The efficacy of policy interventions for socioeconomic challenges, like food insecurity, is difficult to measure due to a limited understanding of the complex web of causes and consequences. As an additional complication, limited data is available for accurate modeling. Thorough risk based decision making requires appropriate statistical inference and a combination of data sources. The federal summer meals program is a part of the safety net for food insecure families in the US, though the operations of the program itself are subject to risk. These uncertainties stem from variables both about internal operations as well as external food environment. Local partners often incur risk in operating the program; thus we use decision analysis to minimize the risks. After integrating public, private, and government data sources to create an innovative repository focused on the operations of the child nutrition programs, we construct a Bayesian network of variables that determine a successful program and compute the expected utility. Through an expected utility analysis, we can identify the key factors in minimizing the risk of program operations. This allows us to optimize the possible policy interventions, offering community advocates a data driven approach to prioritizing possible programmatic changes. This work represents substantial progress towards innovative use of government data as well as a novel application of Bayesian networks to public policy. The mathematical modeling is also supplemented by a community-facing application developed in Shiny that aims to educate local partners about evidence based decision making for the program operations.