Don't Waste MDM Efforts: Add Data Governance Now

Loraine Lawson
Slide Show

10 Critical Myths and Realities of Master Data Management

Prevalent myths surrounding MDM alongside an explanation of the realities.

With a toddler, a dog and an eight-year-old and her accompanying posse of friends, my house never seems to stay clean. After a while, I can't stand it anymore, and I have to do something about it. So last week, I begged my parents to babysit and spent the whole day cleaning house like a fiend.


Here's the thing: There are three able bodies living in the house - not just one. If each put his or her own stuff away and cleaned up his or her own plates and laundry, that would make my life a lot easier.


Clearly, something had to change. So that afternoon, in my newly cleaned house, I set up a job jar and a few rules about who was responsible for what. By Monday, miraculously, my house was still clean.


What's my point? It's simple. Master data management is a lot like me on Friday: You hire someone or you bring in some technology and then you clean up your data like a fiend. You get the redundancies out, you clear up any dirty data and you wind up with a set of master data, all spic and span for the usage.


MDM is so good at this, it can actually reduce data clean-up costs, according to a recent list issued by Informatica of the seven ways MDM can reduce IT costs:

By integrating data from these disparate applications into a central MDM system, it becomes possible to cleanse all data across the enterprise in a single system. This helps resolve conflicts across source applications, and makes it possible to create and store the history and lineage of any changes to the data. By centrally cleansing the data, companies can save on license and support fees for additional instances of data quality tools.

But then you realize you're not the only one using this data, even when it comes to the data stored in the MDM hub. In fact, you're not the one who made a mess of the data in the first place. Clearly, it's time to spread some of this work around before the data gets dirty again.


That's where data governance comes into play. Data governance means spreading the responsibility around, assigning people with some roles and adding a few rules about managing the data.


Master data management may do the hard work of cleaning up huge messes, but data governance is what will keep the data clean.


And, as I shared last week, experts say you can go one better by making data governance part of the business process. Just as my daughter is much more likely to remember to take out the trash if it's coupled with something she already does, coupling governance with an existing process makes it easier to keep data clean.


This logic - that MDM works better when it's coupled with governance - makes sense on the face of it, but recently it got a boost from a survey conducted by The Information Difference - and sponsored by Pitney Bowes Software - that queried 110 respondents from across the world, although the majority (71 percent) were from North America.


Sixty-nine percent felt that MDM and data governance were mutually supportive, the survey found. Interestingly, that's a higher rate than those who reported moderate success with either data governance (roughly half) or MDM (54 percent).


Also worth noting: Companies where the business or business and IT initiated data governance were more likely to report success than those where IT lead the governance initiative.


The moral of this story: If you're going to clean up your data with MDM, put data governance in place. The really smart will establish governance before MDM, but second best is doing them together, notes The Information Difference.

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