A simple trick accounts for much of our success as a species: pattern recognition.
Confronted with complex interactions and wide-ranging inputs, we manage to sort through the clutter, emerging with dots that we can connect and actions we can take. Pattern recognition requires abstraction, distinguishing data from noise. But coherence has many forms, and only through demonstration does it stand as actionable argument.
Data visualization offers that demonstration. Over the centuries, it has served as the haiku of pattern recognition. Any particular visualization privileges specific parameters of experience, with data representing quantity, motion, duration, location, and so on. But the art – and success — of visualization turns on more than simply isolating data: the graphic rendering of that data carries the argument. As Edward Tufte and others have eloquently shown, the right data in the wrong form say little of importance. And as we can see through the long haul of history, getting it right has little to do with technology. The best arguments, it seems, are data-based stories of pattern recognition, whether inscribed on cave walls and strips of wood, or in digital animations and dynamic renderings.