Why America Needs to Grow the Field of Data Policy
From the small businesses relying on census figures to a cancer patient who survives because they found the right clinical trial, federal data are the essential, invisible infrastructure of modern American life. The data enterprise of the United States government is world-class. But it’s not perfect, and American communities have long needed data that are more detailed, timely, and relevant while also protecting privacy and security.
Unfortunately, federal data are held together with a patchwork of outdated and brittle policies that leave our data vulnerable, and currently, federal data programs face a perfect storm of funding cuts, personnel departures, and the erosion of institutional knowledge.
The solution is not to simply rebuild the data infrastructure as it was. To build a more healthy, safe, prosperous and equitable society, we need a new approach to data. That means catalyzing the field of data policy.
More robust public data policies will provide communities with better warnings for extreme weather, protect people from illness and disease, give businesses market insights they need to be profitable, and enable innovations like autonomous (“captainless”) shipping and AI-informed emergency response. Such policies might include more robust engagement to enhance the utility of federal data, from strengthening funding and talent pipelines to sustain and improve the federal data ecosystem, to modernized privacy and data sharing to improve public trust and deliver better services to the American people.
What is data policy?
Data policy is the framework of laws, executive actions, official standards, and practices that govern across the lifecycle of federal, state, and local government data. Without a cohesive field of data policy, public data become more siloed and fragile, while becoming less useful and relevant. The ultimate purpose of public data policy is to turn disconnected government data from an accidental byproduct of bureaucracy into intentional, protected, and accessible national assets.
The field of data policy is distinct, but nestled among similar but different fields. Data policy:
- requires conversance in data science, but does not involve direct data analysis;
- is a specialized and technical track in public policy;
- is an occasional add-on to tech policy and public interest tech;
- is a subset of information policy;
- includes, but is broader in scope than, statistical policy;
- encompasses the robust field of census policy, which includes stakeholders in business, social justice, law, history, budget, redistricting and voting rights, and more;
- looks to the field of data for civil rights to anchor policy frameworks in justice, equity, and human dignity;
- is foundational to AI policy (because AI feeds on data) but often overshadowed by AI-specific applications;
- may mandate or enable evidence-based policymaking;
- includes some implementation and enforcement, where it can overlap with the more day-to-day role of data governance; and
- spans all domains and categories of public data (statistical, administrative, scientific, geospatial, reference, evaluation, accountability, and navigation).
Data policy covers the policy landscape described in the federaldatafieldguide.us, including but not limited to: legislation; executive actions; statistical policies; enabling policies around talent, procurement, service delivery, accessibility, and other issues; OMB memoranda; best practices, standards, and methodologies; data policy implementation artifacts; and horizon issues such as AI safeguards and Tribal data sovereignty. This is outlined in depth in the Field Guide.
Building a toolkit for Data Policy
We have already started building the tools necessary to support the emerging field of data policy. With dataindex.us we monitor federal data, assess its wellbeing, and mobilize stakeholders when policy windows for public engagement arise. With federaldatafieldguide.us and essentialdata.us, we shine a light on the different categories of federal data, and tell the story about how they benefit the American people because federal data are often invisible and taken for granted. Through this work – across domains including immigration, public health, climate, housing, and transportation, to name a few – two opportunities are clear. One, people who depend on public data would be more likely to engage with federal data stewards if they better understood the policy context. And, two, real-world applications of public data expose the shortcomings of current data policies, and thus, opportunities to improve the policy landscape.
How will things be different with a robust field of Data Policy?
Preparing the workforce of tomorrow. Today, a college student can study public policy and data science, yet never learn about how the Privacy Act protects personally identifiable information, or how the Open Government Data Act requires public engagement. Formalizing the field of data policy will prepare students and mid-career professionals to more effectively design and implement data policies for the future.
Delivering better insights. Geospatial data on environmental hazards and statistical data on income and demographics have completely different policy contexts, and only come together when administrations build special tools or analyses to support related policies. A more robust field of data policy will enable interoperability of different types of datasets, increasing their utility even as policy priorities shift across administrations.
Improving services for the American people. Both the Biden and Trump administrations have championed service delivery and fraud reduction initiatives. These use cases depend on using federal data assets in novel ways, but often gloss over the nuances of data security and privacy, data standards, and fitness for use. At best, this results in missed opportunities to use data policy to build a more responsive data ecosystem. At worst, it undermines the health and trustworthiness of federal data, reducing data quality and increasing risks of disclosure of sensitive information. Having data policy expertise on service delivery design teams could shift this work from hard-won, one-off solutions to government-wide policies that strike a balance between ease of use and mitigating harm.
How we approach this work
At the FAS Data Policy Institute, our priority is to ensure that public data policies yield safe, trustworthy, and actionable public data that drive better outcomes for all communities.
We believe that policy should work across all levels of government, siloed domains, and single agencies. We regard data users — businesses, researchers, academics, nonprofits, state and local governments, and the general public — as critical policy stakeholders, not merely consumers of government output. We view data professionals inside government as colleagues and collaborators who will be more effective with civic tools to complement their institutional capacity.
We collaborate with a diverse collective of data partners and communities to build a more just data infrastructure for the nation. By equipping policy processes with agile, accessible technical tools, we break down systemic barriers to public participation. We believe true progress requires making data policy relevant to everyday Americans, driving for disaggregated demographic and geographic data that accurately describe our communities, combined with effective privacy and security safeguards that protect individual rights.
The strength of our nation’s data policy isn’t just in the rules we write, but in how effectively we translate those rules into a system that prioritizes the public good. Join us in this movement to design and build more resilient national data policies.
The strength of our nation’s data policy isn’t just in the rules we write, but in how effectively we translate those rules into a system that prioritizes the public good. Join us in this movement to design and build more resilient national data policies.
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