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The “use-cases” of MDM

by Andrew White  |  October 1, 2009  |  Comments Off on The “use-cases” of MDM

A few years ago we introduced the notion of MDM “use cases”.  After looking at hundreds of end user inquiries we spotted some patterns; some were clear and some were less.  One very clear pattern is what we called, “operational MDM”.  The common characteristics that describe this use case were (and still are):

  • identify erroneous master data that need resolution (in order to yield “single view”)
  • explicit intent to clean up master data at source
  • resulting output (clean master data) is used in any and all business applications used by line of business

The second item could be said another way: explicit intent to clean up the business processes that crate the bad data in the first place.

As such, operational MDM involves business users in real work (over time, less then what had been executed before MDM) to resolve issues with bad master data.  Process chances result in possible changes to application strategy; changes have to be made to legacy and packaged applications that hitherto fore had not awareness of the MDM discipline.

A slightly weaker pattern related to MDM “use case” was visible in Business Intelligence (BI) land.  When IT builds a data warehouse to support BI it has to identify data sources, and that data is gathered together (federated), and cleaned up (merged, transformed) and stored in the data warehouse – on which all manner of analysis is performed.  Mechanically, some of the cleaning routines and rules generated from them, look and smell like the same as those that would be used in operational MDM.  But, there were also some differences, and this is what helped us create the name for this next pattern (analytical MDM).  The common characteristics that describe this use case were (and still are):

  • identify erroneous master data (among all the other data targeted for the data warehouse) that need resolution (in order to yield, in the data warehouse, “single view”)
  • no desire to clean up master data at source
  • resulting output (clean master data, and all the other data to be stored in the data warehouse), are not used in any business application, only in the BI applications using the data warehouse.

What gets real interesting with analytical MDM is the third bullet.  In the last few years more and more applications have been built on this “BI data warehouse” that create new data; and this data has to be stored, managed, and governed, like any other business data.  As such, this “BI application” behaves like a business application.  This is important.  We need to keep things simple, and re-define things.  A BI application is one that is used for reporting and analysis only and does not create any new business data; a business application may also have analytics, but it does tend to create business data.

For BI applications, analytical MDM is adopted.  For BA, operational MDM is adopted.

There are at least two other use cases for MDM – but they are much weaker to discern.  I will follow up on those in another post.

See you next week at Gartner’s MDM Summit!

Gartner MDM Summit

Category: analytical-mdm  operational-mdm  

Tags: analytical-mdm  operational-mdm  

Andrew White
Research VP
8 years at Gartner
22 years IT industry

Andrew White is a Distinguished Analyst and VP. His roles include Chief of Research and Content Lead for Data and Analytics. His main research focus is data and analytics strategy, platforms, and governance. Read Full Bio




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