On Aug. 4, 2021, USDA awarded approximately $3.6 million in Data and Technical Assistance (DATA) grants to five states. These grants are intended to support the full range of needs state agencies may have related to the development, collection, reporting, analysis and use of SNAP E&T participant outcome data. Among other things, funds can be used for training and capacity building, including efforts to improve E&T data quality; information technology systems development to support the collection, reporting, and analysis of SNAP E&T data; and/or continuous program improvement of SNAP E&T programs.
Arkansas Department of Human Services will establish a foundational Business Intelligence (BI) and Analytics module that builds upon their current SNAP Works system to standardize, consolidate, and manage E&T data. The module will be able to generate automatic results for the Annual Outcomes Measures Report, develop and track appropriate component and referral measures for E&T participants, and store client data across their lifecycle including demographics from eligibility systems, assessments, employment plan, trainings, work components, and quarterly wage data. Arkansas will also create interactive dashboards with the ability to filter data, see new trends, provide alerts for corrective actions, and give information on how the participants are meeting their activity milestones. Arkansas also received a DATA grant in FY17.
Indiana Family and Social Services will implement a new E&T Reporting Platform in their statewide system with new analytic architecture, allowing reports to be produced on a scheduled basis with self-service features for users. This technical solution will lead to increased data transparency and quality for FNS, Department of Family Resources (DFR) policy and operational staff, and DFR partners. Indiana will also automate weekly reports for more frequent outcomes-based reporting, create data entry standards for E&T case workers, integrate wage and applicable eligibility data from outside data sources, and implement ongoing software development lifecycle maintenance as well as continual improvement processes.
Louisiana Department of Children and Family Services (LDCFS) will create a data dashboard for SNAP E&T that will serve as a model throughout LDCFS. The public dashboard will show all workforce development data for SNAP E&T, and ultimately all LDCFS programs. Data collected and demonstrated will include participant outcomes by case manager or provider, and will include data from all ecosystem partners: community-based organizations, technical and community colleges, supportive services, community action agencies, and others. Data will be used to assess provider and component activity performance (outcomes and cost) regionally and statewide, participant performance (outcomes and cost), and identify service strengths and gaps of the state's E&T program.
Minnesota Department of Human Services will implement an advanced system for continuous, data-driven program improvements, focusing on equity within the state's E&T program. This project seeks to answer fundamental questions about the program and participants, and as such, answer key questions about the quality and equity of its E&T program to make data-driven program improvements. Minnesota will accomplish this project in 3 phases: research and analysis on the state's SNAP E&T program data, including participation, performance, and equity; the development of new metrics and data systems to measure these factors; and the design, implementation, and measurement of strategies to address root causes of service, performance, and equity concerns.
Virginia Department of Social Services will improve E&T data collection, data quality, and reporting systems by developing standardized program performance and outcome measures, streamlining data collection methods, and conducting IT system development. Virginia also proposes improving administrative data analysis by integrating quarterly wage and other education and employment data from other agencies within the state, and disaggregating the data by demographic characteristics.