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Techniques to Handle Big Data in BI Data Integration

By SiliconIndia   |   Thursday, 10 May 2012, 04:37 Hrs
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Bangalore: The quality of Business Intelligence systems and their supporting data warehouses depends heavily on the quality of the data that they deal with. Incorrect handling of BI data integration can land your end users and your organization in dilemma.


Now, as BI tools penetrate into organizations and determine the success of business operations, utilizing a fool proof way to integrate BI data is crucial. The data integration issues have been a major constraint on the progress of BI, quite often leading to project failures.


Recently, due to the changing complexities of data (with 'Big Data' also in the picture), matching and making the data ready for BI application is turning out to be a nightmare. In addition, for older systems it becomes necessary to interpret the data. When faster access to BI information is required, the problems become quite complex.


The traditional technology used for BI data extraction is ETL (Extract, transform and load) software. This manages BI data extraction by extracting the requisite data from source systems. Newer data integration techniques seem to reduce the delays as they stream the data in granular form as compared to the ETL which manages streaming in huge chunks. Another option is a virtual approach which will not move the data from its source system. Instead, data can be viewed from multiple sources for BI uses. However this never antiquates ETL, which will be used as long as there is data that cannot be delivered in real time.


The analysis of Big Data makes BI data integration more complex in many organizations. Organizations that are planning or have just started the implementation of big data analysis should be prepared for data integration issues, starting with the capital and manpower to deal with challenges like huge inputs of data obtained from social media.


Real time integration is expensive and only a strong business need for it warrants the usage. Yet we can't deny that analytics applications are moving towards real time data integration. So one may deduce that BI is at interesting crossroads.

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