more aware of their needs and how these needs can be fulfilled in the most efficient manner. Settling for a sub-standard product or service is not an option anymore and the several choices in the market make it challenging for organizations to compete for a market share. Running a business in these times means that you need to be acutely aware of what your competitors are doing to survive.
Business intelligence service offers organizations concrete evidence of what is needed to compete on the same level as their competitors. Having deep insights about your target market, their preferences, price acceptance, stimulus responsiveness, among other factors can greatly affect your business’ position in the market. For instance, knowing why a regular customer has switched from your brand of fresh fruit juice to your competitor’s can provide you with enough information on what needs to be done to improve your subsequent sales and profits. This goes to show that business intelligence can greatly improve a company’s decision-making abilities.
IBM Cognos – What Does It Offer?
IBM Cognos is a web-based, enterprise business intelligence tool that provides a business with numerous options to improve data migration and integration, as well as analytics. Its several services include:
Although IBM Cognos is not primarily a data visualization tool, it allows for fast and efficient data relationships to provide business users with actionable insights in no time. IBM Cognos also offers a framework manager, transformer, and cube designer to help with its processing and analytics.
The need for departmental integration arises from the need for everyone in the organization to be on the same page. Without effective data recording and storage in the correct format by the budgeting department, the production department may not be able to plan for raw materials and production. Similarly, the accounts department may be unaware of the expenses that they will have to pay and the human resource department will be unable to plan anything with regards to the workforce. With IBM Cognos training, business users will have an effective and secure enterprise-wide platform linked to the data warehouse or server, that will allow its users to access, plan, forecast, and prepare budgets for production, marketing, sales, and other functional departments.
Benefits of Using IBM Cognos
Qualitative data exploration
Multiple analysis techniques
A wide, integrated architecture
Powerful and seamless connectivity
Functionality with Microsoft Office
Enhanced user experience
Powerful ‘what if’ tools
Ability to create custom dashboards
Dynamic reporting with a ‘drag and drop’ interface
Fast results (reduced reporting time)
Multiple file export formats
Multiple users, secure logins
“With Cognos BI training, your organization can improve its departmental integration, workflow, and efficiency, in addition to providing actionable insights, leading to effective decision-making overall. Contact ExistBI for more details on IBM Cognos Training.”
Understanding Data Science and Its Benefits With Informatica PowerCenter Training
You must have read about the boom of US data science and Big Data technology that has taken the world by storm, but if you have never heard about Informatica PowerCenter and BDM, you are definitely missing out on some powerful tools. The concept of business intelligence is not new, however, if you are not staying up-to-date with the latest trends in data technology, you may as well say goodbye to the prospects of a flourishing technology career. Informatica PowerCenter training and BDM can provide you with all the necessary skills to help you start your professional BI and Big Data technology journey with a bang.
The Boom in US Data Science
With a lot of competition today, many companies are adopting data science technology and methodology to enjoy greater benefits in the corporate world. The requirement to avail of the uncountable benefits of data science is the secure and efficient storage of data. The knowledge extracted from this data can provide deep insights and unlock profitable opportunities for organizations.
However, without the assistance of experts, you may never be able to unlock the power of big data. Every profession relating to data science and technology holds enormous value today as experts in this field have the ability to leverage a firm’s data and drive it towards profitability. Here are a few examples of the benefits it provides:
It helps management and functional-level users with decision-making.
It directs actions on the basis of trends (driven from data) and helps define a direction to work towards.
It helps users adopt best practices and focus on important issues.
It helps identify opportunities.
It aids in decision-making based on factual information.
It helps to measure the outcomes of decisions beforehand.
It helps to identify the target audience and refine the search.
It allows the right talent to be recruited.
Informatica is one of the globally identified enterprise software that offers cloud data management and other data-related services. With various products, Informatica tops the charts with its ETL tool, secure platform, and big data capabilities among other things.
The Informatica PowerCenter is one of the most widely used data ETL tools around the world. It specializes in the extraction, transformation, and loading (ETL) process of data that is one of the building blocks of a data warehouse. The PowerCenter acts as a nerve center for all processes that allow data to be stored and retrieved according to the requirements of the user. However, the use of Informatica is not just limited to data scientists and administrators – it can also be used by developers. It helps with data architecture and workflow but mainly focuses on data integration. It also helps with job scheduling, debugging, session monitoring, relational data, structured as well as unstructured data, and mainframe data.
Informatica PowerCenter training is available in the US, Canada, UK, and Europe – it helps you meet the growing data integration demands of every business that include high scalability, security, high availability and seamless recover option, a collaborative culture, data masking, metadata management, and much more.
Businesses are always on the lookout for new talent with unique data skills and the motivation to succeed. Unless you are determined to change with the world, you may just end up at a mediocre job with a low salary and little to no benefits. Informatica PowerCenter training offers the opportunity to observe data science and advancement in technology up close. With niche areas like Big Data Management, Data Governance, Data Quality, Data Lakes, Data Warehousing, MDM (Master Data Management), B2B exchange, data integration, service-oriented architecture, machine learning and much more, you will have the option to identify and explore your strengths. Turning towards technology is a need today, rather than a want. But you don’t need to worry – there is still time for a strong entrance into the corporate world before the next wave of technology hits and renders your skills less valuable.
By the literal meaning of the words, it is understood that ‘big data’ refers to huge amounts of data; however, there is more to it than meets the eye. A business receives both, structured and unstructured data in large volumes on a daily basis, but here, the volume is not what you should focus on. It is what you can do with this data that should be of interest to you. With today’s data being collected in such large amounts, it is often unstructured and even time-sensitive, which is why relational database engines are not able to process it as efficiently and effectively as needed. The new data approach uses substantial parallelism on easily available hardware to process the huge chunks of data appropriately.
Today, every piece of data that we receive or create is in raw format and is much greater in volume than what we used to generate even a week or month prior. Moreover, we have come a long way from databases and spreadsheets, therefore relying on the same old machinery and techniques for emerging data processing needs is simply pointless, not to mention, time-consuming and ineffective. Today, our every move leaves a digital trail, but analyzing how that trail can be useful in the future is what this hype is all about.
Big Data Capabilities
So, why big data? Indeed, you and I were happy with what we had, i.e. our spreadsheets and manual data entry techniques. But could that really go on forever?
We are living in an era of extreme digitization where competition is at an all-time high; therefore, there are many opportunities that we can create if we utilize the data we collect. With large sets of data, you can gain insights for; new product development, quick decision-making, increased efficiency and cost reduction. With the right business intelligence tools and hardware, you can accomplish the following business-related tasks:
Generate offers based on consumer buying habits on point of sale
Determine the root cause of strategic or operational issues, defects, and failures in real-time
Detect fraudulent behavior to improve security
Recalculate risk portfolios
Visualize important decisions to improve confidence
Here at ExistBI, we have been providing MicroStrategy Training and Consulting for many years and we are always intrigued to see what improvements are made in the upgrades. The beginning of this year sees the new MicroStrategy 10.11 release and we are excited about the improvements that will have a big impact on your ability to get the most out of your company’s data. The up-grade promises faster access, better mobile support, and innovative visualizations and it delivers. Here is a quick summary of the some of the new or improved features…
Out-of-the-box – The software contains improved visual analytics capabilities that produce out-of-the-box visualizations. These advanced images display the data in a more engaging and revolutionary way.
Mapbox – This is the new geospatial capability that provides additional interactivity and greater overall flexibility through dynamic layers, enhanced clustering and much more.
Library App – The new Library App allows users to view dossiers easily on any device. The app is optimized for iOS and Android Smartphones.
Dossier Recommendations – The MicroStrategy workstation, Desktop, and Library have an intelligent recommendation engine that provides suggested related content for the user to view.
Face ID integration – iPhone X users can quickly use the Face ID authentication method to access the MicroStrategy Mobile app and MicroStrategy Library app. This fast, on-the-go method is very convenient for today’s busy working world.
These are just a few of the exciting features of the 10.11 platform, our team or Certified MicroStrategy Trainers are ready to help you utilize them to promote business development and profitability. Find out more about our training opportunities here https://www.existbi.com/microstrategy-training/ or contact our team on the link below with any queries.
In the world of Big Data Management, Informatica has fast become the leading ETL tool with 2-3 times faster processing powers than its competitors such as Spark. Hadoop has allowed companies to use more data for analysis than ever before. However, this data requires Big Data software tools to integrate, process and analyze this vast amount of information. With so many Big Data engines on the market, it is hard for businesses to decide which would be the most appropriate tool for them. Here we are five reasons why to go for Informatica Big Data Management:
1. Boost Productivity
The Informatica Smart Performance Optimizer is compatible with multiple data processing engines, for example, Spark. The Optimizer executes the highest performance, scalability, and resource utilization without interfering with data pipelines. Allowing existing data pipelines to remain unchanged despite changes in the technology. This system shortens the time to value by allowing maximum developer productivity, operational reusability, and data integration performance.
2. Seriously Impressive Data Connectivity
Informatica Big Data Management has almost universal access to transaction data including RDBMS, OLTP, OLAP, ERP, CRM, mainframe and cloud. Alongside, interaction data such as social media, log files, machine sensor data, Hadoop, NoSQL formats, documents, and emails. This system has specifically high-speed data integration for Hadoop and Spark allowing the data to be processed at any scale. This allows data scientists to be spending their time on analysis rather than integrations.
3. Machine Learning
This intelligent data tool makes it easy to access complex, multi-structured or unstructured data and creates bespoke parsers for repetitive use. The tool also has prebuilt parsers available for market and industry data.
4. Adapts to Open Source Framework
The Hadoop visual interface is continually changing as new innovations emerge, the Informatica Big Data Management adapts to each change in the interface allowing each previous data pipeline to be preserved and applied regardless of the latest updates.
Sold? If so, this is where ExistBI can help, we are a trusted Informatica consulting and authorized Informatica training partner. ExistBI is a leading provider of Informatica classes in the US, Canada, UK, and Europe, these include Informatica Big Data training, PowerCenter training, Axon, IDQ, B2B, ILM just to name a few. Our experienced trainers deliver classic or fit for purpose Informatica training classes. These can be held onsite or via a virtual instructor-led online environment (using Informatica University official infrastructure and materials). We help solve our customers toughest challenges by providing unmatched big data and business intelligence services in training, consulting, digital, technology, operations and compliance. With expertise across most industries and all business functions, we deliver transformational outcomes for a demanding new digital world. Contact us for further information.
“Data Scientists” have become the new key players to advance business growth and development. To get the most out of the vast volumes of data companies produce they need an expert in managing, analyzing, safeguarding and reporting. These skilled experts not only have the ability to solve some of the toughest problems but, also the curiosity to discover the problems that need solving.
The popularity of Data Scientists within businesses and as a career is a sign of the times. Companies now realize the virtual gold mine that sits in all the data they produce. By unlocking the knowledge behind this information, they can boost revenue significantly. The Data Scientist was born. These Specialists started off as Statisticians or Data Analysts, people in the IT Department. Then as the volumes of data grew, the multiple sources generated this data and the software required to analyze and interpret evolved, as did their role requirements. These essential personnel now span the IT and Business department and are integral to decreasing expenses and increasing productivity.
When Do I Need to Hire a Data Scientist?
Before investing in this employee, there are a few things for your company to consider:
Do you produce large amounts of data and does this data contain information that significantly impacts business decisions?
You know you want to be data driven but, don’t know which software tools are appropriate for your company and how to get the most out of them.
Will your company environment accept a data analyst? Do you have a data motivated culture to boost productivity? Will they have executive buy-in?
Are you ready for change? Data Scientists may have a new and challenging perspective based on their findings, are you ready to embrace change?
How ExistBI Can Help?
The new generation data management tools require the expertise of a Data Scientist. These Specialists can help companies choose the most appropriate software, develop, manage, run the applications and train staff on analytics and reporting. This is where ExistBI can help, they solve their customers toughest challenges by providing unmatched big data and business intelligence services in training, consulting, digital, technology and operations. With expertise across most industries and all business functions, they deliver transformational outcomes for a demanding new digital world.
London, UK April 2018 – ExistBI provides GDPR Consulting Services and multiple Software Solutions that help customers meet EU legal requirements. This year the Data Protection Directive with be replaced by the European Parliament and Council’s General Data Protection Regulation (GDPR). This will be the primary law regulating how businesses manage and protect EU citizens personal data. Companies have to comply with these new regulations by May 25, 2018. For those who do not comply, there are significant fines.
ExistBI data specialists identify the impact of GDPR on organizations and shape, mobilize and deliver digital transformation programmes to achieve compliance and enhance data privacy within their processes.
“GDPR effects millions of Companies storing personal data, our team are here to help with the transition to the new EU requirements. We are excited to now offer GDPR Compliance & BI Software Solutions that will take the stress out of this process.” Max Russ, Senior Director, ExistBI.
The GDPR Consent software we implement is a Data Protection Officers best friend to prove data protection compliance. The tool has several features covering consent, legal rights, data analytics, and data management. The software supports all six lawful basis for obtaining personal data and provides transparency to any changes made. It has the ability to integrate with other software’s used within the company to keep changes up to date. For more information contact the ExistBI team.
How Can ExistBI Help You?
ExistBI solves their customers toughest challenges by providing unmatched big data and business intelligence services in training, consulting, digital, technology and operations. With offices in California, Ohio, New Jersey, London, and Zagreb, they partner with Medium to Large Companies and Government organizations, driving innovation through intelligent data led initiatives. They have experience in most industries and all business functions to deliver transformational outcomes for a demanding new digital world.
If you have not yet met Tableau, here are just some of the reasons why we are all hooked.
1. Data Actually Becomes Easily Understandable and Useful.
Tableau’s motto is to help people see and understand their data. This software has the ability to analyze data from over 40 different data sources, in a fast and easy visual format. This allows businesses to solve problems in a timely manner. This time-saving tool can update daily or weekly to provide immediate data impact graphics. Tableau is unique in that it can be an excellent application for an individual user or an enterprise. Data visualizations can be published on the Tableau server or shared securely throughout an organization.
2. No Need to Bother the IT Department.
Tableau has raised the standard for self-service BI tools, users can create high-quality data visualizations and self-service analyzes without the support of an IT department. This capability to analyze data has revolutionized the way data is used by companies, you no longer require IT intervention to interpret this information, it is accessible to everyone. Therefore, creating transparency and a more data-driven culture.
3. No More Coding.
There is no need to battle with coding in Tableau, there is a user-friendly built-in calculator that has drag and drop functionality. This enables complex calculations to be completed with a few clicks of a mouse. However, should you wish to integrate programming language, this is easily enabled within the software.
4. Next Generation Visualizations.
Tableau’s dashboard stories allow easy interpretation of the information and have the facility to be interactive to each individual end user. They can be adapted from desktop to mobile devices through their online apps. For inspiration on the vast capabilities of the Tableau dashboards, you only have to look at the Tableau community and online blogs.
What are you waiting for?! These are just a few of the reasons why your business can benefit from Tableau. To get the most out of this software it is important to invest in some expert training, ExistBI offers onsite and virtual instructor-led Tableau consulting and training. Amongst their training packages available they offer a unique custom three-day Tableau Desktop Bootcamp training designed to take users from a beginner to more advanced levels. This course is a combination of the Tableau Desktop Fundamentals and Tableau Desktop Intermediate curriculum. It is for anyone who works with data – regardless of technical or analytical background. For more information visit our Tableau Training page.
Big Data has become a part of the everyday business; however, it is not without its challenges. A report from Gartner found that only 15% of businesses were able to navigate their Big Data projects to production, with the majority not making it past the pilot phase. This report clearly shows that organizations are finding it challenging to manage and implement Big data project strategies.
Firstly, let’s just summarize what qualifies as Big Data. Big Data can vary for every business, most experts define it under the three Vs Volume, Velocity, and Variety. It is clear to see from the table how these three characteristics can cause challenges for growing businesses. Here are the top 4 challenges that occur.
Big data requires big storage space, this requires companies to continually be scaling up their ability to accommodate this increase.
New data is being generated constantly and companies need to respond to this in real-time.
Data is generated in a wide variety of different forms; text, images, spreadsheets etc.
1. Managing Big Data Growth
It is estimated that the amount of data stored in the world’s IT systems is doubling every two years. Companies are looking to supporting technologies to help them manage and store their ever-growing data needs. Such technologies can help businesses compress their data and consequently their data storage costs. Companies are using tools such as Hadoop, Spark and many more to help overcome this challenge.
2. Generating Real-Time Insights
Within this Big data is information that can be used for business growth and development. Businesses can find ways to decrease expenses, accelerate the speed of production and the capabilities of their service and discover opportunities for new products or services. To keep this data relevant companies are looking to analytical tools that are able to generate reports in real-time, such as Tableau, Microstrategy, Microsoft Power BI, Pentaho, IBM Cognos, SAP BusinessObjects just to name a few.
3. Keeping Big Data Secure
Data protection has always been taken seriously, however, with the introduction of the General Data Protection Regulation (GDPR) in May 2018 every business is having to review it’s current data protection policies. Alongside safeguarding individual’s privacy businesses have to look at protecting access to this Big Data through identity and access control, data encryption and data segregation.
4. Big Data Talent
It is evident that the data generated by businesses is vital for business growth and development. To get the most out of this data you need to have expertise in managing, analyzing, safeguarding and reporting. The new generation of data management tools requires the expertise of a Big data specialist. These specialists can help companies choose the most appropriate software, develop, manage, run the applications and train staff on analytics and reporting.
“This is where ExistBI can help, they solve their customers toughest challenges by providing unmatched big data and business intelligence services in training, consulting, digital, technology, operations and compliance. With expertise across most industries and all business functions, they deliver transformational outcomes for a demanding new digital world.”
What is GDPR and are you ready? ExistBI Can Help You Meet GDPR Requirements
ExistBI is helping organizations around the world understand the impact of GDPR and create project frameworks to guarantee compliance with these new regulations. This year the Data Protection Directive with be replaced by the European Parliament and the Council’s General Data Protection Regulation (GDPR). This will be the primary law governing how businesses manage and protect EU citizens personal data.
Companies have to comply with these new regulations by May 2018. For those who do not comply, there are significant fines. The Information Commissioner’s Office (ICO) has created a checklist to ensure your company complies with the new regulations. This checklist is an online calculator, one for data controllers and a separate one for data processors.
The recommended approach to this impact on your company is to raise awareness of the forthcoming changes and highlight potential compliance problem areas to the relevant people. An audit is the best way to review the personal data you hold; where does it come from, how is it processed and stored.
The GDPR has additional requirements related to your companies Privacy Notice, information provided when obtained personal data. Similarly, you should review your consent procedures against the new GDPR standards. Assess your need to obtain the individual’s ages and subsequently their parental or guardian consent. The GDPR is focused on the protection of children using online service, especially those related to social networking. They have set the age of consent at 16 (although this can be lowered to 13 in the UK).
The GDPR has made it mandatory to have a Data Protection Impact Assessments (DPIAs) in cases where the data gathering process could potentially result in high risk. Once this information has been obtained the GDPR has additional enhancements to previous regulations related to individuals’ rights such as; right to rectification, right to data portability etc. This is the time to review your current procedures, should such a request occur.
Evaluate your lawful basis for processing personal data, as under the new regulations some individual’s rights may vary depending on this. Your lawful basis should be documented in your Privacy Notice and within any request for information, this will ensure you comply with GDPR accountability guidelines. Review your policies should you have a data breach, the GDPR may not only require you to report it to the ICO but in some cases to the individual themselves. The changes required to adhere to the new legislation are not insignificant, by having a designated Data Protection Officer to ensure compliance.
How Can We Help You?
Our data strategy specialists detect the effect of the GDPR on your organization and customs, deliver and support digital data transformation programmes to reach compliance and enhance data privacy within your processes.
GDPR readiness assessment
Our data specialists will design and implement a sustainable privacy and data protection programme
Trump US Election Win, Big Data & Tableau Training
January 25th, 2017
Did Donald Trump have better access to sentiment data through powerful Big Data Software and cutting edge Analytics tools such as Tableau software?
The quick development of Big Data solutions and Analytics software could of helped Trump win the US election by understanding public opinion and key voter trends. His savvy team would of sub-contracted a BI firm to run Big Data Analytics on social media and online publications and then present the data via simple or complex visualizations using Tableau for a clear view of any given Big Data campaign.
Tableau is currently partnering with UK universities to create a Tableau curriculum. Check out ExistBI video which discusses our unique 3-day Tableau Desktop Bootcamp onsite or virtual training designed for anyone who works with data – regardless of technical or analytical background.
When data of an enterprise grows immensely, it needs to be stored properly and categorically in data warehouses called Informatica PowerCenter. Informatica PowerCenter training enables IT personnel to create data warehouses with ease. This stored data can then be accessed and utilized as and when needed for key decisions. In a recent interview with Information Week, the new CEO Informatica, Anil Chakravarthy, said that…
“Informatica wants to transform technology into a tool that can help enterprises to use data insights to drive revenue, profits, or manifest other real business returns. Thus, the job scope and future of Informatica PowerCenter trained individuals look brighter than ever.”
Top Firms Using Informatica PowerCenter
Some of the MNCs, financial conglomerates and large multinationals that use Informatica PowerCenter extensively are:
Salary of Informatica PowerCenter Trained Personnel
According to ITJobsWatch, Informatica developer jobs have become one of the hottest jobs in the data warehousing domain after gaining four points between November 2015 and February 2016. With an acute need of Informatica PowerCenter training for businesses, job opportunities in this particular domain have quadrupled.
ExistBI has specially designed courses to help you leverage the power of Informatica PowerCenter and its Informatica tools. We have hands-on, customizable Informatica PowerCenter training modules.
Get Into Power Center Training? No matter how you see it, data management is of great Importance. So much so, that as for now it has become a subject of discussion everywhere. However, it has also brought forward many problems with it. To tackle and overcome these problems, one needs to find an adequate solution. as well as to be able to undertake challenges that data management and businesses throw at you.
Power Center Training is basically the major part of informatica which helps you explore the informatica better. It enables you to define parameters in a better way and come up with meaningful solutions. Moreover, it allows you to develop better and effective strategies so you can handle businesses and data related problems in a much more efficient way. It is a blend of understanding the informatics data as well as its tools.
We help aspiring students in interpreting the data and study the technology. We also train the students on how to prepare for Informatica certification and deal with informatica problems. Any professional woud know that it is vital in the current rising competition and rising technological market.
Get into Power Center Training helps guide you to the right path to ensure a safer career. By seeking help from the Power Center Training, it becomes easier to compete under pressure. It also helps you build up your reputation in such a way that it will make it hard for other competitors to bring you down. Your output in your work will definitely be increased and you will be able to achieve your desired goal.
Are you struggling with your career and tired of finding your competitors doing better than you? Boost your career with Tableau consulting. If this is the case, you can stand up and take a rise with Tableau Consulting. Doing so will help you develop the skill set required to survive under the pressure of the fierce competition. In fact, it will enable you to thrive for becoming a better person than ever. Tableau consultants offer help in all expertise area related to Tableau which may include database cleansing, transformation, strategic planning, business intelligence and much more.
As the time goes by, the complexities in data and information keep on growing and businesses now have to deal with more problems concerning data. To make it easier, you need to make decisions that will give a boost to your business. Data accumulation and management is an important element of any system, Tableau Consulting is available for that and it offers to provide you with extended tools for your data management. Moreover, Tableau Consulting is offered by highly experienced and informatica professionals whom you can trust.
We have made learning easy for you as we provide them on-site or virtually, trying our best that you make the most out of our service and your investment. We help you compose your qualities like speed, accuracy, and flexibility.
Consulting professionals and people having experienced things that you have not in your professional life can help you build up your confidence. Through their guidance, you can gain motivation even if you are behind other competitors. They will help you learn ways through which you can find ways to deal with the problems that you are struggling with and with the problems that are coming in the near future. We have helped train clients that were belonged from large as well as from small industries and we assure you that our capabilities will certainly meet your needs.
Some of our representative clients include:
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