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.

Poor data quality leads to poor decisions and recommendations. When the data is bad, you can't make decisions ahead of the market and ahead of the competition—or worse, you make the wrong decisions. According to Hennig, data governance helps ensure data quality and prevent chaos in an organization.

"Without frameworks, people tend to protect their data," she says. "When there’s no sharing, there are no use cases that span the value chain. If you're not able to open data silos, you’re not able to harvest the benefits of the data across your company. The biggest value comes when you can implement end-to-end use cases—combining manufacturing with sales forecast planning, for example."

Another important end-to-end use case is sustainability, which requires the first three scopes of greenhouse gas (GHG) reporting: scope 1 is on direct emissions from sources owned or controlled by an organization; scope 2 is on all indirect emissions resulting from an organization's energy consumption; and scope 3 is an account of the emissions across the supply chain.

"All three require you to look across the value chain," says Hennig. "Not only do you need to look at data within your company, but also outside your company, with suppliers and customers. You can’t do this if you have data silos."

But most of all, Hennig says, organizations need to be clear about what problem they want to solve before setting up data governance. The goal should be to deliver business value.

How does your framework help teams work together?

Jennifer Trotsko, who founded the data governance function and later the privacy function at the International Finance Corporation (IFC), the private sector arm of the World Bank Group, was greatly influenced by Gwen Thomas' work.

"We developed our own framework based on DGI components, along with other leading benchmarks," says Trotsko, who went on to become the Head of IFC’s compliance risk function and chief privacy officer. With the foundation in place, IFC was able to coordinate activities across teams. And by using a common language to communicate about everything from policy and rules to technology and processes, each part of the organization could reference the framework and contribute to the overall end state.

"After establishing a project’s business value, the first thing we did was map tasks to our in-house data governance framework," she says. "By assigning a lead in the policy area, another for working with technology, and another for change management, the project had clear guardrails and milestones. This allowed the core team to manage across dozens of departments, and it was the framework that provided confidence to stakeholders that all important components were covered. In short, leads focused on execution, knowing we all had a shared vision for the overall effort.”  

Trotsko is now privacy program manager at the International Monetary Fund (IMF), where she continues to build and adapt the DGI data governance framework, which she says is "invaluable in managing large projects involving data collection, storage, and analysis."