Juice Analytics

I returned this week from the Eyeo festival, a gathering of many of the world’s most influential and innovative data visualization artists. The presentations spanned the thoughtful storytelling of Amanda Cox (Turning a Corner), the playful, organic aesthetic of Moritz Stephaner (Notabilia), the immersive, cinematic style of Jer Thorpe (Cascade) and Aaron Koblin (The Johnny Cash Project), and the hypnotic simulations of Robert Hodgin (iTunes visualizer). It was a group of speakers and demonstrations that has me revising my list of design inspiration links.

The discussions at Eyeo focused on helping an audience get in touch with their humanity, engaging people emotionally, inspiring playfulness, searching for truth and beauty, and achieving the moment of “wow”. Zach Lieberman spoke about achieving an “open mouth moment” — when a person’s jaw drops wide open in awe (via rockmeamadeo.com).

I was struck, however, by the gap between the Eyeo community of data visualization artists and the folks who apply data visualization for day-to-day business purposes. The gurus at Eyeo clearly represent the creative vanguard, tasked with pulling the state of data visualization art forward. Meanwhile, those of us who support daily tasks and decisions through the application of data visualization face very different priorities and challenges. There are at least three key areas of difference: goals, scope, and audience.

The artists are looking for an emotional “wow” moment; our goal is the “ah ha” moment when a user learns something that can lead to productive action. The question that we so often ask: “what can you do about it?” wasn’t a top priority within the Eyeo crowd.

The data visualization artists have the opportunity to choose a narrow problem and explore it deeply. Each project I saw attempted to express something very specific about a very specific data set. With Juice’s clients, it is rare to focus on a single data set, a single concept, or a single question. Business tools often require versatility to serve multiple audiences and answer a broad array of questions.

Due to this scope, the raw data and data analysis is different too. Data visualization artists choose their data wisely and study it deeply. They pour over the data to find the nuggets to be highlighted and gather supporting context to shape the user experience. Data visualization practitioners can know the shape and structure of the data, but the data itself is always changing. Amanda Cox made the point that the data can tell 1,000 stories (but it is important to tell one at a time). For practitioners, these 1,000 data stories can change moment to moment.

Finally, I saw a different relationship to the audience. Visualizations like Moritz’s X-by-Y will engage many people even as others find it confusing. That’s art; it doesn’t have to work for everyone.

For practitioners using data visualization, turning off a portion of your audience is a major problem. If we go out on a limb with a non-traditional graphic, there needs to be a more traditional alternative to see the data.

There is plenty of space for infusing artistic sensibilities into practical data visualization applications. I’d like to see this happen more. There is no better example than Moritz’ OECD Better Life Index. It manages to be both eye-catching and fun as well as truly valuable for data exploration. It is a rare and delicate balancing act.

Ultimately this art vs. practice dichotomy is natural and healthy. In our work, we are inspired by the fun and energy expressed in artistic visualizations. Data visualization is a tool that can and should be used differently depending on the purpose and the audience. The skill in using the tool can be appreciated equally across these different contexts.