research design laboratory

A rainbow is nature's infographic

Tonight I attended a Montreal Girl Geeks Session entitled Data Visualization for Fun and Profit with Julie Steele. Julie Steele has coauthored two books I intend to read: Designing Data Visualizations: Intentional Communication from Data to Display and Beautiful Visualization: Looking at Data through the Eyes of Experts. So the woman knows a thing or two about data viz!

The talk was a really great refresher about effective communication design, reminding us of the sometimes subtle if not subliminal powers of colour, shapes, layout, typography, and cultural reference points, as vehicles of meaning. Steele's approach to (static) data visualization is decidedly linear and academic; each representation of data begins with a specific question. Steele also explains the difference between infographics (smaller data sets with manual outputs) and data visualization (larger data sets with algorithmic outputs), and the different roles each can have in exploring, or explaining, data. As Steele points out, data visualizations and infographics are not only everywhere, but are extremely persuasive; it's time we have a shared vocabulary to understand them as much as the technological tools to create them.

The talk left me with this feeling: if the starting point of most data graphs is a specific question to be answered through data visualization, and yet that question is rarely made explicit, do these beautiful and compelling arrangements of shapes and colours risk concealing important alternative readings, and more so than their more simple and less seductive 'mathematical' equivalent? And if academics have long learned to be weary of statistics without context, shouldn't we do more work toward untangling the data that feeds the base? Should more attention be paid to tracking down the 'why' than the 'how' at this juncture? Can data visualization itself be used to counter the very notions of raw data, truth, and objectivity on which it rests?
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