The Problem With Interactive Graphics

In a nutshell: People aren’t interacting with them.

The Problem With Interactive Graphics
Illustrations: liuzishan/iStock

In a recent talk given at the INCH Munich conference and later published on Medium, designer Dominikus Baur doled out some surprising data points on the interactive infographic. At the New York Times—which, in our opinion, publishes some of the best interactive graphics out there—readers only interact with about 10% to 15% of graphics. In fact, 85% of the Times‘ page visitors online simply ignore interactive infographics altogether.

In his talk, Baur notes that he got these numbers from talks given by Times graphic editors Gregor Aisch and Archie Tse (you can find Aisch’s talk here and Tse’s talk here). Baur lead with these numbers not to depress people about the value of data visualization, but rather to bring up the question of how interactive designers can more effectively bring people to their work. His advice? Worry less about novelty in designing for interaction, and more about using it as a tool to offer readers an individualized experience.

In other words, interactive graphics are most useful when they let people click around and adjust the visualization to address their specific needs and questions. Baur puts it this way: “If you think about visualizations as a mass medium, something made for huge audiences, interaction turns them into very personal tools.” He compares good interactive graphics to “having a tête-à-tête with an expert on the data, patient enough to explain you everything.”

Technology has led the way for mass customization—the creation of specialized products for individual users on a large scale—in clothing, sneakers, architecture, and other goods. It seems only natural that it be applied to data visualizations as well. In some ways it already is: Effective infographics, even in static mediums, make sense on multiple levels. For example, graphic designer Jennifer Daniel, in an interview for Co.Design, described her methodology as the “Bart and Lisa approach” to data design. The idea is that you create something that Bart Simpson could understand on a very superficial level, but that the more meticulous Lisa could also dig into for more detailed information.

Baur’s point is that interaction allows you to do even more with that, as long you know how to wield it. By his definition, all interactive infographics “have ways for the end user to change their attributes,” giving users different lenses by which to view the data at hand. Good interactive infographics offer lenses that illuminate what is most important to individual users. To evaluate, Baur writes, consider first how much time users have with the graphic. Is it part of an exhibition at a museum, where viewers have a lot of time to play around with it, or is it part of a breaking news article that needs to be understood quickly? The medium of the graphic should also be informed by what the reader wants to get out of it: Will an app, poster, or interactive embedded right into an article best help users achieve the goal they have in interacting with it?

Another common factor that Baur has found in popular infographics is that they meet the users where they are, making the information personal to them. This might be achieved by getting users to enter in personal information that tailors the graphic to them; for example, by asking for their house address in a graphic about how housing prices vary per location. A more high-tech option is to use sensors on smartphones, like the one that picks up on users’ geographical location (and prompts them to click “allow” first). In a Co.Design story last year, entitled “What Killed The Infographic?” Mark Wilson wrote, “Almost every [data design] studio I talked to pointed to a coming trend, in which data visualization designers take part in the data curation step, collecting their own data from a world full of sensors that we’ve barely begun to consider, to make their own work more relevant.”

Creating interactive graphics that factor in these points takes time and talent. They’re also expensive to create because they have to work across different platforms on desktop and mobile. Still, those feel like speed bumps on the road to ever more sophisticated graphics that allow users to parse the information that is most important to them.

In that way, data designers are like the architects of infographics: they set the scene, provide the platform and guide the way for incredible amounts of data to be made accessible. But the most successful data visualizations, Baur argues, are the ones in which readers also play a leading role—by being able to shape the data to coincide with their own interests.

Read the Medium post in full here.

About the author

Meg Miller is an associate editor at Co.Design covering art, technology, and design.

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