Just when you thought it was safe to catch your breath and hunker down with your general purpose DBMS data warehouse, data and business requirements are escalating everywhere past the point where this strategy is tenable.  The market has filled this gap nicely over the past few years, providing us with a plethora of alternatives - none of which would seem to me to indicate a continued allegiance to a single platform enterprise data warehouse being the elegant sole end game.  Database specialization is upon us and the primary determinant of an end client's portfolio is the workload.

End clients face an array of possibilities today to accomplish common objectives of getting leaner, more agile, becoming more real-time and reducing costs.  The general purpose DBMS, hosted and supported in-house, will, of course, remain an anchor of operational and post-operational environments.    Multidimensional OLAP will also continue to play a key role in many environments.  Playing on the edges now in many environments are data warehouse appliances.  Some have made it their enterprise data warehouse platform.  There are memory resident DBMS.  There are DBMS that store data in columns, instead of rows.  Actually so many "analytic" specialist DBMS dot the platform today, such as NeoView, Aster Data, Exadata, ParAccel, and DB2 BCUs, garnering so much interest, that it would be unusual for a shop to not be considering one or more now.

All of these categories are not mutually exclusive!  There are appliances that are columnar, memory resident and hosted in the cloud.  Go figure.  It's important to know the building blocks that solutions are made of, ensure they are all compatible with your workload and goals, and not look solely at how the platform delivers a single application.

Forming and interacting with Master Data Management teams is interesting because the people tend to come from one of two worlds.  While each world is legitimate, the best team will comprise the best of both worlds, if not in experience, at least in perspective. 

These worlds I'm talking about are the post-operationally-minded information management professionals and the operational-systems-minded professionals.

On the one hand, MDM does comprise quite a bit of traditional information management - the data modeling, the data quality programs, the data profiling, the data integration - all familiar to builders of data warehousing. 

On the other hand, MDM is operational and will interact in real-time, not batch, with other operational systems.  MDM will utilize governed workflows to bring together its data and will need to be keenly aware of operational issues and operational data needs.  Changing the data so suit analytics is not always best.

While the perspectives are not mutually exclusive, the skill gap to what is needed can be astounding if teams are formed, and therefore opinions of MDM formed, exclusively from one camp or the other.  Make sure your MDM program achieves the right balance of skills and focus.  Take inventory.  This can be done through consulting and education, as well as conscious effort towards balance and achievement in both operational and post-operational issues.