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.
We’re a Visionary
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.
Which brings us to my third point: They’re strong believers (as we are) in business-user-centered data quality as a disruptor in this market.
Some relevant quotes:
"Evaluating and selecting data quality tools is much less of a specialized IT task than it was formerly. IT now requires much more collaboration with business leaders and users."
That’s good news. Business users are the ones who understand the value of the data in question, and what it means for that data to be suitable for purpose. While IT plays a critical role in making all of this stuff work, business users should be an important part of vendor selection, because they should also be an important part of getting the data quality up to snuff.
"Vendors have prioritized a range of key advances. For example, they have improved their products’ ability to fulfill requirements via proofs of concept, easier integration with information governance frameworks, and better usability and effectiveness for staff in business roles, such as data stewards."
"Data analysis capabilities that give business and IT functions (especially those supporting business users) insight into the quality of data..."
For the key capability of usability, they say, "Suitability of the tools to engage and support the various roles (especially business roles) required by a data quality initiative."
One of our strengths is "Data profiling, visualization, and business-driven workflow," which reads: "Information Builders’ reference customers score it highly for its data profiling, visualization and workflow functions. These functions support key business roles, such as information steward."
Finally, in its description of the Completeness of Vision axis, we read that they rate highly, "The vendor’s ability to adapt to significant market changes and disruptions, such as by supporting business-centric roles and providing advanced data quality functionality for the IoT (connectivity and deployment), data lakes, streaming data and external data."
Got that? Business-centric data quality is in the same class of disruption as IoT and data lakes. It’s that important.
There’s plenty more to read, along with reviews of 15 vendors so you get a feel for their strengths and cautions. (Did I mention we’re a Visionary?) Feel free to check it out, on us, and leave a comment to tell us what you think.