I recently attended a three-day training class on one of the leading data quality products. It got me thinking about the symbiotic relationship between master data management (MDM) and data quality.


MDM without a robust approach to data quality can be dangerous. We’ve all heard the IT cliché “garbage in, garbage out.” But that is very true of building an MDM hub solution. You’re literally at the mercy of the worst data entry person in the company as you gather information from any number of source systems to feed into your new hub. If you don’t have a strong filter for what goes into the hub, both during the initial load and during day-to-day operation, you’ll quickly be fielding complaints from businesspeople around the company on the quality of the information coming out of your hub.


So it’s pretty obvious that there’s a strong data quality component to an MDM initiative. You’ll probably want a batch-oriented data quality capability for:


  • The initial load from each source system into your hub,
  • Nightly or weekly batch updates to the hub from the source systems,
  • Periodic checking of data quality levels in the hub and
  • To process monthly or quarterly feeds from outside information providers like D&B.

A real-time component that can check each record one by one is helpful too. Whether you’ve got a centralized data stewardship team creating all of the new records and managing all of the updates or you’re still allowing end users in all of the individual source systems to add and change records and integrating new and modified records back to your hub, a real-time data quality component will ensure that data quality levels stay high after all of your hard work to fix the data.


One of the biggest drivers for MDM, in my experience, is mergers and acquisitions. Inevitably, the acquiring company needs to integrate the customer, product, supplier and other important master data into its enterprise systems. Rather than merely using a “load-and-pray” approach, I am starting to see very acquisitive companies using the principles of MDM and data governance as part of their data conversion approach for acquired companies. Data quality tools are a great fit here.


Nearly every acquired company is going to have some overlap with the acquiring company’s core customer base. And the data quality of the acquired data is suspect, because in many cases little is known about the source systems or applications of the acquired company (and what acquisitions and data conversion they did) prior to the aquisition.


Think about how to integrate a good off-the-shelf data quality tool with your MDM platform. Most MDM vendors have either chosen to build their own data quality capability, to “OEM” (Oracle Enterprise Manager) someone else’s data quality tool or to integrate tightly with a variety of different third-party data quality tools.


Depending on your requirements, how your MDM platform handles data quality is ultimately going to be a big question.So, start looking into it early on in the MDM evaluation and selection process. Keep in mind that data quality, like many other things in life, is only noticed when it’s missing.


The business owners and end users around the company won’t understand or appreciate the care it took to build a robust data quality strategy into your MDM initiative. However, they will sure notice and come find you when something is wrong. Save yourself some headaches, and plan for it from the start.


What would that look like? Start by evaluating which data quality tools your potential or current MDM platform can work with.Whether your MDM hub has a built-in data quality tool, one integrated on an OEM basis or it integrates with a variety of data quality products, you’ll want to start by knowing what your options are.


Second, begin socializing the idea that you’re going to take a hard look at data quality very early in your MDM initiative. When people start saying “Well, I don’t know if that’s necessary, our data is pretty good, I’m sure,” gently agree with them and suggest looking at the data with a data quality tool just to confirm how good it is.I’ve never seen a business unit or source system where the low level of data quality didn’t surprise and scare the business owners, once they got a good look at it.


Third, after profiling the individual source systems, plan to use the selected data quality tool as a staging area and filter for loading the MDM hub.Define strict business rules around the values that the hub will accept.A “single view of the customer” (or of products, suppliers, employees, etc.) loses a lot of its enterprise value if the data is suspect or wrong.


Finally, incorporate the data quality tool into the ongoing life of your MDM initiative.If you don’t have a plan for maintaining data quality, it will inevitably degrade over time.Plan to use the same set of business rules and algorithms to periodically reverify the data and cleanse every individual new record as it goes into the hub.


Data quality, in many ways, is the engine that will drive the success of your MDM initiative. The data stewards will be accountable for maintaining it; and for them to be effective, you have to give them the proper tools. Be sure to document and communicate the “business wins” that result from improved data quality (and their dollar value). Good documentation of how you’re achieving your expected ROI and a clear communication plan for getting the word out to the business are vital to securing and maintaining your program’s funding for the next year and beyond.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access