Policy & Practice | Fall 2023

through the collected data fields, analyses revealed a large propor tion 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 multiagency ’Ohana Nui, 2 a multi generational approach to improving family well-being, by connecting families to nutrition supports and rep licating the data matching model to improve service delivery and customer experience. Overview of the Three CSNS Data Sharing Models 1. Match Cross-Program Data: This model involves sharing and matching data between agencies administering different programs to identify eligible participants who are not enrolled in all eligible services. It allows agencies to target outreach to these individuals, streamlining access to benefits. 2.Aggregate Cross-Agency Datasets in Data Lake: In this model, data from multiple systems are aggre gated in a centralized database or “data lake.” This allows agencies to perform more robust cross-program data analysis, providing deeper insights to inform strategic planning and evidence-based projects. 3.Automate Cross-Program Referrals: Data are shared auto matically between agencies when a customer interacts with one agency, triggering a referral to other eligible programs. While this model offers swift action for enrollment, it may not provide as comprehensive ana lytics as the previous two models.

n Analytics: Data analysts and sci entists uncover trends and gaps in program access to inform modern ization efforts. n Monitoring & Evaluation: Staff responsible for program monitoring and evaluation tracks key perfor mance indicators to assess impact. n Customer Service & Engagement: Specialized staff develops high-impact customer-facing interventions. n User Research: User researchers ensure program enhancements meet user needs and are user friendly. n Data Architecture: Data architects optimize data collection and storage for compatibility across agencies. n Data Visualization: Staff special izing in data visualization creates tool for advancing human services agencies’ priorities and achieving better outcomes for participants. By visualizing and describing the tested data sharing models, this new brief aims to demystify the process for non technical professionals and leaders. Emphasizing the core expertise areas and enabling factors, agencies can embark on successful data sharing initiatives to drive innovation and improve programmatic outcomes. To read the full brief and more, please visit APHSA’s Coordinating SNAP & Nutrition Supports webpage. 3 Reference Notes 1. https://bit.ly/3PavfrT 2. ’Ohana Nui is a proven approach that capitalizes on Hawai’i’s unique multigenerational family structure and provides a framework for human services delivery that positions whole families for a chance at greater well-being. See https://humanservices.hawaii.gov/blog/ ohana-nui-kicks-off/ 3. https://aphsa.org/APHSA/Policy_and_ Resources/csns.aspx clear and understandable data visuals to assess project impact. The CSNS grant program has shown that data sharing can be a powerful

Leveraging Cross-Program Data to Modernize Outreach and Enrollment in SNAP and Connected Benefits

Download this brief at https://bit.ly/3PavfrT

implementing technology-oriented tasks successfully. These collabora tions ensure that the right expertise is harnessed to efficiently execute data sharing initiatives. Equally important are clear use cases and specific plans for data analysis. Alongside setting data sharing goals, agencies must develop concrete strategies for ana lyzing the shared data effectively. By applying insights from the analyses, agencies can introduce innovations that improve their processes and prac tices, thereby enhancing the overall efficacy of their programs. To execute successful data sharing ini tiatives, agencies require expertise in: n Legal and Policy: Early engagement of policy staff and legal councils is essential for executing robust data sharing agreements. n Program Operations: Staff involved in program operations must be rep resented on project teams to ensure meaningful impact. n Data Systems: Data stewards play a crucial role in ensuring data compat ibility between agencies. Core Expertise Areas Required for Data Sharing Initiatives

Enabling Factors for Successful Data Sharing Initiatives

The success of data sharing ini tiatives hinges on several critical factors. First and foremost, strong leadership commitment to cross program coordination is essential. Additionally, forming robust relation ships with reliable vendors and subject matter experts plays a pivotal role in

Morgan McKinney is a Project Associate for Process Innovation at APHSA.

Jess Maneely is the Assistant Director of Process Innovation at APHSA.

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Fall 2023 Policy & Practice