Exploring Mutuality in Data Storytelling

Picture yourself at work and taking center stage to present your data-driven findings, excitedly breaking down the data viz you spent hours making. Now, imagine yourself taking a pause and looking at your audience. Something is wrong and a cringe feeling washes over you. Instead of curious faces, you find yourself meeting the gaze of glossy-eyed and dissociated colleagues. You think to yourself, what went wrong?

If that picture sounds familiar, you’re not alone. Sometimes it feels like data storytelling is one chart away from leaving the audience speechless (not in the good way). In past experiences, I’ve found that data can easily divide speaker and audience, and make it difficult to engage authentically with colleagues.

Data can do that sometimes. Data can push aside mutuality when we need it the most to maintain a genuine connection with our audience. In today’s post, I’m curious to explore the concept of mutuality and talk about how to leverage this dynamic towards delivering stronger and engaging data presentations.

What is Mutuality?

For those with a background in psychology, this term might sound familiar. It’s often discussed by therapists in the context of relationship-building. The American Psychological Association defines mutuality as “the tendency of relationship partners to think of themselves as members of a dyadic relationship rather than as distinct individuals.” My favorite definition comes from Harvard Professor, Judith V. Jordan —

“In a mutual exchange one is both affecting the other and being affected by the other; one extends oneself out to the other and is also receptive to the impact of the other. There is openness to influence, emotional availability, and a constantly changing pattern of responding to and affecting the other’s state. There is both receptivity and active initiative toward the other.”

Mutuality reminds me that in order to build an authentic exchange, it’s necessary to maintain an awareness of the message and its impact. Mutuality inspires me to ask questions like:

  • How are my findings landing in the room?
  • Are my visualizations helpful? Or confusing?
  • Have I facilitated opportunities to receive feedback?
  • Is the audience finding my data consumable and relevant?
  • Am I staying receptive? How have I integrated feedback?

These are the types of questions that feel the best to me, especially towards grounding my data storytelling in the goal of creating shared meaning and building collaboratively. As I think about mutuality in this context, I also wonder about what other challenges data storytellers come up against.

Have you found yourself at either end of a data-driven presentation that just wasn’t working? If so, what do you think was missing? What do you think makes a data-driven presentation memorable and engaging?




Data Science Student and Latina Educator at UCLA. mitzibandera.com | @latinasindata_

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Mitzi Bandera

Mitzi Bandera

Data Science Student and Latina Educator at UCLA. mitzibandera.com | @latinasindata_

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