Does data governance make a difference? Part 1


I was recently asked to participate in an architecture review for a master data management project, and as a participant I was provided with a set of documents that had been used in promoting the program to potential users of the master data system. Those documents implied that data governance was to be part of the program, although I did not see specific evidence that supporting data governance had been considered as a significant component of the system architecture.

In addition, during the conversations, I noted that my suggestions about some specific data governance tasks (such as end-user requirements solicitation, management of semantic and structural metadata and integrated data quality monitoring) were perceived as “soft” aspects of the program as opposed to the discrete and “hard” aspects such as master data architecture, ETL and system performance.

However, after reviewing my notes after the set of meetings, I realized that a large number of the issues they were concerned about resulted from the absence of governance.

For example, because the end-users were not engaged prior to designing the master data model, the IT group inferred what they thought should reside in the master model, creating a very wide table with many combined data attributes. In turn, the time it took to extract, transform, parse, standardize, match and then load the data was very long because of the sizes of the records. Isolating this as performance issue triggered a conversation about ways to adapt different ETL tools to try to speed up the process. I would contend that reducing the width of the master data table by eliminating non-required data elements would reduce the data latency associated with extraction and then with loading, which might alleviate the performance issue somewhat.

I guess that suggests that the title to this entry is really a rhetorical one – of course I believe that data governance makes a difference when applied as a preamble to design and implementation of a system. This is especially true for enterprise initiatives like MDM (and ERP or CRM and other acronymized systems). More on this next time…


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at

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