Data is for Everyone: Reflections from a Community Data Capacity Initiative by Jenrose Fitzgerald, Jillian Martin, Dan Ferris, Sara Mohamed, and Paul Sorenson

Date: Friday, January 16, 2026

Greetings from Washington University in St. Louis! I am Jenrose Fitzgerald, Program Manager for the Data for Social Impact (DSI) initiative at the Center for Social Development at WashU. My co-authors are Jillian Martin from Duke University, Dan Ferris from Washington University in St. Louis, Sara Mohamed and Paul Sorenson from University of Missouri St. Louis. The objective of DSI is to build capacity and connection among social sector organizations working to increase impact for and with the communities they serve. While DSI programming reaches national and international audiences, we are particularly focused on the St. Louis region. Broadly, we aim to build community data capacity, which includes both evaluation capacity and equitable data practices.

The DSI initiative brings together a wide range of people with varying data skills, organizational contexts, and roles, and advances the idea that everyone is a data person. Because of this, it is important to meet people where they are and develop offerings and tools that can build connection and a sense of shared values both within and across organizations and sectors. Given these factors, our evaluation approach to DSI needed to be flexible enough to respond quickly to changing community priorities. We draw on developmental evaluation and emergent design evaluation,  allowing us to incorporate our evaluation findings into our programming and pivot in our approach when needed.

As the initiative has evolved, we have identified critical pivot points, moments in which we shifted course in response to participant feedback while maintaining our core values and vision. For example, interviews with social sector leaders shifted our approach from an original focus on ethical data science training aimed at executive leadership to collaborative and equitable data practices more broadly, with an emphasis on stronger communication and collaboration across roles. Feedback from our advisory committee and from public roundtables initiated a shift in curriculum areas, leading us to prioritize accessible language and programming designed to accommodate all levels of data fluency. Participant input even led to a name change from Data Science for Social Impact (DSSI) to Data for Social Impact (DSI). Finally, feedback from event and online module surveys led to the formation of communities of practice and data equity cohorts grounded in peer learning and knowledge exchange.

For DSI, a developmental and emergent approach to evaluation has provided a flexible framework for building responsive programming for a diverse range of participants with multiple, sometimes conflicting priorities, while simultaneously building connection and a sense of shared values and vision around data and impact.

Lessons Learned:

• Build on social sector strengths. Too many capacity building initiatives are deficit based, grounded in the notion that organizations lack capacity or resources. Grounded community capacity building in collaboration and equity build on areas in which the social sector is well positioned to lead.
• Prioritize peer learning and exchange. In our experience, capacity building efforts that center expert knowledge are less likely to resonate with social sector practitioners than those that grounded in peer learning. This is especially important for reaching people who may not view themselves as “data people,” but who have valuable perspectives and insights to share about what meaningful impact looks like.
• Translational skills are as important as technical skills. When building community data capacity, process is key. Cultivating a strong data culture and the ability to communicate about data may prove more valuable in many contexts than acquiring deeper technical skills.

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