Master Data Management Can Learn from Data Quality
Blog by Philip Russom
Research Director for Data Management, TDWI
For about a month now, I’ve been interviewing users on the phone, in search of speakers for upcoming TDWI events. I need speakers who can share their organization’s best practices and strategies for data management. As you can imagine, I’ve heard a lot great tips in these interviews, many of them concerning master data management (MDM).
A tip I’ve heard from people in multiple organizations is that MDM solutions achieve a higher level of success when they adopt some of the techniques and best practices of data quality (DQ). Let me give you some examples of DQ practices applied to MDM. DQ techniques
. For years, I’ve watched data integration solutions incorporate functions that originated with data quality tools, especially data profiling and data monitoring. In a similar trend, I’m now seeing MDM solutions incorporating DQ functions for data standardization, deduplication, augmentation, identification, and verification. After all, master and reference data benefits from these functions, just as any data domain would. Data stewardship
. DQ success usually depends on the processes of data stewardship. A data steward plays a key role in linking data quality work and standards to specific business goals and business applications. The average data steward can identify and prioritize DQ work that will yield a noticeable return for the business. I’m now seeing a similar stewardship approach to prioritizing MDM work. Collaborative data management
. Note that a steward’s priority list is only accurate, when developed in conjunction with business managers who know the impact of data’s quality on the business. Likewise, data stewardship can be a process for IT-to-business alignment and collaboration in the context of MDM, not just DQ. Data governance (DG).
I’ve seen a number of organizations take a successful data stewardship program (originally designed to support DQ) and evolve it into a data governance program. You see, a good data stewardship program will establish a process for proposing and authorizing changes to data and applications for the sake of improving data’s quality. A DG board or committee needs a similar process for the data standards and data usage policies it has to create and enforce. In fact, the first policies produced by a DG program usually govern data via quality rules. And a typical “next step” that a DG program takes is to apply said process to data standards and usage policies for MDM. Change management
. DQ and MDM share very similar goals, in that each strives to improve data, whether the data domain is master data, customer data, product data, financial data, etc. Achieving improvement almost always requires changes to data, applications, and how end-users use applications. Therefore, a change management process is key to effecting improvements. DQ has long standing change management processes via stewardship, plus new options for change management via data governance. MDM’s likelihood of effecting positive change is increased when it taps the data-oriented change management processes that evolved from DQ and stewardship. Conclusion
. Frankly, I’m not surprised that MDM solutions are absorbing DQ techniques and best practices. I’ve seen a similar absorption by DI solutions, going on for about ten years now. And I already mentioned how some data governance programs are essentially data stewardship programs, expanded into a data-standards-oriented form of data governance. So, it’s clear to me that a variety of data management disciplines can learn from DQ techniques and stewardship practices. And the discipline going through that cycle right now is MDM. You should follow this trend, if you’re not already.
So, what do you think, folks? Let me know. Thanks!
Posted by Philip Russom, Ph.D. on September 8, 2011