Afterschool programs are seen as a way to keep low-income children safe and to foster the skills needed to succeed in school and life. Many cities are creating afterschool systems to ensure that such programs are high-quality and widely available. One way to do so is to ensure afterschool systems develop and maintain a data system.
This interim report presents early findings from a study of how afterschool systems build their capacity to understand and improve their practices through their data systems. It examines afterschool data systems in nine cities that are part of The Wallace Foundation’s Next Generation Afterschool System-Building initiative, a multi-year effort to strengthen systems that support access to and participation in high-quality afterschool programs for low-income youth. The cities are Baltimore, Md., Denver, Colo., Fort Worth, Texas, Grand Rapids, Mich., Jacksonville, Fla., Louisville, Ky., Nashville, Tenn., Philadelphia, Pa., and Saint Paul, Minn.
To date, research on data use in afterschool systems has focused more on the implementation of technology than on what it takes to develop and sustain effective data use. This study found that the factors that either enabled or hampered the use of data in afterschool systems—such as norms and routines, partner relationships, leadership and coordination, and technical knowledge—had as much to do with the people and process components of the systems as with the technology.
Strategies that appear to contribute to success include:
Starting small. A number of cities intentionally started with a limited set of goals for data collection and use, and/or a limited set of providers piloting a new data system, with plans to scale up gradually.
Ongoing training. Stakeholders learned that high staff turnover required ongoing introductory trainings to help new hires use management information systems and data. Providing coaching and developing manuals also helped to mitigate the effects of turnover and to further the development of more experienced and engaged staff.
Outside help. Systems varied in how they used the expertise of outside research partners. Some cities identified a research partner who participated in all phases of the development of their data systems. Others used the relationship primarily to help analyze and report data collected by providers. Still others did not engage external research partner, but identified internal staff to support the system. In any of these scenarios, dedicated staffers with skills in data analytics were key.