The data quality tool industry has always had a hard time offering capabilities for solving the data quality issues that relates to product data.
Customer data quality issues has always been the challenges addressed as examined in the post The Future of Data Quality Tools, where the current positioning from the analyst firm Information Difference was discussed. The leaders as Experian Data Quality, Informatica and Trillium (now part of Syncsort) always promote their data quality tools with use cases around customer data.
Back some years Oracle did have a go for product data quality with their Silver Creek Systems acquisition as mentioned by Andrew White of Gartner in this post. The approach from Silver Creek to product data quality can be seen in this MIT Information Quality Industry Symposium presentation from the year before. However, today Oracle is not even present in the industry report mentioned above.
While data quality as a discipline with the methodology and surrounding data governance may be very similar between customer data and product data, the capabilities needed for tools supporting data cleansing, data quality improvement and prevention of data quality issues are somewhat different.
Data profiling is different, as it must be very tightly connected to product classification. Deduplication is useful, but far from in same degree as with customer data. Data enrichment must be much more related to second party data than third party data, which is most useful for customer and other party master data.
Regular readers of this blog will know, that my suggestion for data quality tool vendors is to join Product Data Lake.