Since 2012, the Wisconsin Department of Public Instruction (DPI) has maintained a statewide predictive analytics system providing schools with an early warning in middle grades of students at risk for not completing high school. DPI is considering extending and enhancing this system, known as the Dropout Early Warning System (DEWS). The proposed enhancements include better understanding how and why schools use a tool like DEWS, supports and training necessary to translate DEWS into school change, and extending DEWS into other domains such as college and career readiness. This paper identifies national models of predictive analytic systems in education, including a focus on the Early Warning Implementation Monitoring System (EWIMS) (National High School Center, 2013). The paper explores how such policies might succeed in achieving their goals (e.g., dropout prevention and reduction of predictive at-risk behaviors), ways that districts and schools can make the policies more successful, and how states and state agencies like DPI might strengthen the policies, thereby facilitating local success.