Case Study

Maximizing Insights from Existing Data for the Camden Promise Neighborhood Initiative

By Candice Dias, Ph.D.
September 2022
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Launched under the U.S. Department of Education’s Promise Neighborhoods grant program, Camden Promise Neighborhood in Camden, New Jersey, is a collective impact initiative. The vision of Camden Promise Neighborhood is to drive efforts, resources, and strategies to significantly improve educational and developmental outcomes of the children and youth in the target neighborhood, from birth to college and career. This work extends across multiple domains of a family’s life but is anchored in neighborhood schools, where 95% of students are Black/African-American or Latinx. Families in Camden experience high rates of poverty and unemployment, with 35% of families living below the poverty level and unemployment rates as high as 14% within the Camden Promise Neighborhood footprint.

A central goal of Camden Promise Neighborhood is to establish a data-driven culture with and among partners. This case illustrates the shift toward this culture by focusing on the process of extracting new perspectives from existing school data, specifically attendance data. It outlines how the partners established trust for data sharing and collaboratively interpreted data. In creating structured and guided discussions within regular scheduled accountability meetings and employing tools such as root cause factor analysis to collaboratively problem-solve, practitioners co-created findings that shaped efforts to improve outcomes for neighborhood youth. This case is a snapshot of early-stage work in an emergent learning cycle. It suggests that, in complex and under-resourced systems, even descriptive data analyses can offer practitioners important insights that may have been previously overlooked.

This case suggests several lessons related to fostering and deepening practitioner engagement with data. It was imperative that research and evaluation staff were willing to code switch from the language of data, research, and analysis to terminology that is more accessible and less intimidating to practitioners. Collective review of findings provided an opportunity to co-create meaning and empower stakeholders to derive actionable evidence. Establishing efficient technical processes through a trusted broker and data pipeline limited burden on under-resourced partners, releasing them to participate in timely reviews of data with openness and an orientation toward shared learning. The collective attention to student attendance as the foundation of improved outcomes facilitated additional resources for wraparound social supports to get students to school, where staff could then prioritize their needs based on attendance, grades, and behavior.