Resources
Public confidence in data collection has been eroding but it’s not too late to rebuild trust. Safety First: A SMARTT Data Framework is a resource designed to help nonprofits and government agencies better serve their communities. Created by a team of experts in data fortification and collection, the SMARTT Data framework offers practical guidance for collecting and sharing data that is Safe, Meaningful, Accurate, Representative, Translatable, and Transformative.
Citation: Beddawi, Adam, Karthick Ramakrishnan, Michele Wong, Beth Jarosz, and Jacob Pasner. Safety First: A SMARTT Data Framework to Serve, Protect, and Defend Our Communities. Berkeley, CA: Institute for the Study of Societal Issues, 2025.
Executive Summary
We are developing a “safety first” approach to data collection designed to rebuild community trust and restore participation in public programs and services.
In recent years, communities have grown increasingly concerned that their personal data may be used against them. This trend has undermined public confidence in federal data collections and threatened the ability of policymakers, service providers, researchers and public agencies to understand and serve their communities. It has also raised complications regarding the implementation and adoption of the recently revised federal standards for data on race and ethnicity, which promise more accurate data collection and reporting on historically underserved populations. Without assurances that population data will be kept safe, many communities may hesitate to share their information with government agencies and other institutions.
Community mistrust in public data collections is poised to get dramatically worse, coming on the heels of political developments such as federal capture of state data for unauthorized activities (e.g., federal use of state administrative data for immigration enforcement), as well as technological developments such as the opaque use of artificial intelligence (AI) technologies in domains ranging from qualification for public benefits to predictive algorithms in lending and policing. Far from being only a “data problem” or a “research and evaluation problem,” these threats to data governance are likely to generate strong chilling effects and withdrawal from participation in a variety of public programs.
To serve, protect, and defend our communities, we need to fortify state and local data collections. Data fortification includes two central goals: protecting constituent data from a variety of external threats and strengthening the reliability of public data collections and their ability to address constituent needs.
This framework on data fortification aims to help government agencies and nonprofits collect, analyze, and share data in ways that are:
- Safe: Secure from misuse by third parties.
- Meaningful: Designed to address community and government needs.
- Accurate: Reflects true individual experiences and outcomes.
- Representative: Portrays the full diversity of subpopulations.
- Translatable: Understandable and accessible to various stakeholders.
- Transformative: Drives innovation to serve community needs.
This solution is particularly important now, with mounting concerns about the integrity and reliability of federal data collections and the lack of guardrails preventing the misuse of state and local data. The SMARTT Data framework, when paired with implementation resources and guidelines on data infrastructure and data governance offered by the Massive Data Institute and other partners, can help us meaningfully advance effective policy solutions at the national, state, and local levels.