Outlines two cost-effective strategies used for managing programs in the private sector that could be useful for public sector administrators.

This article looks at government programs, particularly social service programs serving low-income individuals, and examines two analytical managing techniques that can be used to improve program services while efficiently allocating limited resources. The first managing technique, predictive analytics, “refers to a broad range of methods used to anticipate an outcome. For many types of government programs, predictive analytics can be used to anticipate how individuals will respond to interventions, including new services, targeted prompts to participants, and even automated actions by transactional systems” (p.1). The second, rapid-cycle evaluation, “uses evaluation research methods to quickly determine whether an intervention is effective, and enables program administrators to continuously improve their programs by experimenting with different interventions” (p.1).  The brief provides information about how program administrators can use predicative analytics and rapid-cycle evaluation, including suggestions for and approaches to implementation. (Abstractor: Author and Website Staff)

Full Publication Title: Smarter, Better, Faster: The Potential for Predictive Analytics and Rapid-Cycle Evaluation to Improve Program Development and Outcome

Major Findings & Recommendations

The authors suggest that federal social service agencies take two actions. “First, agency departments with planning and oversight responsibilities should encourage the staff of individual programs to conduct [a] thorough needs assessment” and second, they should “promote the adoption of predictive analytics and rapid-cycle evaluation more broadly across programs” (p.6-7). In support of these measures, the article makes the following recommendations: • “Help programs make individual-level data available for analytics” (p.7). • “Improve data governance and facilitate data sharing. Although high-quality data are necessary, agencies also need strong data governance policies that establish accountability for data quality and that define the terms for how and where data are used” (p.7). • “Encourage analytic decision making…focus on how to improve the programs and empower program administrators” (p.7). (Abstractor: Author and Website Staff)