Achieving persistent data governance, pt. 2: focus on trouble areas

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In the first part of my series on ensuring data governance success, I mentioned the importance of linking different teams. Collaboration is an often overlooked, but critically important, part of having a successful project. Not only that, coordination and cooperation helps to create the right culture of data mindfulness throughout the organization.

In this week’s tip, I want to expand on that concept and apply it to teams that might be especially difficult to reach and get to work together.

Strategy #2 – Focus on business areas that do not cooperate very well.

Although it may sound counterintuitive, one of the easiest ways to deliver value to an organization (and gain executive-level recognition) is bridging the gap between two groups that do not cooperate with each other but have a directive to share data in daily operations.

The groups’ dysfunction has more than likely led to conflicting approaches in data definitions and data provisioning, which may have resulted in subpar data quality at the enterprise level.  Such discord limits the organization’s customer service capability, inhibits efforts to leverage analytics in business decisions and generally degrades the value of data assets.

Effective data governance can benefit the organization in two key ways.

  1. Data governance can provide resources needed to capture data definitions, negotiate and rationalize differences, and document persistent gaps so they are understood.  Once each team understands the enterprise definitions, they can proceed with new initiatives with broader knowledge and understanding.  Going forward, the data governance team will also monitor changes to the definitions and provide notifications to interested parties.
  2. Second, the data governance team can establish a data quality monitoring and communications process to bridge communications gaps that may exist.  After initial efforts are established, next steps can include workflow notification for data remediation, and may eventually evolve to include an MDM platform.

By rationalizing definitions and implementing a data quality process, you'll gain better cooperation and efficiency in your organization. That’s something that executive leadership will notice. Especially if your teams were historically struggling to work together effectively. In no time, your work will have a big impact. And maybe even a banner in the breakroom that says “Kudos to Data Governance!”

Check back for the next post in my series, where I expand on how you can select the best leader to guide your project to the finish line.

In the meantime, what do you think are common pitfalls of a data governance initiative? Share your thoughts below. 

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About Author

Bryan Finnegan

Sr Technical Consultant

Bryan Finnegan is a consultant at SAS in the data management practice where he has worked for the past two years. His focus for the past 22 years has been assisting clients wishing to improve data quality, and reduce risks related to suboptimal data accuracy. He is an expert in driving change within large complex organizations, and creating a vision and executive support for new data management methods. He has delivered advisory services that include development of data architecture framework for a large bank, a data quality assessment and roadmap for a large federal agency, and an MDM implementation plan for a national retailer

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  1. Pingback: Achieving persistent data governance, pt. 3: find a visionary - The Data Roundtable

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