In the News: The Importance of Data Visualization

While some people believe that data visualization is overrated, and that data can effectively be reduced to statistics, a recent project by Autodesk clearly demonstrated that this belief is misguided.

Building on prior work by Francis Anscombe and Alberto Cairo, Justin Matejka and George Fitzmaurice generated twelve datasets that have the exact same summary statistics, but very different visual qualities—and meanings!

The point is, it’s always important to visualize your data—and to present these visualization to your audience—rather than simply relying on numbers.

Between the Lines: Human-Scale Statistics 

In The Science of Speaking, I note that when numbers are very large or very small, it’s often good to present them by using an analogy. For example:

The accuracy required to land a spacecraft on Mars is like Steph Curry throwing a basketball from the three-point line of the Oracle Arena (in California) and hearing it swish in Madison Square Garden (in New York) just as the buzzer sounds.

In Made to Stick, Chip and Dan Heath present a similar analogy along with data to back up its utility. Compare the following two examples, they say:

  1. Scientists recently computed an important physical constraint to an extraordinary accuracy. To put the accuracy in perspective, imagine throwing a rock from the sun to the earth and hitting the target within one third of a mile of dead center.
  2. Scientists recently computed an important physical constraint to an extraordinary accuracy. To put the accuracy in perspective, imagine throwing a rock from New York to Los Angeles and hitting the target within two thirds of an inch of dead center.

When presented with the first analogy, 58% of people thought this feat was “very impressive.” When presented with the second analogy, 83% of people thought so!

The key, then, is not just to use an analogy, but to use an analogy that lives at the human scale, placing numbers on a scale that we can wrap our heads around. As the Heaths note, this can take some finessing. In both analogies, the distance from California to New York is still a bit intangible. “The problem,” they explain, “is that if you make the distance more tangible—like a football field—then the accuracy becomes intangible. ‘Throwing a rock the distance of a football field to an accuracy of 3.4 microns’ doesn’t help.”

As another illustration of human-scale statistics, the Heaths present an example taken from Stephen Covey’s The 8th Habit, in which Covey presents the findings of a survey of 23,000 employees from a variety of industries. Here are the findings:

  • Only 37 percent said they have a clear understanding of what their organization is trying to achieve and why.
  • Only one in five was enthusiastic about their team’s and their organization’s goals.
  • Only one in five said they had a clear “line of sight” between their tasks and their team’s and organization’s goals.
  • Only 15 percent felt that their organization fully enables them to execute key goals.
  • Only 20 percent fully trusted the organization they work for.

As the Heaths note, this is “pretty sobering stuff. It’s also pretty abstract. You probably walk away from these stats thinking something like ‘There’s a lot of dissatisfaction and confusion in most companies.'” But “then Covey superimposes a very human metaphor over the statistics.”

If, say, a soccer team had these same scores, only 4 of the 11 players on the field would know which goal is theirs. Only 2 of the 11 would care. Only 2 of the 11 would know what position they play and know exactly what they are supposed to do. And all but 2 players would, in some way, be competing against their own team members rather than the opponent.

As the Heaths note, “the soccer analogy generates a human context for the statistics. It creates a sense of drama and a sense of movement. We can’t help but imagine the actions of the to players trying to score a goal, being opposed at every stage by the rest of their team.”

Whenever you are presenting statistics, see what you can do to humanize them, either by shrinking them (or blowing them up) to a human scale, or presenting them in the context of a human story. By doing so, you’ll make your numbers even more impactful.

Note: This advice is a nice practical supplement to yesterday’s post about how great leaders appeal more to emotion and intuition than they do to logic. By humanizing your statistics, you can begin to transform logic into emotion and intuition, making your appeals even more effective. For a good example of this in action, see Hans Rosling’s TED talk about the magic washing machine.