What’s Interesting About the Gartner Magic Quadrant for Data Quality Tools?

Three things struck me as really important in the new Gartner Magic Quadrant for Data Quality Tools, by analysts Melody Chien and Ankush Jain. You can download it here. Go ahead, I’ll wait.

First, we’re a visionary. We’re proud of that, and we think a lot of it has to do with our vision for data quality as part of the overall data integration and management stack, as opposed to some standalone activity.

Second, and related to that vision: It’s striking that Gartner has so closely associated data quality to integration.

A close reading seems to indicate that people who aren’t doing integration aren’t really doing modern data quality, either, and vice versa.

Start with what they say about the tools in the market: “The packaged tools available include a range of critical functions, such as profiling, parsing, standardization, cleansing, matching, enrichment and monitoring.” They also list, among the necessary key capabilities that organizations need in their tool portfolio, if they are to address the increasing importance and urgency of data quality, “connectivity; matching, linking, and merging; metadata management; and architecture and integration.”

Totally reasonable, right? How could you improve data quality without all of those things? But note how closely all of these things are related to integration. Even something as common as enrichment generally requires information from a data source outside of the data being enriched.

They do caution immediately afterward that “the data quality tool market continues to interact closely with the markets for data integration tools and for master data management (MDM) products. Users expect effective integration of, and interoperability between, these products, but not convergence.”

To really effectively improve data quality – especially when business users are doing the work – you want more than just interoperability.

Call me crazy, but I think users will eventually demand that all of these things be part of the same workflow. To really effectively improve data quality – especially when business users are doing the work – you want more than just interoperability.