Urban Institute | Jonathan Schwabish, Alice Feng
This guide discusses how to use an equity lens in every step of the data process: from data collection to presentation. The authors interviewed 20 individuals who approach inclusivity in their data communication to compile best practices, some of which include:
- Use person-first language
- Order labels and responses purposefully (e.g. listing by magnitude of results, not “men” and/or “white” racial and gender breakdowns first by default)
- Carefully consider colors, icons, and shapes and their ability to reinforce stereotypes (e.g. blue/pink for specific genders)
- Consider missing groups (i.e. what groups disappear when data is aggregated)
- Actively solicit feedback from, and compensate, communities represented in the data
Overall, it is important to focus on the individuals and communities behind the data points obtained through analysis and presentation. Consider how communities represented will perceive their portrayal in order to apply empathy in practice. This guide also includes a diversity, equity and inclusion in data visualization checklist to apply to your own data visualizations.