The goals of data governance are unimpeachable: consistency in how corporate data is defined, managed and used. Throw in some related data quality processes, and perhaps a master data management initiative, and you have the ingredients for clean, accurate and uniform information across an organization. Alas, getting the recipe right isn't so easy; data governance programs in particular are prone to problems and even outright failure.
To help you avoid a governance effort that leaves a sour taste in everyone's mouth, we've published a Q&A with Michele Koch, who leads the data governance program at Sallie Mae. Koch details the student loan provider's approach to data governance and offers her take on governance best practices. Two of her keys to success: meetings (at the outset of the program) and metrics (for measuring its progress).
Earlier this year, Koch was one of several data governance professionals who took part in a panel discussion at Enterprise Data World 2011. There was more talk of data governance best practices that day, and we were on hand to report on what the panelists had to say about topics such as the importance of zeroing in on the business problems that governance can help address and the role of data stewardship in a governance program.
On the other side of the coin, there are things you don't want to do as part of a governance initiative. We asked consultant Rick Sherman to write an article on the top 10 data governance worst practices that organizations should avoid. Assuming that technology alone is the answer is one; trying to do too much, too soon is another. See what else made Rick's list.
What's your take on how to make data governance work? Feel free to send me an email on that or other data management topics.
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