What's the equivalent of "literary interpretation" for data experiences?

For one of my posts in the 30-day chart study challenge in the Elevate learning community, I analyzed a Washington Post map from July 2023 by Tim Meko and Kevin Uhrmacher. The map shows the theaters screening the Imax-70mm version of “Oppenheimer.”

The archival feel of an old U.S. government map quietly transforms what could have been a forgettable visual into a powerful metaphor for the elephant in the room: a film grappling with the legacy of nuclear development.

Design details

• The cross-hatched pattern fill: The fill evokes the texture of radiation fallout maps, a subtle nod to the shadow of nuclear history

• The drop shadow on the red pins: This effect helps the pins stand out visually while echoing detonation points or warning indicators on cold war-era military maps

• The desaturation of the map: From the gorgeously subtle waterlines to the state polygon outlines, the overall color palette reinforces the archival, haunted look and feel of the map

• The decision to label Alamogordo and Los Alamos: Even though these locations don’t host an 70mm screening, they’re labeled in the boldest font. The font weight and inclusion of the label is a quiet editorial move that brings these places symbolically front and center, where the film and American memory often fail to. Their prominence, paired with their absence from the screening network, asks an uncomfortable question: Who was this film really made for?

• Label kerning: The kerning on the country labels is v satisfying. As are the light-gray city labels that anchor the reader geographically without competing with the data points.

This map could easily have been a fun pop culture visual, but these specific visual markers and editorial decisions elevate it into a visual artifact reflecting the conflicted reception to the film. I'm curious about the graphics reporters' process and intentions, since this analysis is obviously a product of my own views and taste.

Maybe one day we'll write critiques of data visualizations the way we do for films like “Oppenheimer”—not just dissecting what they show, but debating what they mean. There’s growing acceptance of data visualization as an art form in parks and awards and museum collections and it’s no longer blasphemous to acknowledge the subjectivity of data viz. Yet, we haven’t fully embraced the idea that data experiences can also invite interpretation and contradiction beyond the superficial debates of "was this the right chart form?" (like that COVID spiral chart moment). Maybe we’ll argue over the meaning of a legend or a drop shadow the way we might over a line in an Ocean Vuong poem or a simile in a Shakespeare play—the choices in a viz hold just as much weight and intention.

My brain is swirling with a bunch of questions about how do we critique data viz and extend the art of critique to data viz and what would it mean to intentionally design a data experience with several possible interpretations and other curving tangents I shall spare you from.

As always, please reach out if you have any thoughts or resources to share!