Introduction to BI and BI data Integration challenges

Failure and Success of almost companies can be tracked down to the degree of excellency of their decision making. Data driven decision making process has been touted as one of the route to improve quality decision making. Data driven decision making needs to gather and analysis big data. Every company collect data on different aspects of its businesses it can be sales records, support department or customer calls. deeply analysis of big data and relevancy of data from different departments can produce key facts about real time state of business and any historical trends. Data can be used for predictive analysis or investigative analysis, Quality big data produce key insights in to business.

From 1960 companies have been employing Decision making Support Systems, to aid decision making. Over the years DSS has developed in to “Business Intelligence”. Business Intelligence, BI is the set of process, methods and technologies that derives significant insights out from big data. Almost all business organizations employ BI for planning, decision making, forecasting. Last couple of years has seen many applications of BI in tactical and operational decision making and better outcomes. These days for business organizations, BI is not a preference but a must have investment to survive in rapid competitive market.

BI has become a strategical requirement for businesses. Improving BI implementation cycle and flexibility could allow for more successful BI plans thus potentially accelerating data driven decision making in organizations.

Top five BI data integration challenges

You should start action by analyzing your business requirements, plan a process pattern, and understanding BI user needs.

Here are the uppermost points BI data integration challenges, along with suggestion for deal them.

1. The big-bang trap

It is attractive to tackle all sides of BI data integration all at once (big bang), but that approach may not be cost effective. A piece-meal approach will bring few challenges. BI data has to be integrated to the miniature level to be useful. Look at each data point separately, create a scheme for each, and integrate them one at a time.

Suppose you requirement to combine data from three applications: a business management system (MMS), a product database, and a customer database. Break it down into separate data sets such as customer information, financial data, etc., and merge them one at a time.

2. Neglecting Automation

Automated BI data integration is little challenging. The smaller manual intervention, the fewer the errors and data integration challenges. To illustration, to move data from the business management system to the point of sale application, we require to create HTML files. We can then manually upload them to an FTP server, where the POS system can process it at the separate store level. Or, we can put the data in the store folder, where it is processed directly by itself through a scheduler. The exactness and accuracy of the data can be maintained by a system of check-points.

3. Difficulties in drill-down reporting

Data integration is often the first step towards BI. To support drill-down reporting, big data has to be integrated at the deep level.

In the little scenario, financial data analytics is done at numerous levels: company, store, region, category etc. The better route to integrate big data is at the product level, therefore then the BI tool can itself merge with the higher levels as required. Integrating data straight at the company level, for illustration, will open up challenges when we give rise to generating products level drill down reports. Keep in mind, down to up is a better way of dealing in BI data integration.

4. Discrepancies in business data

Data should be readily monitored not only at the data integration design stage, but also at the user level stage. Users can point out a lack of compatibility or similarity and spot BI data integration challenges that the technology team may overlook. Quality check points should be made available to users also. If the sales data is found erroneous after data integration, users should be able to spot discrepancies in the data in any of the source systems.

5. Vendor challenges

Many implementation vendors lack adequate skill-sets to design/implement a BI data integration solution. Seek vendors who understand the technology well and know how to architect a solution. Make the final payment only after the solution is implemented successfully. This will ensure that the vendor tackles all BI data integration challenges that surface during implementation.

Pay attention to finer processes such as extract-transform-load (ETL). To improve data quality, make sure your Extract and Load processes occur at the source level, and not at the target level. The Load and Display processes can then be completed at the target level. Check volume and freshness of data regularly with real-time updates based on business requirements. Lastly, identify all the data points for integration and plan them out before you start the process.

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