Connect to Lived Experience and Community

Learning Objectives

After completing this unit, you’ll be able to:

Describe the importance of lived experience as it relates to data.
Recognize that data is not neutral, and how to mitigate bias.
Explain ways to engage communities in data research to improve results.

Not everyone has the same life experience. Our individual characteristics such as ethnicity, gender, neurodiversity, and age deeply impact the way our research and data visualizations take shape and form. While our differences make us unique, these same differences have a powerful impact on how we see the world.

Doing good data means that you need to consider lived experiences, lead with empathy and fairness, reflect the lived experiences of the people in your data, and connect with the people at the center of your research.

You also need to build relationships with the people and communities you’re researching, consider what your work may be missing, and seek out the people who can help you tell a more complete story. Data storytellers should always do their research and analysis with communities, not on behalf of them.

Data Isn’t Neutral. Proceed with Care.

Structural racism, historic discrimination, other barriers and inequities, and the mechanisms by which they might operate can and should be woven into the framing of your research and the data visualizations you create. You can’t rely on data and visualizations speaking for themselves: Data is not neutral or objective, and data visualizations are not neutral either.

Without a point of view that embraces diversity, equity, and inclusion, data visualizations tend to express the viewpoint of the dominant group in society and perhaps unintentionally mask a host of issues behind the scenes. When providing context, you should have diverse references and citations, particularly from scholars of color or scholars who are members of the community of focus. You should lift up the voices of these communities and highlight lived experience where possible.

Go Deep

Instead of shying away from detail and necessary context, acknowledging the inherent complexity of many social issues provides a more accurate reflection of the topic and promotes better understanding. With this information, viewers will be able to draw the correct conclusions.

Viewers also feel more connected and engaged when presented with robust, nuanced visualizations. Incorporating qualitative information, expressing subjectivity, and acknowledging uncertainty provides a more valuable experience for the viewer. Dense, custom-designed data visualizations presenting multiple layers of information can encourage more careful reading, better personal connection, and a deeper understanding because readers are offered various paths to explore the data. You can be clear and informative without oversimplifying.

Does all data need to be visualized? Not always. Sometimes a simple number set can be more effective than a graph. In some cases, a chart is the wrong choice for conveying complexity or bias inherent in the data or the visuals. Also remember that, as a storyteller, you may find that traditional charts and graphs are not nearly as effective as traditional narrative media such as photographs or video.

Create Human Connections

As you expand your tools and awareness, you may need to incorporate qualitative research methods that reflect lived experiences. Match the method to people you work with, and you may find the results improve dramatically, especially since people’s reactions to data visualizations are often driven or framed by their personal experience (such as where they live or work, their level of educational attainment, or their political identity).

Long-form surveys, interviews, and focus groups can provide an important opportunity for community members to share their experiences and lift their voices. People who give interviews should feel empowered by your work to question data visualizations, use them to sell their own story, and cause a shift from awareness to action. Transparency of methodology and enlistment of subjects can go a long way to increasing participation in interviews. Data visualization specifically should empower users to allow them to question the representations.

We’re not suggesting that pursuing qualitative research is as simple as conducting a few interviews. When qualitative and quantitative researchers collaborate, a more powerful narrative can emerge that includes a greater portion of the population.

Concluding Thoughts

As a data communicator, you have a unique opportunity to shape key narratives in society and culture. When you embrace the principles of Do No Harm in your data storytelling you’re contributing to a brighter, more equitable future. Visit the Tableau Data Equity Hub to learn more from leading experts at the intersection of data and equity issues.

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Connect to Lived Experience and Community

Learning Objectives

Data Isn’t Neutral. Proceed with Care.

Go Deep

Create Human Connections

Concluding Thoughts

Resources