On Building a “Data Culture” in a Real Organization

January 20, 2017 Dennis D. McDonald

In How to build a sustainable, value-focused data culture, Jodi Morton and Robert Parr discuss the role of the Chief Data Officer (CDO) in financial services firms. They state that the push for improved data governance needs to evolve from being reactive and regulation-driven to becoming more integrated with both the firm’s operations and its strategic goals.

Regulation-driven data governance, they say, needs to be followed by actions that make data governance part of the organization’s culture. Based on work at Freddie Mac on evolving the CDO position, here are the data governance actions they recommend:

Embed in the business (e.g., locate data governance in the business not in IT).
Cultivate strong partnerships (e.g., work with both IT and business leadership to generate early wins involving improved analytics).
Focus on strategic value alongside control (e.g., make sure that data are not only controlled but provided in useful ways to individual lines of business).
Examine the entire “data supply chain” for benefits (e.g., don’t let individual “siloes” block a comprehensive view of how data “flow” through the organization).
Transformation requires constant, consistent communication (e.g., make sure that people speak the same language about data no matter where they sit in the organization).
100 wins in 100 days program (e.g., deliver data-based value to customers early and often).

These points are consistent with what I have found in my own research and consulting on data program planning and management, i.e., the need to align data analytics with business goals, coupled with the need to provide both tactical and strategic value from improved analytics.

What will vary from organization to organization will be the manner in which data governance and data analytics services are managed. The CDO has to have the authority, responsibility, and resources to manage all the necessary development, services, staff, and support associated with key points in the data supply chain.

One important issue is what ongoing services have to be provided to make data and analytics useful, given the mix of analytical skills of those who can use the data. This should be driven by a consideration of what problems we are trying to solve with the help of data. As I suggested in How Much "Data Science" Do You Really Need?,

We need to make sure that our "data science" team is focusing its energy -- and technologies -- on solving the right problems. That means understanding and working with management to accurately assess the "pain points" being experienced by the organization. We also need to understand and plan for the ongoing resource requirements of managing the team that reliably addresses these pain points.

Morton and Parr’s list of actions does -- if implemented -- help ensure that data analytics will address those “pain points” while ensuring that the underlying work to provide those services is sustainable.

Interestingly, the question of technology is not being raised in this discussion. Instead, we’re focusing on management, policy, and organizational issues.

That’s appropriate. Technology is secondary. While there’s little question that new data management and analysis technologies will enable services that were impossible to provide in years past, effective data governance depends primarily on effectively addressing what’s important to the organization. That's somoething that leadership must determine.