There is a trend on the data management market that the solutions are either going very niche (best-of-breed) in the data domain covered or they are encompassing a broader range of data types.

This can be seen in the spectrum of master data and product information as reported in the post MDM, PIM or Both.

We also see that governance and management of reference data is included in addition to managing master data as told in the post What is Reference Data Management (RDM)?

Some MDM (and RDM) solutions also extend the reach to cover aspects of transaction data and big data. The main scenarios covered are:

• Matching of party entities in traditional systems of record with the parties referenced in social streams and weblogs (systems of engagement) as well as in sensor data. This can be used in creating a Customer Data Platform (CDP).
• Extending data quality and data performance dashboards related to master data to cover aggregated transaction data and big data held in data warehouses and data lakes by using a shared set of reference data.

When product information is to be shared in business ecosystems through Product Data Syndication (PDS), this can be accelerated by using a data lake concept and new data stores as staging areas. This is due to that a main challenge here is that the data quality standards on the providing side most often are different from the data quality standards on the receiving side.

The diagram is a variation of a diagram included in the whitepaper Intelligent Data Hub – Taking MDM to the Next Level. The original is developed together with Salah Kamel, CEO at Semarchy.