Data governance is a wide-ranging collection of processes, policies and standards designed to ensure that employees use data responsibly and effectively.

"Good data governance is imperative for governments that aim to become more data-driven as part of their digital strategy," the Organisation for Economic Cooperation and Development says. "It can help to extract value from data assets, enabling greater data access, sharing and integration at the organizational level and beyond, and increasing overall efficiency and accountability."

When done right, data governance enables many benefits.

• Improved decision-making. Well-governed data is easier to find and is usually of higher quality. Decision-makers know where to look for it, and they know what they will get—meaning they are more likely to derive actionable and meaningful insights from it.
• Reduced costs. A good data governance strategy will lower data management costs, enhance the return on investment of big data analytics projects and minimize the risk of costly data breaches. When you spend less time managing risk and errors, you can devote more of your budget and resources elsewhere.
• Enhanced data quality. Data governance strategies should aim to standardize and improve data consistency and accuracy. Quality, after all, is key to improved decision-making.
• Effective compliance. A solid platform can help you build best-in-class compliance programs, seamlessly manage audits and meet the expectations of myriad data security standards.
• Unified view. Effective data governance gives you a 360-degree view of your organization—a single version of the truth for every stakeholder to rally around. That creates continuity, clarity and confidence in data reuse and democratizes data throughout the organization, reducing your IT team's workload.

In the private sector, data governance is ultimately about driving competitive advantage. But in the public sector, it is more about creating value for citizens through improved public services, policymaking and accountability.

The bipartisan Foundations for Evidence-Based Policymaking Act modernized data management expectations. Federal agencies are now required to:

• Promote openness and publish data where possible
• Consistently inventory all data
• Put in place leadership to drive improvements

The act formalized and standardized the leadership role that chief data officers play in agency projects. And they are driving results: Nearly 75% of chief data officers reported success in their data governance efforts, even as three in five of them cited budget constraints as a serious barrier to data-driven government.

The Federal Data Strategy 2020 Action Plan set motivational guidelines for federal and federally sponsored programs, and it led to the launch of a data standards repository to accelerate the creation and adoption of relevant standards across government organizations.

If you are starting from scratch, the prospect of drawing up an effective data governance strategy might seem fairly daunting. But focusing on a few specific points can simplify things.

• Organizational alignment. Policies should match organizational goals, and they should be measured and analyzed for progress.
• Data trust. You should know where your data comes from and monitor data sources accordingly.
• Accountability. Every stakeholder should understand their responsibilities and the authority they have to make decisions about data.
• Threat monitoring and mitigation. Security is critical to data governance. Make sure you have put every necessary security provision in place.
• Transparency. The public should have access to analytics, and decision-making should be open to third-party scrutiny.
• Education and training. An effective data governance strategy requires consistent buy-in across your organization.

A Public Sector data governance policy touches many parts of your organization. Here are the key roles involved in its creation and management.

• Chief data officer. Your chief data officer takes the lead on data governance. They make sure that you manage and achieve your primary goals. They might work with a committee to establish the correct policies and procedures.
• Data owners. Data owners are senior stakeholders, and they ensure data quality and security. Each relevant part of the organization should have its own data owner accountable for the activities of the data stewards.
• Data stewards. Data stewards are junior partners to data owners, and they take a more hands-on day-to-day role in drafting and maintaining data quality rules. Where data owners are concerned with risk and access, stewards worry about correct data usage.
• Data custodians and managers. Custodians and managers physically manage or curate the data. They are usually members of the IT team, as the role requires management of the technical environment, including databases.