CORE Insights: Six steps toward more equitable data visualization

September 11, 2023

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  • Data visualization practices can reinforce inequities and mislead, misrepresent, and harm communities that have experienced inequities and discrimination. Applying an equity lens is essential to more accurately representing and communicating with the communities served by researchers and others.
  • As part of CORE's Insights blog series, we share six key steps to help apply an equity lens to your data visualization efforts.
  • View the webinar below or read on for ideas and insights from the Urban Institute's Do No Harm guide, Catherine D'Ignazio and Lauren F. Klein’s Data Feminism, and real-world examples from CORE’s community partners.

Presenting data in visually compelling ways helps audiences understand it more easily and make decisions faster. Yet, according to the Urban Institute’s Do No Harm Guide, “When data are collected and communicated carelessly, data analysis and data visualizations have an outsized capacity to mislead, misrepresent, and harm communities that already experience inequity and discrimination." 

In a recent webinar, Kristen Lacijan and Claire Devine of Providence CORE explained how applying an equity lens to data visualization can help you respond to these challenges, understand potential harms, and work to address them. As part of the webinar, Kristen and Claire shared six steps toward applying an equity lens to data visualization, leveraging insights from the Do No Harm Guide and Catherine D'Ignazio and Lauren F. Klein’s Data Feminism, along with real-world examples from CORE’s work and community partners. These are just a few of the resources we're exploring at CORE as we continue our learning around more equitable practices.

Read on for a summary of recommendations, or view the webinar video here.

1. Ask questions

Data users don’t always have control over how data are collected; for example, census data or Medicaid claims data. However, the more we can understand what’s behind the data and its collection, the more fully we can represent that context and its limitations when visualizing it. Questions to consider include:

  • Why were these data collected?
  • How were these data generated?
  • Are the data demographically representative?
  • Whose voices, lives, and experiences are missing?
  • How we will communicate about missing voices, lives, and experiences?
  • How much can these data be disaggregated by race, gender, ethnicity, etc.?
  • Who stands to benefit from these data?
  • Who might be harmed by the collection or publication of these data?

2. Include context

Once you understand that underlying context, communicate it in your graphics or figures to give audiences a more holistic understanding of the story behind the data. It is essential to be clear about what data are useful for, what they are not useful for, and what you are trying to say. Kristen and Claire highlighted several specific examples, including the following context from the Oregon Health Authority 2021 CCO Performance Metrics Dashboard’s section on race and ethnicity.

A sample disclaimer about how to interpret Oregon Health Authority data

3. Choose language/imagery

Data visualization tends to reduce people and their experiences to bars, lines, points, colors, categories, icons, etc. Being conscious and careful about how you present data visually and with words can help build more trusting relationships with the communities behind it. Recommendations include:

  • Always remember that data reflects the lives of real people.
  • Use people-first language. For example, people in prison vs. inmates.
  • Avoid using labels, hierarchy, color palettes, or icon selection that reinforce stereotypes or norms. 
  • Consider alternatives to using “other” as a catch-all category. For example, “another race” or “identity not listed.”

Claire pointed to an example from the Do No Harm Guide demonstrating how a graduated color palette can inadvertently suggest a hierarchy between races and reinforce White as a norm.