Operational vs. analytical MDM

0

mature businesswoman considers two different types of MDMIn my last post, I described how organizations are starting to use master data management (MDM) applications for more analytical purposes.

Today I’ll define and differentiate the two different types of MDM: analytical and operational.

Analytical MDM

Analytical MDM has replaced the term customer data integration (CDI), at least in the customer domain. To state the obvious, that term never applied in domains such as product, employee, patient, vendor and the like.

Although it serves the same general purpose as operational MDM (more on that below), the specific goal of analytical MDM is a bit different. At its core, the objective is to deliver clean, comprehensive and consistent master data to downstream systems. Here I’m talking about data warehouses, data marts, cubes and business-intelligence applications.

I asked noted author and MDM expert Dalton Cervo about how these two types of MDM play with one another. As he wrote in my second book The Next Wave of Technologies:

Analytical MDM is the quick-hit approach. While organizations can quickly make a tremendous impact with respect to reporting and BI, with the analytical MDM approach relatively minimal inputs yield corresponding outputs. Specifically, organizations fail to harvest the benefits of the MDM services back to their operational data. What’s more, by relying on a data warehouse, the analytical MDM approach
does not enforce any regulatory or audit requirements. [Emphasis mine.]

Although exceptions exist, organizations have tended to adopt analytical MDM before its operational equivalent.

Operational MDM: Greater risk?

Writing for Gartner, Andrew White notes that “operational MDM centers on ensuring a ‘single view’ of master data in [core]systems that employees use.” Why these systems? Because this is where master vendor, customer, product and employee data is “born.”

Unlike analytical MDM, operational MDM ties together critical enterprise applications predominantly CRM and ERP. As such, the latter is inextricably intertwined with data governance and data stewardship. Put differently, operational MDM needs to deal with issues such as security, privacy and regulatory compliance.

These issues are paramount in operational MDM but not in its analytical equivalent. Even a few errors in each can be problematic, but a mistake in the former can do much more than corrupt other transactional and reporting systems. For instance, think about the consequences that a compromised operational MDM application can cause. Here I’m talking about far more than errant KPIs or reporting errors: potential liability or HIPPA fallout is enough to scare any CIO.

Simon Says: Organizations can benefit from both types of MDM.

Taken together, organizations employing both types of MDM can reduce costs, manage their risk and grow their revenue.

Feedback

What say you?

Download a strategic guide to data governance and MDM
Share

About Author

Phil Simon

Author, Speaker, and Professor

Phil Simon is a keynote speaker and recognized technology expert. He is the award-winning author of eight management books, most recently Analytics: The Agile Way. He consults organizations on matters related to strategy, data, analytics, and technology. His contributions have been featured on The Harvard Business Review, CNN, Wired, The New York Times, and many other sites. He is a full-time faculty member Arizona State University’s W. P. Carey School of Business (Department of Information Systems). He also runs 5marbles, a Agile software-development shop.

Leave A Reply

Back to Top