Data visualization is everywhere, and nowhere, in content marketing. What do I mean? Judging from what flits across my screen, every week companies publish and promote hundreds, if not thousands, of infographics, interactives, dashboards, slides, and microcontent, all attempting to artfully blend words and data and distill insight from raw information.

Even as data visualization has become so much easier, and the data available so much more available, it more than ever runs the risk of going nowhere fast. As far back as 1983, Edward Tufte was decrying “chartjunk” and other examples of unnecessary visual confusion and complexification through dataviz.

But as data visualization guru Stephen Few writes in his book Now You See It, the fanciest tools are no substitute for good old-fashioned thinking: “During the past two decades, we’ve seen tremendous progress in technologies that allow us to collect, store, and access data, but we’ve largely ignored the primary tool that makes information meaningful and useful: the human brain. While concentrating on the technologies, we’ve forgotten the human skills that are required to make sense of the data.”

The Way Out of the Data Fog

“Dataviz” is best used in content marketing when you want to concentrate insights into an easily digestible format and promote them through multiple channels. (What’s “dataviz”? Take a look at these examples.)

Dataviz is not necessarily the wunderkind it was a decade ago, when it began to take off in popularity. But when done well, data visualization can help readers see patterns in complex information, make better decisions, and find the signal amid the noise. Dataviz can also separate leading brands from those that lag behind. It can be surprisingly hard to pull off successfully, but when done well, it can generate as much traffic or more as a long report.

When done without enough thought and skill, however, dataviz can clutter the mind and subtract value rather create it. At Insight Content Lab, we’ve seen plenty of pretty graphics that ultimately say very little, or even worse, leave the reader scratching their heads in confusion. We’ve seen a number of projects stumble through multiple iterations, as data, messages, and design change again and again.

Before rushing out a poorly conceived and organized project, it pays to take the time to consider these hard-learned lessons from years of developing clear and compelling dataviz that communicates and informs.

  • Develop a plan. Before setting off on a project, it’s often well worth hiring a professional writer or editor to take a careful look at the available data and begin to tease out the stories to tell. Often with traditional long-scrolling infographic projects, this takes the form of a script that includes a title, selected data to visualize, key data labels, and captions with the key takeaways and messages about the data. With other projects like interactives and dashboards, a script can involve assembling, reorganizing, and refining the key data available, and then mocking up some of the best stories that jump out.
  • Simplify, simplify, simplify. Ask not simply what can go into dataviz, but also what can be left out. I call this strategy “addition by subtraction.” The reader will rarely miss the reams of data and tangential information you’ve taken out. A “minimum viable product” is one that has that just-right Goldilocks quality of just enough information to aid readers in understanding but not too much as to cause eyes to glaze over.
  • Be open to an iterative process. The story will inevitably change in reaction to the design. That’s OK, as long as what emerges in the end is better than it was before. Take a look at the examples below showing infographics before and after design. Believe me, there were a few steps in the process that are not shown, including false starts, digressions, and last-minute changes of mind.
  • Develop dataviz in parallel with written content, not as an afterthought. I can’t count how many projects have kicked off after a writing team has put a major report to bed. A collective sigh of relief goes up, and after a few weeks of rest, everyone focuses again on creating something new. Resist the temptation to tackle projects like this. Derivative infographic and interactive content can take as long or longer to produce than a report. Start as soon as the data is absolutely ready to go (but not sooner).
  • Avoid the temptation to show everything. With many projects, there’s a natural tendency for your eyes to be bigger than your stomach. So many fancy bells and whistles get added to the spec that this contraption would hardly get off the ground were it even capable of flying. Let go of the urge to build every feature into the design. Busy readers will thank you.
  • Don’t simply reproduce a deck. Many times writing teams come to me with a 100-slide, chockablock deck that was “very popular with clients.” The designer and I take a look and scratch our heads. We have so many questions that it quickly becomes apparent that we’re going to need to completely rethink the data, the descriptions, and the design. That’s completely normal, because dataviz is not a deck. What may go over well during a fast-paced presentation featuring compelling voiceover will be much differently received when readers take the time to carefully study and understand it.

“The best designs … are intriguing and curiosity-provoking, drawing the viewer into the wonder of the data, sometimes by narrative power, sometimes by immense detail, and sometimes by elegant presentation of simple but interesting data,” Edward Tufte wrote in his classic book The Visual Display of Quantitative Information. We can all aspire to that lofty strategic goal. And in the meantime, we can keep in mind the more prosaic, but critical, tactics that lead to creating insight from information.

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