Today’s popularized infographics have solid foundational roots. Today, however, they are more about decorating the data (or worse) than communicating it.

Beginnings

Around the time the Founders of the United States were discussing how best to build a new republic from a patchwork of British colonies, a guy named William Playfair was establishing himself as a key founder himself — of data visualization. Two hundred years later, Edward Tufte authored The Visual Display of Quantitative Information, a book of biblical importance to data visualization. Amazon calls it one of the “Best 100 non-fiction books of the 20th century.”

What Playfair started in the late 18th century (and what Tufte so eloquently articulated into theory and best practices) is now experiencing a groundswell, powered in large part by the social web. And 2010 was a breakout year for what has come to be known as the infographic.

Take a look at Google Insights for “infographic.”

And Google’s Timeline for “infographic” from 2007 to 2010 shows a similar slope.

This is by no means a perfect metric for measuring the circulation of actual infographic images, but it does offer a multidimensional sense of the term’s usage in reference to visualizing data in various contexts.

This increase is significant. Infographics have penetrated every meaningful social sharing mechanism. Anywhere there’s data, there’s an infographic: the future of travel, various stats about strippers, health care spending, race and sports, and facts about poop. GOOD.is has an entire section of content devoted to them.

Why the Surge?

From my perspective, there are four key factors fueling this spurt:

Marketing. Mint.com’s blog is spilling over with personal finance infographics. They began using them to augment their marketing efforts and enjoyed “incredible success.” Others have since followed their strategy. This tactic involves varying degrees of quality, intent and integrity ranging from pure and utterly meaningless linkbait to valuable and instructive visualizations — and they perform very well.

Consumption and distribution. The new breed of infographics are easy to consume, don’t require the time investment of a news article, and come nicely packaged in image format for easy distribution.

New classes of data (and more of it). If visualizations of the inflation-adjusted price of oil bore you, you’re in luck. Social networks are collecting massive amounts of new data. David McCandless compiled this fascinating time series of breakups from data sourced from Facebook. A number of other visualizations are now possible with Twitter’s API, like this mood map.

Designers have discovered numbers. It’s happened. The creative community has discovered data. Boring numbers, long since abandoned in high school and university classrooms, are cool again — as long as they’re nice to look at.

The Designer’s Duty

It’s not quite clear who first said “with great power comes great responsibility,” but its hard not to see how this can apply here. The power comes from the broad distribution of the data, and the responsibility is to let the story come from the data — not the design.

It’s a good thing that there’s a new vibrance in data visualization, and designers have the power to breathe life-giving energy into data and to make more clear the story that the data is telling us. They also have the power to obfuscate and distort the data, intentionally or not. Tufte writes a chapter on this called “Graphical Integrity.” He also explains in no uncertain terms how he feels about data being subjugated by artistic embellishments. I’m convinced that Tufte often winces in nausea when he sees this new breed of data visualizations.

The conditions under which many data graphics are produced—the lack of substantive and quantitative skills of the illustrators, dislike of the quantitative evidence, and contempt for the intelligence of the audience—guarantee graphic mediocrity. These conditions engender graphics that (1) lie; (2) employ only the simplest designs, often unstandardized time-series based on a small handful of data points; and (3) miss the real news actually in the data. It wastes the tremendous communicative power of graphics to use them merely to decorate a few numbers. Moreover, much of the world these days is observed and assessed quantitatively—and well-designed graphics are far more effective than words in showing such observations.

Tufte then offers his views on improving the character and quality of data visualizations.

How can graphic mediocrity be remedied? Surely there is something to be said for rejecting once and for all the doctrines that data graphics are for the unintelligent and that statistics are boring. These doctrines blame the victims (the audience and the data) rather than the perpetrators. Graphical competence demands three quite different skills: the substantive, the statistical, and artistic. Yet now most graphical work, particularly at news publications, is under the direction of but a single expertise—the artistic. Allowing artist-illustrators to control the design and content of statistical graphics is almost like allowing typographers to control the content, style and editing of prose. Substantive and quantitative expertise must also participate in the design of data graphics, at least if statistical integrity and graphical sophistication are to be achieved.

~ From The Visual Display of Quantitative Information, 2nd Edition

Tuft goes on (using terms like “chartjunk”, “ducks” and “data ink”) to describe concepts he views as compromising to the profession he loves. He presented his theory of data graphics decades ago. But as I examine them today I see, in far too many cases, how designers have turned their backs on those foundations.

Integrity and Clarity: Letting the Data Speak

In the summer of 2010, when the world watched live video feeds of the Deepwater Horizon oil gushing into the Gulf of Mexico, FastCompany presented “a new chart, putting the size—and cost—of the spill in perspective.” They produced this graphic in contrast to all the others that merely visualized the oil spill in absolute size. Theirs, they argued, showed the true impact and economic costs irrespective of its physical size. To the right is a scaled down view (full image: 1433×1027):

Three variables for each spill are shown: volume, cost and environmental impact. The environmental impact variable is extremely unscientific, based on estimates of inconsistent data points. But for the sake of demonstration, let’s assume this is real data.

I love the aesthetic. It looks professional and polished. But almost any other design would be more effective. The data wants to tell a story about the oil spills, but instead it is pushed aside in favor of aesthetics resembling the mass and orbits of Saturn’s moons.

Look at the infographic at any size or scale you wish. Can you immediately see which spills are the most costly, or have spilled the most number of gallons? The rings offer some sense of quantity, but the text is ordered in reverse (least at the top, most at the bottom) for two of the three variables. Examining the rings reveals a severe understatement of the volume of the Ixtoc spill because they are not spaced to scale. The damage and cleanup cost rings are similarly distorted. The rings make Deepwater Horizon’s economic costs appear nowhere near double that of the Exxon Valdez, when in fact they are.

Here’s an alternative, multivariate graphic (each data point shows three variables) using the same data, to scale.

I’ve done nothing to embellish or otherwise distort the data, and you can immediately see the story here: Relative to the other spills, Deepwater Horizon is a very, very costly, very environmentally impactful spill, yet nowhere near the largest (assuming the data is good). And this graphic is less than half the size of the original.

As another example, take a look at this infographic by GOOD.is, created from survey responses by teachers.

Each response seems randomly positioned. They’re also color coded, so I have to dart my eyes back and forth to quickly learn the key. The hand and puzzle piece shapes obscure the true relative magnitude of the responses. Look just at the issue responses that are visualized by bars (professional development, opportunities for alternate careers, and pay tied to performance). The bars vary in scale. The 40% who say professional development is “somewhat important” is given greater weight than the 40% who say opportunities for alternate careers is “somewhat important.” These values are the same, and should be represented that way. The bars also vary in alignment as well, making it even more difficult to get a sense of relative quantity.

Now try to visually absorb the image overall. Can you easily notice any outliers, notable data points or any other story the data has to tell?

Here’s an alternate version I put together using the same data. Note how a design can bear a relevant motif but still allow the data to speak.

Two things emerge from this approach. First, you can easily see there’s not a lot of middle ground here. The teachers who responded to the survey viewed a range of issues at least “very important” with a solid majority considering supportive leadership to be vital. Second, two issues jump out as being considerably less important: opportunities for alternate careers and pay tied to performance. These facts are faint at best in the GOOD.is version.

While I’ve picked on only two examples, the reality is there are numberless infographics circulating that are guilty of a litany of distortions, omissions, exaggerations, confusing designs and puffed up data points. From my view, I conservatively estimate three or four infographics showcasing the artistic skills of the designer over the data they profess for every one that designs in deference to the data itself.

What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather the task of the designer is to give visual access to the subtle and the difficult—that is, the revelation of the complex.

…Above all else, show the data.

~ Tufte in The Visual Display of Quantitative Information, 2nd Edition

Going Forward

I think the revival of data visualizations in the form of infographics is a very positive thing. We have access to more data today than at any point in human history. This, combined with ever-improving tools to present that data in clear and compelling ways can help transform our understanding of the world we inhabit.

My challenge to infographic creators going forward is this: Build on the pioneering minds that helped develop the field of data visualization, resist the temptation to overdecorate, and offer their skills in the revelation of the complex.

If you specialize in visually communicating data, what does your creative process involve? What priorities do you have in mind when you approach a project? Where does your inspiration come from?