What’s the most important thing about how you communicate your research and expertise?
If it’s too early in the morning for such a big question, look at what the Apple data visualization engineer Elijah Meeks — executive director of the Data Visualization Society — says should be the most important thing about a chart, one of the fundamental units of research communication:
The most important thing about a chart is not its aesthetics, the technology used to create it, the kind of data visualization layout or even the data it represents. The most important thing about a chart is its impact. Impact is what a chart does. And yet, we hardly even have a vocabulary around data visualization impact.
You — your research-driven organization, your research-based expertise, your attempts to communicate same — are all like a chart. Creating impact is your first job. After, that is, how you define “impact.”
In his seminal piece “What Charts Do,” Meeks lists these four ways for a chart to have impact:
- Provide insights;
- Cause change;
- Cause visual literacy; and
- Create new charts.
With slight semantic modifications, those all fit our work as research communicators for non-specialists.
First, a chart that provides insights, says Meeks, needs to “optimize the chart space for their discovery and manipulation,” rather than letting those insights be gathered in other modes extraneous to the chart.
In other words: Understand the kinds of insights your audience is looking for and will respond to, and make your insights obvious. Make content that spotlights those insights instead of relying on a research paper to convey it.
Second, a chart that causes change “reveals some trend or insight…that is acted upon in a place beyond the chart,” says Meeks.
Yet, we’ve done little to connect the activity beyond the chart back to the chart. It’s as if we want to spend all this time and effort creating a mechanism to understand and communicate but pretend we don’t need it once the action is taken. This is even more difficult to measure than insight, but also more important. Understanding how a particular chart was an effective piece of evidence or motivation is key to developing more effective data visualization.
In other words: Measure the change your communication creates — in your audiences, in yourself. Survey them if you have to. If you have well-positioned ideas, monitor and record how they are taken up — in ways faithful and adopted — on social and search.
Third, charts that cause visual literacy teach the audience new ways of seeing. “All data visualization was, at some point, complex data visualization, until an audience grew comfortable and literate enough to read it,” says Meeks.
In other words: It’s your responsibility to teach your audiences how to understand your new ideas, especially if they are disruptive.
Fourth, charts create new charts — as Meeks puts it, “old-fashioned dials let to data visualization in the form of dials which eventually led to the election dial chart, complete with skeuomorphic jitter, which itself led to even more dial charts.” Each formal evolution of a chart carries with it all the attributes of its ancestors.
In other words: Good ideas evolve in the hands of others — another measure of impact Martin Weitzman’s idea of taking out insurance against the fat tail of catastrophic climate change outcomes became, in time, the Green New Deal. If you’re uncomfortable with your insights evolving as they’re adopted, you’re in the wrong business.
Takeaway: Everyone involved in research communications — researchers, communicators, and especially directors and CEOs of these organizations — should demand impact as their first principle. And recognize that impact is a dynamic, learning process. The interaction of every chart with its audiences should make the next chart even more impactful. As with everything else you do.
You are like a chart. So be one everyone else wants to share.