Governance Profit

Data Governance requires a careful balance between the soft skills of managing people, committees, upper management and the workforce while still being able to 'get in the weeds' and provide strong analytical skills to your data model, data processes, and metadata.

Whilst so many things are going on, don't forget about metrics. You'll need to show what you've done, why your program is valuable, and where you are going. In my blog, I always stress taking notes and tracking what you do. One very tangible way to do this is with a data maturity model.

A data governance scope generally revolves around a specific set of data. What you'll do is first create a maturity model for the data. It doesn't have to be particularly complex, just something that shows the natural progress of a field from 'no governance' to 'fully governed'. Here is an example:

1 - Not in data model, no analysis, and/or no metadata
2 - Added to data model with metadata
3 - Valid values established, issues identified
4 - Issue analysis performed, resolution pending
5 - Fully governed, full analysis and issue resolution performed
6 - Data quality in place to find anomalies and violations (possibly in real-time depending on your tool)

The reason that this model will help you is because, as you begin to work through your in-scope data, you'll be able to show the progress of the domains your review. As time goes by, you'll see how the maturity of your data is progressing, and where you still have room for improvement. For more practical information on data governance, please visit my website at DataGovernanceBlog.com

For the best information on Data Governance visit Michael's Data Governance Blog at http://datagovernanceblog.com

There are a wealth of resources on Data Governance Maturity Models, Conferences, Tips, Advice and even contests for prizes!

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