The graphic shows a visual depiction of something measurable, specifically how much juice you still have in your battery.
Here’s an exercise:
Draw a picture of jet lag. Don’t draw a picture of some who’s suffering from jet lag. Draw “jet lag”.
The task itself requires other tasks first, starting with a clarification of goals. Without an clear understanding of how this purely synthetic enterprise will be useful, the effort risks falling somewhere between a waste of time and comic amusement. Add a dollop of well considered explanation, however, and suddenly an abstract expression about time and distance catches our attention and makes sense.
Data visualization is about taking measured, quantified data and turning it into images that convey relevance. Where individual measurements may be concrete, the accumulated meaning of many measurements present an opportunity to reveal some sort of abstract truth.
One of my favorite examples of successful data visualization appears on just about every cable news program every single hour of every single weekday. In a corner of the screen usually alternating between the clock, the station ID, and a logo for whatever program happens to be playing, there is often a set of little triangles showing the relative movements of the main stock indices, the NYSE, NASDAQ, and S&P. If the markets are higher in comparison to the previous trading session the triangles appear green, with one vertex pointing up. If the markets are lower, the triangles appear red, pointing down. Usually there’s a matching numerical measurement of the market’s current measurement next to these triangles, but at a glance the colored triangles let us know if we’re in the money or in stormy weather. In a single polygon, we get a visualized snapshot of relative market movement. It’s elegant in its simplicity.
There’s a fine art to doing data visualization well. It’s one thing to communicate; it’s quite another to do it with brio and inventive flair. It’s like food: a person can stave of hunger without ever discovering what good cooking tastes like. With data visualization, it’s easy to plot points on an X/Y graph, but it’s something else entirely to turn those points into a representation that reveals an otherwise invisible structure or significance. To put a sharp point on it, teams who deliver good data visualizations make it look easy (like all talented artists), but they also capture imaginations with compelling ideas.
Data visualization is not a new thing, but it’s a vocabulary that only recently became a driving part of ordinary, non-scientific, non-technical lives. Fitness watches commodified personal tracking data, and millions of people now use that data and the graphics they generate to steer the days—and feet. But ubiquity does not convey the gravity of how much this communications paradigm shift has had an influence on culture. The more we rely on synthesized data depictions, the more we change some of the fundamental ways we interact with the world of ideas. Unlike animation, data visualization is not a province for fiction. At it’s core, it’s a discipline designed to reveal an underlying structure or reality that might otherwise hide in pure numbers. By organizing measurements into structures, visualizers present hidden aspects of reality.
For the millions of people who wear an Apple Watch and are forever checking to see how far they are from “closing their rings”, the clever data visualization depicting your target distance may give you a new insight into how many more steps you need to reach your goal. One has to wonder, however, if that constant, albeit smart visual solution really helps users get in touch with the intangible aspects of keeping fit. When the physiological and psychic aspects of our physical humanity get converted into graphical representations, albeit elegant ones, there is a translocation of meanings at work. When an amateur runner starts to look for visual validation simply to clarify his or her feelings about a completed workout, the visualization has taken over from being descriptive to being prescriptive.
One of the first ways everyone learns to track dynamic data comes in the form of timekeeping. Analogue clock faces tell us not only the precise time—twelve-twenty-seven, for example—but also the relative position of time as the physical position of actual clock hands gives us a visual context for time’s passage. It’s ironic that in our new, data-supercharged era that we have gotten away from visual depictions of time for less subjective, ostensibly precise digital readouts. That’s clearly an unintended consequence, but it’s a consequence that changes how we understand the world. With everything else around us being reduced to graphical depictions of data, a numerical quantification of time seems like a natural extension. That said, this is an ironic twist: the real data visualization is the older time keeping presentation. Clock hands visualize relative time, where a specific numerical depiction of time only shows a single point in time.
In other words, something gets lost when the world becomes nothing but measurable, discrete data. The thing about art is that it resists direct measurements. Art visualizes the world without turning to measurements. What this tells us is that for a discipline like data visualization—a vital, complicated, potentially thrilling human act—making a picture about a collection of points is less important than making a picture that tells us why those points matter.