Across the human services sector, a variety of data sharing models support state and local benefits access interventions and increasingly catalyzes longerterm systems transformation initiatives. Projects funded under the Coordinating SNAP & Nutrition Supports (CSNS) grant program deployed three distinct methods for sharing data in their projects, which were aimed at driving better customer service, simplifying access to the Supplemental Nutrition Assistance Program (SNAP) and connected benefits, and streamlining program delivery and workflows. Through cross-program data sharing, these projects advanced agency priorities ranging from customer-focused service delivery modernizations, to strengthened outcomes evaluation, to organizational culture enhancements within and across agencies.
This issue brief draws insights from administering the CSNS grant cohort of six sites to document the following for agencies and partners:
- How data sharing models can advance program priorities
- Easy-to-follow visual and narrative descriptions of three tested data sharing models
- Considerations for embarking on data sharing across agencies or programs.
While this brief is specifically dedicated to data-related lessons learned, future publications will offer more details on individual site projects and their outcomes.
Sharing Data Can Help Administering Agencies Advance Their Priorities
Specifically, in their CSNS projects, agencies worked across systems and departments to analyze enrollment gaps in the programs that partners administer. CSNS sites used those analyses to develop enrollment- and retention-focused interventions, such as the Kansas team’s targeted client outreach to populations overrepresented in enrollment gaps. Agencies also leveraged data sharing models to streamline referral and enrollment processes to eliminate steps customers need to take to apply for and receive needed services, exemplified by project teams in New Jersey and New Mexico deploying APIs, Decision Engines, and systems linkages to simplify WIC certification for families enrolled in SNAP and other related programs.
Administering the grant cohort, we learned that, depending on the data fields collected by and exchanged between agencies, analyses can reveal trends relating to participant characteristics and demographics such as age, race, location, languages spoken, and more. CSNS project teams in Michigan and Mecklenburg County, North Carolina demonstrated how these analyses can be deployed by agencies to develop and execute specific equity goals. For example, an analysis that reveals a wide participation gap among a population that primarily speaks a language other than English can be used to generate customer-focused improvements to program access for that specific population. Such was the case in Mecklenburg County, where through the collected data fields, analyses revealed a large proportion of Hispanic/ Latino residents lacking access to nutrition assistance programs. Relatedly, the Hawai’i CSNS team demonstrated how agencies can use cross-program data analyses to more broadly understand trends across program areas and engage in multi-agency ‘Ohana Nui, a multigenerational approach to improving family well-being, by connecting families to nutrition supports and replicating the data matching model to improve service delivery and customer experience.