- Cool thing 2
- Cool thing 1
A term popularized by some professional illustrators — data viz — is a contraction of data visualization. Engineers have always been challenged to transform their data into a visually accessible format, whether it be with simple graphs and bar charts or something more complex. We have not always done this successfully. Consider how often we have accompanied a technical presentation with slides, many of which had so much information in a single frame that it was difficult to read beyond the second row, and might require five or ten minutes of careful analysis to fully comprehend.
Data visualization can be an isolated exercise or a component of a larger illustration called an infographic (information graphic), which can include other elements — photographs, drawings, and cartoons, for example. The many computer programs for data visualization that are now available provide endless creative possibilities — not just for engineers but for marketers, financial analysts, web designers, and publication editors. And infographics has become a specialization — perhaps even a distinct profession — for commercial artists and illustrators.
Yet when we view the often puzzling output from some of these new programs, it is easy to wonder whether they are more dangerous in the hands of our own technical community or the visual professionals themselves. Much of the output of the latter ends up in both popular and specialized magazines, or posted online, where we can judge their degree of success, or lack thereof.
When I went online to do a bit of research on the subject, I found that technical illustrators often disagree about what constitutes good and bad visualization techniques. In one case the author of an article posted online by Smashing magazine favored several visualization examples. Many of the viewers, however, found the author’s examples to be more appropriate in demonstrating what not to do. One of the examples in the article is pictured above, showing the same data in two separate versions. The bottom “tweet-o-meter” version was chosen by the author as “creative and unique” and thus superior. Viewers found the bar chart more effective, a typical comment noting that the author’s selection had too many elements and “it is annoying to keep flicking to the legend, then back to see the numbers on the chart.” And the exaggerated weight given to the subjects further from the center of the circle is a misleading feature of the meter.
Consider the Audience
Avoid the temptation to use every “viz” tool that is available, one designer cautions. The object should not be to produce an entertaining, challenging puzzle, but to impart information simply and quickly.
Ultimately, several designers concluded, the degree of pizazz imparted to an infographic or data visualization ought to be a function of the intended audience. Is the audience incidental or professionally committed, they ask. A reader of Time magazine would be part of an incidental or lay audience, while a reader of IEEE Spectrum would be part of a committed, professional audience. The incidental reader might be attracted by colorful visuals and “cutesy” icons, but these would be off-limits for a professional audience, who might find it both inefficient and “speaking down” to them.
The client is also a factor, several commercial artists noted. The client may favor an artist or design firm who uses “eye candy” to “enhance the user experience” even though doing so may compromise usability and obscure the intended message.
The client may also favor, or even require, a more complex infographic as opposed to a one-topic data visual. In the case of a magazine, the requirement may be to fill one page, or even a two-page spread. This sometimes results in a three-ring circus, in which the viewer does not know which ring to concentrate on. Even more challenging is a “global infographic,” which consists of too many elements, too many factors per element, too little space devoted to some of the elements, unclear linkage among elements, and little or no text calling attention to the major points intended.
Cramming too much information into a single visual presentation may initially attract the viewer’s attention (the “wow” factor), but it can quickly weary him/her from spending the time required to sort everything out. The visual should help, not hinder, interpretation of the underlying data. Several separate visuals often prove more useful than a single “global” visual.
Color is a useful tool but is often overused or misused. It can distinguish one element or grouping of data from another. But when used as eye candy to impress, it may simply confuse or distract. In overly complex infographics, multiple distinctions may require too many shadings of the same color, or the use of colors that are too close in value (purple, violet, etc.).
Your opinions on good or bad data visualization/infographic design are welcome.
- Balliett, A., The Do’s and Don’ts of Infographic Design, https://www.smashingmagazine.com/2011/10/14/the-dos-and-donts-of-infographic-design/ (retrieved Feb. 12, 2014)
- Yau, N., The Do’s and Don’ts of Infographic Design: Revisited (retrieved Feb. 24, 2014)
- Tufte, E. R., The Visual Display of Quantitative Information, Graphics Press, 1983.
- Tufte, E. R., Beautiful Evidence, Graphics Press, 2006.
- Rendgen, S., and J. Wiedemann, Information Graphics, Taschen Publishing, 2012.
- Cleveland, W., Visualizing Data, Hobart Press, 1993.
- Awesome Free Tools to Make Infographics, https://www.makeuseof.com/tag/awesome-free-tools-infographics/ (retrieved Feb. 21, 2014)
- Also see a collection of online posts at Essential Resources: Charting, analysis and explanatory tools. (retrieved Feb. 28, 2014)
- Data Viz Stories-Digg. Archive of unusual and humorous examples of data visualization projects, https://digg.com/tag/data-viz (retrieved March 4, 2014)