Using Data to Strengthen Afterschool Planning, Management, and Strategy: Lessons from Eight Cities

This report was published by Chapin Hall at the University of Chicago.

HERE’S HOW THE AUTHORS DESCRIBE THIS REPORT:

Afterschool programs can offer enriching opportunities, homework help, and a safe environment. To ensure that these programs are cohesive, high-quality, and widely available, many cities have designed community-wide systems to coordinate the various afterschool programs offered by different providers. Having a way to collect and share reliable data through these systems can help cities inform and strengthen their efforts.

Drawing from The Wallace Foundation’s Next Generation Afterschool System-Building Initiative, a four-year effort to strengthen systems that support high-quality afterschool programs for low-income youth, the report presents findings on how data systems were established, operated, and used in eight cities: Baltimore, Md., Denver, Colo., Grand Rapids, Mich., Jacksonville, Fla., Louisville, Ky., Nashville, Tenn., Philadelphia, Pa., and Saint Paul, Minn.

Interim findings were summarized in an earlier report — Connecting the Dots: Data Use in Afterschool Systems — which highlighted the importance of three central pillars to developing capacity to collect and use data systems: investments in people, processes, and technology.

By the end of the initiative, the eight cities had established data systems by building working coalitions across public, philanthropic, and nonprofit sectors. Operating these systems required continued attention to the three central pillars. The cities made notable progress in how they used data in meaningful ways, both by expanding how data were used across the system, and by engaging providers more systematically and purposefully in using data, including for accountability, improvement, strategic planning, and program management.

The report offers insights about lessons learned, including:

• Bring a systems-level focus. This includes having shared goals, staying focused on outcomes, and understanding how work and contributions are distributed.
• Collaboratively agree on meaningful indicators that signal progress and can generate recognition.
• Understand local circumstances, contexts, and expertise — and that expertise resides at multiple levels of a system.

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