Data quality when it comes to product master data has traditionally been lesser addressed than data quality related to customer – or rather party – master data.
However, organizations are increasingly addressing the quality of product master data in the light of digitalization efforts, as high quality product information is a key enabler for improved customer experience not at least in self-service scenarios.
We can though still use most of the known data quality dimensions from the party master data management realm, but with the below mentioned nuances of data quality management for product information.
Completeness of product information is essential for self-service sales approaches. A study revealed that 81 % of e-shoppers would leave a web-shop with incomplete product information. The root cause of lacking product information is often a not working cross company data supply chain as reported in the post The Cure against Dysfunctional Product Data Sharing.
Timeliness, or currency if you like, of product information is again a challenge often related to issues in cross company supply chains. You can learn more about this subject in the post How to avoid Stale Product Data.
Conformity of product information is first and foremost achieved by adhering to a public standard for product information. However, there are different international, national and industry standards to choose from. These standards also comes in versions that changes over time. Also your variety of product groups may be best served by different standards. Learn more about Five Product Classification Standards here.
Consistency of product information has to be solved in two scopes. First consistency has to be solved internally within your organisation by consolidating diverse silos of product master data. This is often done using a Product Information Management (PIM) solution. Secondly you have to share your consistent product information with your flock of trading partners as explained in the post What a PIM-2-PIM Solution Looks Like.
Accuracy is usually best at the root, meaning where the product is manufactured. Then accuracy may be challenged when passed along in the cross company supply chain as examined in the post Chinese Whispers and Data Quality. Again, the remedy is about creating transparency in business ecosystems by using a modern data management approach as proposed in the post Data Lakes in Business Ecosystems.