Companies rarely throw data away, especially if it relates to profit-generating operations. As their historical databases grew into enterprise data warehouses and then into big data applications, the critical question was always, “How do we analyze this information to generate actionable intelligence?” It started with power users having knowledge of statistics and query languages querying the data warehouse or big data applications.
Then new applications and new data types appeared, along with growth in customers, products, services and transaction volumes. These issues made analysis more complex. Companies saw the need to employ data scientists, specialists who had both knowledge of the business data as well as analytical and statistical techniques. As data and applications grew, understanding all of the various data associations, relationships and correlations became increasingly difficult.
The answer was BI. Many vendors created BI software solutions for business users that provided graphic methods of visualizing data and data relationships. Users could request analysis of business data from multiple data sources including on-premises, external data and cloud. The software would generate the appropriate queries against data sources, merge, match and sort the intermediate results, and finally aggregate and subset the final results for the requestor.
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