The best data governance is both invisible and transparent


In a previous blog, I wrote about the top ten fallacies of why data governance is perceived to be too burdensome and costly. Hopefully I dispelled the preconception that data governance is slower and less nimble than today's informal data management practices. In this post, we'll examine the concept of governance as an “invisible hand.”

The invisible hand of the market is a metaphor conceived by Adam Smith to describe the self-regulating behavior of the marketplace through the collective action of individuals to maximize their individual gains. In a similar way, well-designed data governance can serve as an invisible hand on data sourcing, quality and delivery. Many constituents serve to gain from better documentation and data standards. The key difference here is that transparency – e.g., that ability of all end-users to understand the process and gain accountability for data decisions – should actually facilitate the program’s invisibility. So the balance of transparency with invisibility should be the end-result of efficient data governance.

Let’s think about good civic governance in day-to-day life – traffic lights, stop signs, snow removal and public safety. Most of us take these services for granted and assume they will happen as needed or on a regularly scheduled basis. Without commotion the city council will debate and resolve issues put on the agenda. Policy is developed that most of us know nothing about – nor do we want to.

Good governance does not impede our ability to get our jobs done; rather, it enables us to go about everyday life with fewer hassles. We need to be able to focus on doing our jobs, not making up for the lack of accountability or structure that supports a civic structure. Who patches the roads? Who changes the streetlights? Who decides whether a standard stair-step is 11” or 9”? When was the last time you noticed that a stop sign had been replaced? Who cares?! Governance should be invisible (at least non-intrusive) to be effective. Our investments in good governance should deliver smooth-sailing in our ability to do the jobs we are tasked with, not someone else’s job that we don’t know how to do.

As a citizen, I need to be able to identify who to contact in case my sidewalk cracks or a tree falls on my power lines. I need to know how long it will take for the issue to get resolved. This is where governance requires transparency. For me to participate in reporting significant events that require attention, I need at least some understanding of the process, and it would be nice to be able to track where in the process my issue is in terms of priority and impact. From a data governance perspective, as an end-user of enterprise data I need to know who to contact in case of a data quality issue or a source data change. I need to understand how and whether the issue will be escalated in order to reach an appropriate level of resolution given others who are impacted by the same issue. But I also need to get my job done while the issue gets resolved.

Data governance is a set of processes, decision-rights and escalation points formalized to drive out redundancies, conflicts and rework. Investment in data governance is designed to counteract the currently unmeasured but certainly existent data churn and fire-fighting that accompanies one-off fixes performed on a recurring basis. Clients routinely report spending anywhere from 40% - 60% of business analyst time on resolving data quality issues or ensuring that the data is trustworthy – usually because metadata and source data documentation do not exist or are not maintained. Security policies either don’t exist or can’t be located. New source data systems don’t comply with enterprise standards and may result in failure to run automated models and reports – or worse: in-system crashes.

When performing ROI analyses for companies that are considering an investment in data governance, these are some of the most significant cost-savings factors. The fact is, a successful data governance program, like all good civic governance, should be almost entirely invisible, but transparent enough to be effective.


About Author

Carol Newcomb

Carol Newcomb, SAS Information Management Consultant

Carol Newcomb has 25 years of experience in information management, particularly in the healthcare industry. She specializes in the design and implementation of data governance programs. Carol has worked with the Department of Education to design a long range data strategy and has designed data stewardship and broad organizational training materials to ensure ongoing program success. Prior to SAS, she held positions at The Joint Commission, Northwestern Memorial Hospital, Henry Ford Health System, and UHC. She is the author of the SAS E-book “When Bad Data Happens to Good Companies” and has written numerous blogs and white papers, including “Implementing Data Governance in Complex Healthcare Organizations.”

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