It’s the end of the year, which means the world is filled with “End of Year” lists rounding up the top 5s, 10s and so on of every possible interest. While lists of the top 10 albums of the year have been a staple, this year I stumbled across Flowing Data’s list of the Best Data Visualizations of the year. Discussions of the list’s topical authority aside, as I perused, I was struck by how unintelligible many of these visualizations were to the layman’s eye.
This particular example is a visualization of an algorithm. Now, while that is a relatively abstract undertaking, the results are more visually appealing than they are enlightening — at least to my untrained eye (if you understand what this means, please enlighten me). But, I think it highlights an important distinction between data visualization (which at this point is a fairly well understood field) and information translation (which is the highest ideal espoused by the War on Stupid).
Data visualization is a repackaging without critical analysis. At it’s best, it provides a different way to understand information, but making that information more broadly accessible to humanity isn’t necessarily the goal. Meanwhile, what we call information translation is an attempt to make ideas/events/processes/etc. more readily understandable by presenting them in alternative forms. This is a key distinction.