A new paper published in the Harvard Data Science Review outlines complementary models for rethinking how data is used in the social sector, emphasizing that technical expertise alone is not enough to ensure fair and effective outcomes.
The paper, “Learning Models and Modalities to Build Data Equity Competencies,” argues that everyone is a data person, regardless of one’s role or title, and makes the case that centering community input, ethical decision-making and collaboration is essential for equitable and effective social sector data practice.
Drawing on the work of Data for Social Impact (DSI) initiative at WashU and Actionable Intelligence for Social Policy (AISP) at the University of Pennsylvania, the authors show how equity-focused, nontechnical training programs can help practitioners better understand how data practices impact communities and build both trust and more equitable outcomes.
“Data is never neutral. It reflects the assumptions and structures of the systems that produce it,” said Dan Ferris, an associate professor of practice at the WashU Brown School and co-author of the paper with Amy Hawn Nelson, of the University of Pennsylvania. “If we want data to serve the public good, we have to recognize that everyone brings valuable expertise and strengths. That means investing in people, culture and capacity across social-sector organizations to make that possible.”
The paper highlights DSI and AISP as complementary case studies, using cohort-based learning and practical toolkits to embed data equity into organizational practice and strategy. The authors suggest that building these competencies at scale is essential for organizations seeking to responsibly leverage data in ways that benefit all communities.
