I was shocked to read that “the tag clouds train has left Rock City” in a design blog post from April 2005. The post was referenced in a recently published (October 2011) blog by Jacob Harris, the NYT senior software architect, in a larger piece about journalism and data visualization, which is how I came across it during one of my seemingly random perusals of the internet.
Other than the personal embarrassment in the realization that my last few family holiday cards used a customized version of the word cloud to replace the dreadful “holiday letter” (which I have never been guilty of producing but reserve the right to do so in my dotage), I fully understand the disdain with which any journalist would view this simplistic visualization tool. It is, after all, an automatically generated cloud of words with font emphasis to denote frequency of use. The most common free tool for creating one can be found at Wordle.
Social media rating services routinely use category clouds to describe the users most common tweets or subjects of interest (or authority). Sadly, mine usually consists of the most oft repeated RT (for ReTweet) and the now ubiquitous # (hashtag) – I believe a poor indicator of what I truly focus on in the social media space (a more accurate portrayal would be EDUCATION, politics, CINCINNATI, ohio, etc. but these pale in sheer volume compared to what I would consider proper social media punctuation).
So all this got me thinking about data visualization in the education sector and political arenas. Making all that data available and visually appealing while holding on to accuracy is a challenge – particularly in the digital space where agate type resolution is iffy at best and EVERYTHING is footnoted. Enter politics and the reform candidacy of Ross Perot and his infamous pie charts – such simple and clear data visualization tool quickly embraced by the USA Today’s of the reporting sector. But are they accurate? Do they give us a down and dirty quick take on trend lines – or do they etch themselves onto our retinas and define the essence of what is important? In other words, if “what gets done gets measured” are these simplistic data visualization tools defining for us what we should care about? If we dig a little deeper into what a word cloud says about us, who gets to filter the end result?
Secured by