Research Report Do No Harm Guide: Crafting Equitable Data Narratives
Jonathan Schwabish, Alice Feng, Wesley Jenkins
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So much of our world derives from framing—the narratives we lift up, the words we use, the images we show, the data we highlight. But there are also the narratives we ignore, the words we leave unsaid, the images we overlook, and the data we omit. All these framing decisions embolden and enrich some people while demonizing or minimizing others. 

This sixth guide of the Urban Institute’s Do No Harm project explores the concept of crafting equitable data narratives. What does equity mean? Who does equity include? What methods do we use to ensure equity in data work? How do we present the data in a way that show the humanity they represent?

Our goal with this guide is to expand the boundaries of what we consider equitable data. Data practitioners often frame their work as definitive: if the data say so, it must be true. But so many assumptions and decisions shape every part of the data collection, analysis, and communication process, leaving some people lumped together and others left out entirely. Interrogating this framing at every point in the process is crucial to promoting data equity.

Key Findings

The authors of the 12 essays in this guide work through how to include equity at every step of the data collection and analysis process. They recommend that data practitioners consider the following:

  1. Community engagement is necessary. Often, data practitioners take their population of interest as subjects and data points, not individuals and people. But not every person has the same history with research, nor do all people need the same protections. Data practitioners should understand who they are working with and what they need.
  2. Who is not included in the data can be just as important as who is. Most equitable data work emphasizes understanding and caring for the people in the study. But for data narratives to truly have an equitable framing, it is just as important to question who is left out and how that exclusion may benefit some groups while disadvantaging others.
  3. Conventional methods may not be the best methods. Just as it is important for data practitioners to understand who they are working with, it is also important for them to question how they are approaching the work. While social sciences tend to emphasize rigorous, randomized studies, these methods may not be the best methods for every situation. Working with community members can help practitioners create more equitable and effective research designs.

By taking time to deeply consider how we frame our data work—the definitions, questions, methods, icons, and word choices—we can create better results. As the field undertakes these new frontiers, data practitioners, researchers, policymakers, and advocates should keep front of mind who they include, how they work, and what they choose to show.

Research Areas Race and equity Sexual orientation, gender identity, and expression Housing
Tags Structural racism in research, data, and technology LGBTQ+ equity Homelessness Family and household data Neighborhood change
Policy Centers Income and Benefits Policy Center
Research Methods Participatory research Community Engagement Resource Center Qualitative data analysis Quantitative data analysis
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