Dirty Data Practice No. 3Data governance policies are set—no worries ever again.
Data governance is often begun in conjunction with a specific data warehouse or BI project. However, if you think of data governance as a "project," your efforts are doomed. Successful data governance depends on a long-term commitment from the business at large to both the technological and cultural foundations.
Clean It UpEstablish a culture of data governance.
Ongoing training and key milestones that measure data governance's benefits can help keep quality control on users' radar. Successful data governance also depends on dedicated sponsorship from someone in top management. Charlesworth says the CIO is often the perfect person for the job due to a CIO's likely combination of forward-thinking and a focus on efficiencies around process, money and technologies. Some companies even create a C title specifically for the position, such as chief data officer or chief data steward.
Dirty Data Practice No. 4You let red tape suck the life from your efforts.
Charlesworth says many data governance efforts fail to show positive change, and instead stall in meetings and bureaucracy. But if you don't focus on action and demonstrable wins, users won't feel the positive benefits firsthand, making user commitment unlikely.
Clean It UpDeliver quick wins.
To get user buy-in and commitment, you must create, demonstrate and internally market the positive changes won through data governance. For example, one measurable benefit to focus on initially could be improving validation of order entries to reduce errors.
Dirty Data Practice No. 5You make ROI the be-all and end-all.
Can you accurately isolate investment benefits and attribute them to a particular project? In today's multifaceted, complex business environment, this is not likely, says Charlesworth. Calculating ROI on a particular investment assumes that everything else in the business either stood still or had no influence on the benefits, he says.
Clean It UpCreate a clear picture of success.
Charlesworth recommends looking to other metrics such as internal rate of return (a measure of an investment's efficiency or the rate of growth a project is expected to generate) and economic value added (estimate of true economic profit). However, the most important thing isn't the calculations per se, it's the discussion around defining success—what it looks like and how you know when you have it, says Charlesworth. This is especially important in terms of measuring value of data governance at various phases and levels of granularity to make sure you stay on track, and, if not, making corrections. His examples of such metrics include a data quality dashboard that displays the accurateness of data processing, data consistency and reuse of rules/measures, and project-specific metrics such as standardization of product master data elements.
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