KEEP GOING
IN SOME WAYS, data visualization is a terrible term. It reduces the idea of good charts to a mechanical procedure. It evokes the tools and methodology required to create rather than the creation itself. It’s like calling Moby-Dick a “word sequentialization” or Starry Night a “pigment distribution.”
It also reflects an ongoing obsession in the dataviz world with process over outcomes. Even now, most of the energy poured into teaching dataviz focuses on making sure you do it the “right” way or judging you if you do it the “wrong” way; on picking the right form; on when to use what colors. Chart crit is all about technique, how the thing was built, what it looks like.
Enough of all that. Forget right charts and wrong charts. Data is only a middleman between phenomena and your ideas about them.1 And visualization is merely a procedure, a way of using that middleman to communicate ideas that convey much more than just pictures of statistics. What we do, really, when we make good charts is get at some truth and move people to feel that truth: To see what couldn’t be seen before. To change minds. To cause action. It’s not data visualization so much as visual rhetoric: the art of graphical discourse.
A common understanding of some basic grammar is necessary to that, of course. We all need to use subjects and verbs in roughly the same way if we’re to communicate. But letting them govern our communication would be paralyzing and counter-productive. When you obsess on the minutiae of visualization rules—or, worse, when you judge a chart according to its relative adherence to those rules—you become one of Emerson’s little statesmen, adoring foolish consistencies.
Besides, software is beginning to take care of all that for you. Tools are evolving to manage some of the grammar.2 They’re getting their own versions of document templates, spell check, and grammar check to guide formatting decisions and correct common missteps. Decisions about color, labels, grid lines, even what chart type to use—decisions to which entire books and courses have been devoted—are being encoded into visualization software so that the output in its default state is at least pretty good.
Interactivity helps too. The number and type of labels to include in a visualization, for example, is a decision that we’re used to making as we construct charts, and it can be difficult. Too many labels create clutter, making it hard to know where to focus; not enough confuse viewers and, likewise, make choosing the proper focus a challenge. But hover states help solve the problem. Toggles manage complexity by showing or hiding variables as needed. A simple Next button can control the pace at which information is added or removed from a visualization.
If you want a peek at the future of data visualization—at least, the mechanical process of it—look at The Atlas of Economic Complexity, an interactive site codeveloped by Harvard and MIT and managed by Harvard’s Center for International Development.3 Shown here is a tree map generated by the site.
The Atlas of Economic Complexity points to a future in which presentation-worthy visualization becomes inherently collaborative.
Notice that the color scheme logically groups continents. I didn’t have to do that. That’s built into the application. Labeling is clear and sized appropriately—again, automatically generated. More detail is available on hover, and I have used multiple toggles to adjust what I see. This is on demand exploratory visualization, and automated declarative visualization. All I have to do is find the idea I want to convey, the story I want to tell, and iterate until I have it.
In short, visualization tools are evolving to make everything available but not always visible. That cracks things wide open. It changes a visualization’s essential nature from imparted to shared; from a transaction—something you present or hand over—to a collaboration, which you work on and adjust with others.
Visualization is becoming fundamentally more interactive. In the near future we’ll take for granted that decisions about what to show or where to focus—decisions you once had to make ahead of time and commit to—can be handled at the moment the dataviz is seen, often by the user. And those decisions will be alterable. Users will control the pace of the storytelling. Depth and complexity will become on-demand services. Show me more. Show me less. Show me just this. Show me only that. In a presentation, a manager will display a good chart and then filter and adjust it when the CEO asks, “What does that curve look like if we exclude the younger demographic?” A new good chart will immediately appear on the screen. “Now just show me how women responded.” Presentations will become conversations, exploratory dataviz in the boardroom.
Charles Hooper is a dataviz consultant who works mostly with Tableau these days, but he used to work in Excel and remembers using Lotus 1-2-3, Harvard Graphics, and a program called Brio. Before that, he hand-drew his visualizations, transferred them to acetate, and displayed them with an overhead projector. “I’m turning 70 next week,” he declares. “And right now, I’m telling you, this is the most exciting time, because it’s getting easy to try things. When it’s not easy, people just follow the specs. But you make it easy, put it in the hands of the masses, give it to businesspeople and not just specialists like me, and they come up with really innovative ways of looking at things. I learn something new every day from people trying out visualization.”
Software will continue to get better, in the ways we can already see and in ways we can’t yet imagine. But what it won’t do—what it can’t do—is intuit your context. And context, still, is everything. Visual thinking and visual communication will become no less relevant no matter what features are added to software programs. If anything, the better the software gets, and the less you need to stress over the number of ticks you put on your x-axis, the freer you will be to focus on the ideas you want to communicate. The process of understanding your context, finding your main idea, and visualizing it persuasively—that is, the guts of this book—will be the most critical skills you can develop.
You are here, at the end, which means you’ve started. Now keep going.