Setting the stage for MDM: some definitions

Master Data Management or MDM is everywhere these days. Executives have heard how MDM is going to save their organizations by revolutionizing how companies deal with their data, and making them more agile, competitive, and successful.

I'm not arguing with that at all. I do believe MDM is capable of achieving all what has been said if done correctly. But it seems like there is quite a bit of disparity to what people call MDM. I've seen organizations simply doing a Data Integration project and calling it MDM. Granted, Data Integration is often enough an important step to getting to MDM, but it is not an MDM per se.

With that in mind, I think it is time to set the stage and have some definitions. I say we need to understand the MD (Master Data) part first, before we can define the second M (Management).

Master data is information that is key to operational and analytics/reporting aspects of business. This key business information may include data about the following entities: customers, products, suppliers, partners, employes, materials, etc. Master data is often non-transactional in nature, but it supports transactional processes and operations, as well as business intelligence via analytics and reporting. Master data is normally used by multiple functional groups and stored in disparate systems across the organization. Since it is commonly stored in siloed systems, the possibility for inaccurate and/or duplicate master data exists. Simply put, master data is that persistent, non-transactional data defining a business entity for which there should be an agreed upon view across the organization.

Notice the two distinct aspects of Master Data: operational and analytics. From that definition, one may say that an MDM project not addressing both aspects is not truly MDM. I'm not that extremist. However, I like to use the following terms to distinguish what is being addressed: Operational MDM, Analytical MDM, and Enterprise MDM. These are not new terms – that's right, I won't take credit for them. I have seen white papers using those terms (sorry for not providing appropriate credits, but I really don't have links to those documents anymore). I'm just surprised that those terms are not used more often to help distinguish what MDM approach in being implemented.

Which one should you implement? You guessed it: it depends on what you're trying to accomplish. Historically, Analytical MDM, implemented in Data Warehouses, has been the most common MDM approach adopted by many organizations, mostly due to its low impact to the company's operational systems. This is still where most of master data is managed today. That is not saying it is the right one. As a matter of fact, most would argue that's not the appropriate solution to manage master data. But that is a topic for another posting.

Next diagram depicts the level of intrusiveness of each approach. This picture is not suggesting phases to follow when implementing MDM (another posting).

To complicate matters even more, there are potentially multiple architecture definitions for each of the three approaches. You guessed it again: more postings to come! I'm getting tired, but I hope you're not!!