Completeness is one of the most frequently mentioned data quality dimensions as touched in the post How to Improve Completeness of Data.
While every data quality dimension applies to all domains of Master Data Management (MDM), some different dimensions apply a bit more to one of the domains or the intersections of the domains as explained in the post Multi-Domain MDM and Data Quality Dimensions.
With product master data (or product information if you like) completeness is often a big pain. One reason is that completeness means different requirements for different categories of products as pondered in the post Hierarchical Completeness within Product Information Management.
At Product Data Lake we develop a range of cloud service offerings that will help you improve completeness of product data. These are namely:
- Measuring completeness against these industry standards that have attribute requirements such as eClass and ETIM
- For manufacturers measuring completeness against downstream trading partner requirements (if not fully governed by an industry standard).
- For merchants measuring incoming completeness when pulling from merchants.
- Measuring against completeness required by marketplaces.
- Transforming product information to meet conformity and thereby ability to populate according to requirements
- Translating product information in order to populate attributes in more languages
- Transferring product information by letting manufacturers push it in their way and letting merchants pull it their way as described in the post Using Pull or Push to Get to the Next Level in Product Information Management.
Liliendahl on Data Quality 