Recently Daniel O’Connor blogged about Three Keys to a Successful Product Data Project BEFORE You Start the Project. Number one key suggested by Daniel is to know what quality product data looks like. I agree.
Besides Daniel’s very valid points on this matter, I would like to bring data quality dimensions into the game. Looking at data quality from a completeness, timeliness, conformity, consistency and accuracy point of view will help crafting tangible measures and identifying the root causes of where current culture, processes and technology lack the capabilities of meeting the desired state of product data quality.
Here is my take on how to use data quality dimensions for product data:
Completeness of product data is essential for self-service sales approaches. A recent study revealed that 81 % of e-shoppers would leave a webshop with incomplete product information. The root cause of lacking product data 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 data is again an issue often related to challenges in cross company supply chains. You can learn more about this subject in the post How to avoid Stale Product Data.
Conformity of product data is first and foremost achieved by adhering to a public standard for product data. 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.
Consistency of product data 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 data 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.
Liliendahl on Data Quality 