Top 10 Best Practices for Data Integration

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Click through for 10 data integration best practices from TDWI and SAS.

Topics : Application Security, IT Process Management, Six Sigma

The data management discipline known as data integration (DI) has undergone an impressive expansion over the last decade. Today it has reached a critical mass of multiple techniques used in diverse applications and business contexts. Vendor products have achieved maturity; users have grown their DI teams to epic proportions; competency centers regularly staff DI work; and DI as a discipline has earned its autonomy from related practices like data warehousing and database administration.

Given all this change, it’s not surprising that people in the field might not be up to speed on the current incarnation of DI. Even DI specialists and the colleagues who depend on them sometimes forget the new techniques, diversity, independence, collaboration, and governance typical of modern DI practices. Many suffer misconceptions and out-of-date mindsets that need adjustment.

The 10 practices in this slideshow, from a TDWI report sponsored by SAS, paint a modern landscape of current DI practices. They also bust a few DI myths that are still too common. Moreover, they raise the bar on DI, showing how sophisticated and powerful a DI solution can be—at least when DI is driven by modern best practices using up-to-date tools.

If you let it all soak in, this checklist  will redefine DI for you and your peers. And it will help you set higher goals and aspirations for DI work and its outcome. The practices listed here can be the guidelines that help you achieve more modern, high-value, diverse, independent, well-designed, far-reaching, green, collaborative, and well-governed uses of DI tools and techniques.

 

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