Achieving persistent data governance, pt. 1: link your teams


During client conversations I often hear stories about past efforts to launch data governance that never reached critical mass and ended up being resized and marginalized. I find such outcomes fascinating... in the same vein as a car crash that causes you to tap on the brakes as you drive by.

When I hear stories of these failed projects, I always ask myself similar questions: How did this happen? What mistakes were made? How can I avoid this in my program? In this blog series I want to share a few thoughts on achieving persistent data governance and recommendations for avoiding a roadside emergency while on your governance journey.

There are many reasons why data governance efforts fall short of expectations, but for this series I will focus on two common issues that we see quite often.

  1. Resistance to change: People are often resistant to changing old methods, especially ones that have been followed for many years.
  2. Political headwinds: Access to data and control over its management is increasingly becoming a political flashpoint in many organizations.

It’s important to note that even with a perfect execution of a terrific governance launch strategy, it’s possible that the culture and complexity of an organization will never support successful data governance.

You see situations like this all the time. For example, federal agencies may struggle with these widespread organizational challenges. Theoretically, their data should be shared across several departments and where each team contributes knowledge that can be integrated into an MDM solution and provisioned out to the benefit of all stakeholders. But the challenge comes when you have seven or eight very large independent agencies with their own management structures, IT departments, data values and definitions. Though coordination is badly needed, it is immensely challenging to attain.

At the other end of the spectrum, some projects that seemed like a slam dunk will fail if certain steps are overlooked. For example, we might see data governance initiatives that absolutely should have succeeded and they would have if some strategic planning and risk management tasks had been included.

These avoidable failures are the programs that I will try to address by sharing three key strategies that every data governance team should incorporate to achieve persistent data governance.

Strategy #1 – Build data governance linkages with other teams

Recently, I worked with a company that is implementing new data management methods by teaming up with the business process redesign (BPR) group. They found that the BPR team had a great deal of experience in overcoming resistance to new methods and, as such, they were very knowledgeable about navigating political bumps.

External relationships can also be established with internal auditing. You’ll find this team is always interested in monitoring data accuracy of financial information. They can evaluate how business teams are progressing in terms of metadata and data quality and provide specific comments for future improvements. Building a cohesive team that’s respected across the business isn't difficult, just follow these two steps: 1) include other compliance and audit teams, and 2) work together. A coordinated approach reinforces the importance of having a culture of persistent data governance and promotes it throughout the organization.

Check back for parts two and three of my series, where I’ll offer more strategies for success that can be implemented at any organization, large or small.

Until then, what does your team do to ensure the success of your data governance projects? Have you linked your success to those of other teams? Share your thoughts!


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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