I've written stories in the past about the two flavors of master data management-analytical, or and operational. These are both terms you'll hear frequently, and back in 2009, quoting a 2006 Intelligent Enterprise article, I summed up my understanding of the two thusly:
...operational MDM synchronizes master data, including product or customer information, across the company's transactional applications, while analytical MDM reconciles data "drawn from a variety of sources to deliver integrated, consistent business intelligence across the entire business."
I didn't realize that Gartner analyst Andrew White coined the term "analytical MDM," but in a recent post, he fessed up and shared what happened to one IT division that essentially opted for analytical MDM and ended up with a nightmare scenario. It's a morality tale that should give pause to those of you who might be opting to try to fudge your way through situations where master data management would be the best solution.
Actually, at first, the post is a bit confusing because he starts out explaining the concept of analytical MDM and how it differs from operational MDM. He predicates all of this on a common assumption: Analytical MDM isn't actually a real thing - "Given that we can all agree (really?) that there is no such thing, literally, as 'analytical MDM'" - and then proceeds to explain how to do it.
After several reads, I think what he's saying is that this isn't MDM, but it does seem to accomplish the same thing -- give you a single view of the data. The problem is, that's happening downstream, in the business transaction and operations applications, rather than upstream, in the applications. It's done with passive governance, through rules and transformations, rather than active governance based on policies and business users.
In other words, it's like a high-tech sleight-of-hand, making it appear the data agrees for the purpose of your BI tool, when in fact, the disparate data still exists at the sources.
This is where our MDM story begins. Enter a Frugal IT division at a global media/marketing organization. Our heroes are charged with rolling out a global business intelligence project.
This requires our heroes to synchronize the data, by the company doesn't have a full-fledge MDM project. So, our hereos do what White calls a "passive" data governance program that basically put IT in the role of data steward over the BI data warehouse.
The plan was for IT to manage the data so as to get "some semblance of 'single view' without all the cost and overhead of an operational MDM program in the business," writes White.
Do you sense a plot twist coming on? You should.
Three or four years into the project, enter the business stage left. The business is now interested in a full MDM program and wants to be actively involved in governing the data. Sounds great! Just scale the analytical MDM and yippee!
But soft-what light through yonder window breaks? It is the dawn of realization, in which IT finds out its approach doesn't scale.
The plot thickens. Writes White:
The organization was pulling their hair out since they could not figure out how to support an operational MDM program with the tools they were implementing. Bottom line- they can't. They selected tools and technology for use downstream in BI (they fell for the vendor line) and their 'analytical MDM' vendor/technology of choice is ill-suited to support a fully fledged operational MDM program.
I'm sure that IT department would love to wake up and find it's all been a bad play, put on for your amusement. Alas, it's not. The group has lots of re-work ahead, writes White.