We’re all familiar with Edmund Burke’s well-known line “Those that don’t know history are destined to repeat it.” Burke’s insight applies in spades to the history of data visualization and its practitioners. Each new wave of practitioners insists on rushing in and claiming the laurels of expertise without first studying the lessons and mistakes of the past. We’d be much further along today if this weren’t the case.
I recently read the book Practical Charting Techniques by Mary Spear, which was published in 1969. This was several years before books on graphics by John Tukey, Edward Tufte, or William Cleveland. Spear’s early work is full of practical wisdom and it focuses on best practices as she understood them at the time. Many of the visualizations that I introduce to students in my courses today, which they find new and exciting, can be seen in the pages of this book. Spear included a graph that looks and works almost exactly like Tukey’s box-and-whisker plot years before he introduced it. She even includes graphics that look like an early predecessor of my bullet graph. What I found particularly interesting are two statements that she made about the state of data visualization back in the 1960s.
Here’s the first:
In the mid-1940s and 1950s the three-dimensional chart was popular. Fortunately that vogue seems to be passing. While some were interesting and attractive, they were for the most part practically impossible to read or interpret directly. Comparisons were difficult to make, and scales had to be visually adjusted so that amounts could be read. Such a process required mental and optic gymnastics. (p. 62)
She bemoaned the popularity of 3-D graphs and explained why they fail, but was encouraged by their decreasing popularity. What happened between the decline that she witnessed in the 1960s and the renewed popularity of 3-D graphs today? Software companies got involved in creating tools for the creation of graphs without taking time to learn the lessons of the past. No companies are more to blame than those in the business intelligence industry. Had they built their tools responsibly, we could have avoided another dark ages of graphical display.
Here’s the second of Spear’s statements that I found particularly interesting:
Statistics can be as misleading as the intentionally distorted chart is. Surveys and samples can be biased, correlations too small, factors missing, improper measures taken—not to mention the possible prejudice of the writer or speaker. But fortunately, as the art of graphic presentation advances, more listeners and viewers are becoming aware of these pitfalls and give heed to them; and more designers of charts are being artistic without confounding the true story. (p. 68)
Advancements in data visualization were already, way back then, leading people from a juvenile proclivity to decorate graphs in ways that undermined their stories into a more mature approach to graphical communication. At some point, however, the progress that Spear observed and appreciated was waylaid by a return to silly graphics. Much of this corresponds to the rising interest in infographics, which encouraged students of the graphic arts to venture into the realm of data visualization without bothering to first learn from the past. Graphic design skills can indeed be applied to data visualization, but not without first learning about the field and how it differs in purpose from other uses of graphics. While a few thoughtful infographic designers have honed their craft by studying data visualization best practices and the realms of science that inform these practices, most have taken the fast track born of arrogance and built on ignorance. Infotainment is the result: displays that entertain with flashy tricks while the information on which they’re based becomes unrecognizable and distorted in the glare.
Must every new generation repeat the mistakes of the past? Can’t we use technology for advancement rather than as a streamlined path to dysfunction? Only if we review our history and learn from it.