Elijah Meeks of the Data Visualization Society makes an interesting case for it — asserting that, while the country has had data-driven presidents, it “has never had a president that cared more about the appearance of data than the data itself, until now.”
Meeks isn’t being snarky here; he wants to know what Trump means for the way we consume data now. When Trump tweets the 2016 map of the county-by-county presidential vote
or draws on the NOAA map of Hurricane Dorian’s potential impact to add “an additional bit of range,” he’s both misrepresenting the data but also way ahead of his critics about how presentation has become the fact. As Meeks puts it about the Dorian map: Trump “knew if he could just change the visualization, that was all that mattered.”
This has been taken by many pundits as a sign that we live in a post-fact era but that’s short-sighted. Instead, public debates about the presentation of data increase the prominence of data visualization as a meaningful act. The previous way of looking at it, that you were just “showing the data” is naive and misleading and leads to products like Trump’s “Impeach This” map. The naive perspective that data visualization is just a final step to help people see the data ignores the importance of subtle steps like showing uncertainty as well as the necessity to design a product that engages the audience (something Trump does far better than many data visualization practitioners).
Trump is a sign of this, not a cause, and as we move forward in our practice we need to be more aware of how, for many people, the visualization is the data.
Swap in “insights” or “arguments” for “visualization” and you have an excellent case for building a thought leadership program alongside your research. Today, data are not just data; they’re also how data are made manifest (or distorted) in the most compelling ways.
Research has to move quickly on these two insights: 1) data visualization and thought leadership are merging, and 2) visualization is quickly becoming as important in every realm as its underlying data. In a world in which SalesForce and Google are buying data visualization companies, we’re seeing (in Meeks’ words) “the acknowledgement that if you don’t have good data visualization than [sic] your insights are less apparent, resonate less with audiences and are harder to communicate among scientists.”
Data visualization is becoming less of a tech company rarity and more a part of everyone’s everyday life. If you have a smartwatch, you see data visualization encoding your exercise routine and other details of your everyday life. And not just there, data visualization is all over in sleep tracking apps, weather reports that encode uncertainty, communications pieces like Spotify’s end of the year report, goal tracking apps, bullet journal habit tracking, diet/food tracking apps like fitness pal, bank statements and more. It will only continue to grow more common in the coming years.
What we need to do next year is reexamine all our preconceived notions about what makes data visualization good and how to achieve it. We need to seriously rethink what we’re doing because what we’re doing has seriously changed. Modern data visualization is optimized for producing charts for busy executives. But that’s changing. Now, data visualization is personal stories, small businesses, data science, political campaigns, human resources, community building— in short, data visualization is becoming a part of the fabric that is modern culture. We need to throw away our old notions of data visualization and understand how this new data visualization is made, how it’s read and how it relates to itself.
As with so much in research communications, there’s no going back to a previous “fairer” world in which “stories” or “hype” or “personal brands” didn’t matter so much. Research simply has to become better at visualization than those who seek to distort it through visualization — because for most, research is becoming visualization.