Rethinking Data Governance for the Digital Age


Data governance was always seen as risk management, and still is. But there’s another way, and that’s going on offense.

What’s your data strategy? Is it aggressive and innovative? Or focused on risk mitigation?

A few years ago, Tom Davenport characterized the work that CIOs and CDOs do regarding data as data offense and data defense. 

“Data offense focuses on supporting business objectives, such as increasing revenue, profitability, and customer satisfaction,” writes Davenport. “It typically includes activities that generate customer insights (data analysis and modeling, for example) or integrate disparate customer and market data to support managerial decision making through, for instance, interactive dashboards.” By contrast, “Defensive efforts ensure the integrity of data flowing through a company’s internal systems by identifying, standardizing, and governing authoritative data sources.”

In my language, defense is about all things that make data ready and safe for analysis. This includes fixing data, controlling its use, and the list goes on. Offense is about the use of data to make decisions or transform a business.

Honestly, in the past, I parked data governance solidly under defense. I’ve since learned that labeling governance this way creates a perception problem, making it harder to get the business to care about data governance. But recently, a colleague of mine challenged my thinking by suggesting that this view of data governance is too narrow and that data governance (like data management) has both offense and defense components.

Before digging into the implications of this big idea, let’s explore how data governance has changed in the last couple of years.

Changes in Data Governance Principles

Historically, CIOs hated data governance even though they considered it important (my recurring #CIOChats on Twitter brought this pattern to light). CIOs hated it because they either had to force governance top down on the organization or IT took on data governance itself. Both approaches often failed, and made the CIO feel like a villain in the process.

So, what should the purpose of data governance be? Data governance should be about the people, processes and technology that enable delivery of the right data to the right people at the right time to support data-driven decisions based upon trusted information, bridging the gap between data and the business. Effective data governance delivers data that has the following qualities:

  • Right source.
  • Right quality.
  • Certified to be trustworthy.
  • To the people who need it when they need it.
  • To make data driven decisions.

When governance is delivered in a system that captures feedback automatically, the entire governance mechanism can improve with time — and guide people to smarter data usage. New approaches to data governance are built on four principles:

 

People-first governance is the opposite of being forced. It uses intelligence to identify (1) the people that know data best and (2) the data that should be governed and how. People-first governance signals a shift in purpose; it is not about forcing people to govern data or locking people out from using it.

In many respects, the philosopher Jean Jacques Rousseau described legacy data governance when he said, “Man is born free but everywhere is in chains.” The legacy approach chained people to a limited view of governance — and restricted their behaviors. To free up data stewards, data governance needs to be built on an intelligence layer that makes it easier to steward data while making key processes autonomous. And finally, to truly improve, data governance needs to measure and monitor end-to-end performance so business objectives can be adjusted for the data governance program.

Related Article: Customer Data Management Is the Key to Consumer Trust, Profitability

Offensive vs. Defensive Data Governance

So, what does data governance that embraces offensive and defensive postures look like? Let’s look more closely at how each process differs.



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