a fascinating article on perceptual learning, a branch of psychology I was not aware of, but that may be relevant to explaining why many individuals and organizations are skeptical of visualization. I need to go the bottom of that Wikipedia entry and read the primary sources.
If you do infographics or data visualization for a living, I'm sure that you've found this objection more than once: "This is too complicated! Our readers won't understand anything here!” —meaning: "I don't understand it myself, and I'm not willing to put any conscious effort in decoding it, therefore no one will see anything.” The non sequitur here is obvious.
The article provides some clues as to why this is so common: To read visualizations, and use them to explore complex information or present it to others, first we need to train our eyes and brains to unweave graphics with little or no conscious effort. Or someone needs to teach us how to do it. Reading visualizations is a lot like reading words: The more you do it, the better and faster you'll be able to do it.
I'm sure that the first time that William Playfair designed a line chart, he was anticipating some pushback from readers, as bar charts weren't common currency before he launched his 1786 Commercial and Political Atlas. Numerical data was usually presented as tables at his time.
We take line charts for granted today, and we're able to interpret them without even thinking (that's the ‘sixth sense’ mentioned in the article,) because we've seen them for centuries. And because there was someone —Playfair— who took a risk to develop that new way of displaying his data, and used it repeatedly. That's why I appreciate the work of people who take risks nowadays, like Giorgia Lupi, Santiago Ortiz, or Jer Thorp. Even if I think that some of the novel graphic forms they devise are incomprehensible and will eventually be forgotten, I also believe that the ones that survive the scrutiny of time will become part of the vocabulary of visualization.
The final paragraph in the article indirectly mentions something that sounds a lot like exploratory data analysis:
"Scientists often think of visual images like graphs as the end result of their analysis. I try to get them to think visually from the beginning.”