Giving Compass' Take:
- Alice Feng and Jonathan Schwabish urge researchers and analysts to prioritize equitable data collection by employing more empathetic strategies.
- How can we include the communities of focus in research or analysis when carrying out studies? How are you prioritizing empathy in your giving framework?
- Read more about how you can promote inclusivity in research.
What is Giving Compass?
We connect donors to learning resources and ways to support community-led solutions. Learn more about us.
Through rigorous, data-based analysis, researchers and analysts can add to our understanding of societal shortcomings and point toward evidence-based solutions. But carelessly collecting and communicating data can lead to analyses and visualizations that have an outsized capacity to mislead, misrepresent, and harm communities already experiencing inequity and discrimination.
To unlock the full potential of data, researchers and analysts must consider and apply equity at every step of the research process. Ensuring responsible data collection, representing the communities surveyed accurately, and incorporating community input whenever possible will lead to more equitable data analyses and visualizations. Although there is no one-size-fits-all approach to working with data, for researchers to truly do no harm, they must build their work on a foundation of empathy.
In our recent report, Do No Harm Guide: Applying Equity Awareness in Data Visualization, we focus on how data practitioners can approach their work through a lens of diversity, equity, and inclusion. To create this report, we conducted more than a dozen interviews with nearly 20 people who work with data to hear how they approach inclusivity. In those interviews, we heard time and time again that demonstrating empathy for the people and communities you are focusing on and communicating with should be the guiding light for those working with data. Journalist Kim Bui succinctly captured how researchers and analysts can apply empathy, saying: “If I were one of the data points on this visualization, would I feel offended?”
We do not want to prescribe what to do or not do, but rather encourage thoughtfulness in how analysts work with and present their data. As we consider the use of words, colors, icons, and more in our data visualizations, asking whether we would be offended makes for a good checkpoint.
Read the full article about equitable data collection by Alice Feng and Jonathan Schwabish at Stanford Social Innovation Review.