Exemplary Data Use by State TANF Agencies

Beyond Routine Reports and Analyses

By Robert M. Goerge, Emily R. Wiegand, Emma K. Monahan, Leah Gjertson


This brief reports findings from an analysis of patterns of data use by state Temporary Assistance for Needy Families (TANF) agencies and is aimed at understanding what characterizes exemplary data use. The findings highlight the importance of collaboration and communication, both internally and externally, around data and how it is used. Technical and data infrastructure (in particular, the age of a state’s primary TANF data system) appeared to have no relationship with the quality of analytic data use.

The brief should be of interest to state TANF administrators who want to make their agencies more data-driven and are in search of strategies and practices for creating a strong data culture.  Additionally, the brief may be of interest to policymakers, researchers, and organizations seeking to expand analytic capacity in state TANF agencies and other related human service programs (for example, SNAP, Unemployment Insurance, and Medicaid).


It is crucial for TANF agencies to use their data to understand how programs are working for the families they serve and how to improve those programs. Yet it is often difficult for agencies to prioritize and implement data use and analytics for program improvement. To identify strategies and practices common to agencies with high analytic capacity, the research team categorized data use in state TANF agencies and examined how agency characteristics were associated with exemplary data use. The brief discusses accessible and attainable strategies to increase the use of data and the dissemination of analyses, with the goal of supporting more evidence-based policymaking and improving programs for children and families participating in TANF.

Key Findings and Highlights

Drawing on data from a national needs assessment of TANF agencies, the research team created a measure of data use and identified three categories of data use: basic, advanced, and exemplary. Using these categories of data use, the team found:

  • Communication and collaboration among frontline staff members, other state entities, and external partners were strongly associated with exemplary data use.
  • Staff expertise and analytic skills were not strongly associated with exemplary data use.
  • Exemplary data users were less likely to report new data systems. Technical and data infrastructure appeared to have no relationship with the quality of analytic data use.

These results offer evidence for the following practices and strategies to foster exemplary data use:

  • Encourage communication and collaboration at all phases of analyses and across staff and agencies.
  • Cultivate useful external partnerships.
  • Prioritize transparency and dissemination to reinforce quality, augment impact, and promote accountability.


Data used in this analysis were collected from two sources: (1) an online needs assessment of the 54 states and territories that operate TANF, of which 48 responded; and (2) a review of publicly available reports and analyses conducted with state TANF data. The first step in this analysis was to create a measure of exemplary data use in state TANF agencies. Next, the team compared a range of TANF agency characteristics at the state level to each state’s data use score in order to understand which characteristics seem to be associated with stronger data use. Finally, the team analyzed results to identify strategies and practices for promoting exemplary data use.

Information collections related to this project have been reviewed and approved by the Office of Management and Budget (OMB) Office of Information and Regulatory Affairs under ACF’s Generic Clearance for Formative Data Collections for ACF Program Support (OMB #0970-0522). Related materials are available at the TANF Data Innovation (TDI) Needs Assessment Survey page on RegInfo.gov.

Goerge, Robert M., Emily R. Wiegand, Emma K. Monahan, and Leah Gjertson. 2022. “Exemplary Data Use by State TANF Agencies.” New York: MDRC.