Standard uses for data integration (DI) tools and techniques tend to divide into two broad practices areas, which we can call analytic and operational. Analytic DI usually occurs in the context of data warehousing and provides data that’s transformed for purposes of business intelligence. Operational DI usually involves operational databases and lifecycle treatments of these, such as data migrations or consolidations. It also involves the synchronization of data across related operational applications and the exchange of data between organizations in a business-to-business relationship.
If you’re a data integration specialist (or work closely with one) it’s important to know the intersections and exclusions of analytic DI and operational DI. They use the same tools, but use them in different ways. The skill sets are similar, yet one demands a knowledge of the DW/BI technology stack, whereas the other focuses on the stacks of operational applications. The two satisfy very different business and technology requirements. For that reason, the two DI practices demand collaboration and coordination with very different populations of business sponsors, as well as with related technology teams. Finally, data integration professionals (who may specialize in either analytic or operational DI) are increasingly asked to do work in both areas.
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