3 powerful lessons of using data governance frameworks

In addition to the data it collects and generates from public sources, the DOC also buys or licenses data from the private sector and uses it for things like economic analysis. “The challenge is that when you take data from outside sources, you have to normalize the data to make sense of it,” says Wise.

Structuring the data and tracing the source are just two of many important aspects of data governance that are carefully considered by the DOC. The Data Governance Board, chaired by Wise, addresses data management and data policy issues for the wide range of agencies that make up the department.

“We have different data governance frameworks for different needs,” he says. “In all cases, the definition of any of the frameworks needs to be a collective effort, so all stakeholders feel they’re being heard. If you do that, everybody is more motivated to use the framework, which will ensure consistency in data management.”

What is the goal of data governance?

Hanna Hennig, CIO of Siemens, says she has seen business units start collecting data without knowing what to collect and why. “It was always a waste of money,” she says. “If you don’t know what problem you want to solve, then you cannot define your data strategy.”

To find out what data you need, start with a clear definition of what you consider to be the desired business outcome. Whether it affects the top-line, the bottom-line, or both, the desired business outcome will drive decisions about which data you collect. Once you identify the data, you can start defining your data governance framework.

The framework should answer questions, such as who owns each data asset, the role of the owner, and how you ensure the data is curated and qualified for use by the technology across the business. If the data is correctly curated and formatted, it can be used by data analytics and, in particular, AI to make recommendations that help an organization make decisions ahead of the market.