The 2016 Magic Quadrant for Data Quality Tools by Gartner is out. One way to have a free read is downloading the report from Informatica, who is the most-top-right vendor in the tool vendor positioning.
Apart from the vendor positioning the report as always contains valuable opinions and observations about the market and how these tools are used to achieve business objectives.
Interenterprise data sharing is the last mentioned scenario besides BI and analytics (analytical scenarios), MDM (operational scenarios), information governance programs, ongoing operations and data migrations.
Another observation is that 90% of the reference customers surveyed for this Magic Quadrant consider party data a priority while the percentage of respondents prioritizing the product data domain was 47%.
My take on this difference is that it relates to interenterprise data sharing. Parties are per definition external to you and if your count of business partners (and B2C customers) exceeds some thousands (that’s the 90%), you need some of kind of tool to cope with data quality for the master data involved. If your product data are internal to you, you can manage data quality without profiling, parsing, matching and other core capabilities of a data quality tool. If your product data are part of a cross company supply chain, and your count of products exceeds some thousands (that’s the 47%), you probably have issues with product data quality.
In my eyes, the capabilities of a data quality tool will also have to be balanced differently for product data as examined in the post Multi-Domain MDM and Data Quality Dimensions.
Liliendahl on Data Quality 