Andy on Enterprise Software

Squaring the MDM circle

April 7, 2008

Jill Dyche raises an important point about the how companies are tackling MDM. She mentions the “random acts of MDM” that are done in isolation in a particular business area, or involving a particular data domain, which are unlikely to evolve into an enterprise-wide MDM solution.

The tricky issue that companies face is that MDM is a genuinely large-scale endeavour, and because we all know how well giant enterprise projects usually go, they are understandably reluctant to take on an enterprise-wide project. Instead they pick off an easier piece, such as one particular data type, or perhaps a broader set of master data types but only in a subset of the enterprise, say across one division. As Jill says, such isolated initiatives won’t in themselves magically grow into enterprise MDM. There is a further danger in disconnected initiatives. At this point the vendor technology out there is at very different stages of maturity depending on what kind of data you want to tackle, and on what scale. Some vendors have a well proven customer hub technology, but with limited experience in tackling product data (and may lack key functionality to do this e.g. attribute inheritance) and usually have very limited ideas about business process workflow and data governance support. Other vendors from the PIM or the analytic MDM world usually have much better business workflow support, yet may have limited scalability e.g. you it would be a brave person who tried doing a 100 million record customer hub using a PIM product. The vendors with a CDI heritage are adding more workflow capability, and the PIM and analytic MDM vendors are working on scalability, but these are works in progress rather than completed and tested features and functions. Hence separate initiatives may end up using different technologies due to the demands of a particular area, and it would be easy to end up with one technology to handle product data, another for customer data, and maybe another where analytics were the driver.

In my view you need to combine an enterprise-wide vision with a practical, bite-sized approach i.e. thing big but start small. You can build a broad enterprise strategy that encompasses data governance processes for example, even if you decide to build out actual master data hubs in a stepwise fashion, beginning with certain high value data domains or company divisions that can best benefit form improve master data. However you need to keep the big picture in mind in order to avoid (or minimise) duplicate technology investments that may prove hard to fit together. There are no magic bullets here, but enterprise architects need to put in place the processes and broad strategy that will lead to a better master data in the long term ,even if the technology to deliver across the enterprise is only partly here today. Setting up proper data governance, and getting business people committed to it, should have real benefits and will be valid efforts whatever technologies are deployed.