Data Science for Social Impact in Higher Education:  First Steps


Engaging External Partners

Learn how CAN network leaders work with others to support their data science for social impact programs. 

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The University of Chicago Data Science Institute (DSI) has a distinct approach to engaging external partners across our data science programs and projects. We center relationship- and trust-building in these partnerships and aim to meet organizations where they are on the data spectrum.

Introductory Meeting


First, we establish an introductory meeting to discuss the organization’s mission and needs and our approach to community-centered and ethical data science. Projects begin and end with understanding the needs and mission of our partners. It is critical in this first meeting, and in additional follow-up meetings, to learn about the following:

  • The organization’s mission and current initiatives.
  • Questions the partner would like to answer and/or projects they would like to complete, focusing on how these may advance the partner’s mission.
  • Establishing what data exists and whether it is sufficient for the proposed project or requires further curation.
  • Understanding the partner’s data capacity and the opportunity for the project to persist at the completion of the work. We ask each organization to take’s Data Maturity Assessment.
  • Any data privacy requirements or possible community harm and security concerns.



Next, we brainstorm ways we can support the organization or project in its goals. Common project ideas, data challenges, and opportunities for growth include:

  • Data collection and storage
  • Transforming and sharing data
  • Data visualizations
  • Dashboard or website creation (internal or external)
  • Data analysis and model building (e.g. historical, predictive, spatiotemporal)

Locate Resources


Once we agree on a project, we locate the best resources at UChicago for this collaboration. These can be the Data Science Clinic, Community Data Fellows Program, staff project consulting, postdoctoral or faculty research, or summer research programs.

  • As part of this process, we draft a formal scoping document with aligned goals, data risk mitigation strategies, timeline, expected milestones, and clear deliverables.
  • We also identified a point person from our partner organization who will be our external lead on the project.

Track Progress


While the project runs, we schedule regular check-ins and progress updates to keep the organization and project lead in the loop on design and implementation decisions throughout the data lifecycle.

  • Through these check-in meetings or regular updates, we surface and address additional opportunities, risks, and challenges that may arise.

End of Project


At the end of the project, we meet in person or virtually with our project partner to debrief and discuss the next steps

  • This includes handing off deliverables, collecting feedback on timelines and communication, and planning the next steps on sustainability and continued value for the organization.
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