Doing master data management with several different entity types is most often seen as the federated discipline of handling Customer Data Integration (CDI) and Product Information Management (PIM) with the same software brand.
And sure, doing this (including making that software) is a challenge as there are basic differences between the two disciplines as discussed in the post Same Same But Different.
But doing both well at the same time is only a starting point. Making business value from the intersection between the two disciplines is the real challenge.
I learned that 20 years ago when I started a new client relationship (which also was before MDM, CDI and PIM was household TLA’s).
The client’s head quarter was in the southern outskirts of Copenhagen, so on a good summer day I could go there on my bike. They imported else wasted peels from oranges grown in the endless South American citrus plantations to be used for our morning juice and else useless seaweed harvested in the hot waters around the countless Philippine islands.
Along with a few other raw materials the peels and seaweed were made into approximately a hundred different semi-finished products. Based on customer orders these were blended into not much more than a thousand different defined finished products being valuable ingredients for food and pharmaceutical production.
The number of different customers was also modest, as I remember not much more than a thousand different worldwide customers.
So, managing 1,000 different customers buying 1,000 different products shouldn’t be much of a MDM case. Of course customer data management with global diverse entities had its challenges and not at least product information handling with rising regulatory demands in the food and pharmaceutical segment wasn’t a walk over either.
But some big hurdles were sure in the intersection between customer master data and product master data and solving the issues did almost always involve data quality related to core transactions referencing the entities described in the master data.
Liliendahl on Data Quality 
