The Four Forces Shaping Government Data Strategy

Across the public sector, data is fast becoming the most valuable—and the most vulnerable—asset government leaders manage. Yet as agencies modernize their technology stacks and embrace AI, they face an increasingly complex challenge: how to govern data in a way that’s ethical, secure, and future-ready.

A new white paper from the Center for Digital Government (CDG), Navigating the Future of Data, highlights how states and localities are responding. The findings point to four powerful forces that are reshaping how government leaders approach data: governance, ethics, infrastructure, and operationalization. Together, these forces form the foundation for trustworthy, data-driven government.

1. Governance: Building the Framework for Trust

For decades, data governance has been the invisible scaffolding of government operations—important, but rarely a policy priority. That’s changing. According to CDG’s research, data governance now ranks among the top ten priorities for both state and local CIOs, reflecting a growing recognition that data quality, consistency, and accessibility underpin every modernization effort.

Yet challenges remain. Nearly 80 percent of states still lack a formal data quality program, and many Chief Data Officers work without dedicated teams or consistent authority. The result? Fragmented policies and inconsistent practices that limit innovation and erode public confidence.

Forward-looking states like Utah are leading the way. By passing the Government Data Privacy Act in 2024 and adopting a unified approach to governance, Utah is setting a model for others: establish statewide standards, clarify ownership, and treat data as a shared public asset. These moves signal a shift from isolated data management toward coordinated, transparent governance capable of supporting AI-enabled initiatives.

2. Ethics and Privacy: Balancing Access with Accountability

The second force shaping government data strategy is ethics. As agencies adopt AI tools to analyze and automate processes, questions of transparency, bias, and data privacy are rising to the forefront. CDG’s report notes that many existing governance models were designed for the private sector and fail to account for the unique accountability demands of government.

Jurisdictions like Hawaii, New Hampshire, and Ohio are pioneering new approaches that combine privacy-preserving technology with adaptive policies. Hawaii allows individual agencies to control access to their data sets while exploring a statewide analytics platform to enable secure sharing. Ohio has created a tiered-access model that promotes transparency while safeguarding sensitive information. The message is clear: ethics isn’t a barrier to innovation—it’s the foundation for sustainable AI adoption.

Governments are also beginning to see that ethical frameworks are not static. They must evolve alongside the technology landscape to maintain public trust and compliance. That balance—between innovation and responsibility—will define the next era of data strategy.

3. Infrastructure: Laying the Groundwork for AI Readiness

Governance and ethics set the rules—but infrastructure makes them real. The CDG report identifies modernization as a critical enabler for AI, with 95 percent of state CIOs expecting increased AI adoption to heighten the importance of data management. Agencies are migrating to the cloud, building data lakes, and developing unified models to ensure interoperability and scale.

Raleigh, North Carolina, offers a case in point. Its data modernization initiative includes data catalogs, security posture management tools, and a data lake that supports secure sharing. The city’s leaders emphasize that “data maturity and data security are not competitive—they go together.” That dual focus on protection and performance illustrates how modernization and risk management must progress in lockstep.

For governments, this modernization isn’t just technical—it’s strategic. Building data architecture that can handle the velocity, volume, and verification demands of AI is what turns data from a liability into a force multiplier.

4. Operationalization: Turning Insight into Impact

The final force is the most visible one: putting data to work. CDG highlights how jurisdictions like Washington, D.C., and Salt Lake County are transforming their data investments into real-world results—from predictive analytics that help prevent overdoses to dashboards that improve financial transparency and public health response.

Operationalizing data requires more than technology. It demands collaboration, data literacy, and cultural change. When agencies empower staff to use and trust data—when “it’s okay to share,” as one Salt Lake County leader put it—they move from compliance to confidence. That shift is the hallmark of mature data governance.

This focus on execution reflects a broader truth across government: the real value of data lies not in how it’s stored, but in how it’s applied. When agencies can see their data, understand its value, and act on it with integrity, they deliver better outcomes for constituents and strengthen accountability across the public sector.

Making Progress Together

The four forces shaping government data strategy—governance, ethics, infrastructure, and operationalization—are interdependent. One strengthens the next. When governments modernize with intention, they don’t just manage data; they harness it to create a smarter, more responsive public sector.

As Hawaii’s Chief Data Officer put it, “I see the motivation from government leaders to move forward with data and AI.” That momentum is real—but it must be supported by the right partnerships, the right tools, and the right frameworks.

Explore the Full Report

To dive deeper into how states and localities are preparing for the AI era, download the full white paper, Navigating the Future of Data, produced by the Center for Digital Government and underwritten by Exterro. Discover the insights and examples shaping the next generation of data-driven governance.