Creating a visualization can be one of the most efficient ways of concentrating data for the highest payoff. Some data sets seem to be begging to be graphed, charted, or otherwise imaged; just take a look at the famous hockey stick graph, the Guardian’s interactive visualization “Everything you need to know about climate change”, and almost any of the topics on The Independent’s DebateGraph - especially this one on The Future of Newspapers. Some of these topics you would never expect to see graphed; it really is amazing.
When there’s a gargantuan pile of numbers and words with an important meaning or message obscured therein, the best thing is usually to take that data and map it into the most manageable form.
The reason I chose this SMBC comic to meditate on, though, is the way it neatly sets out the problem with simplifying data into a visualization: It’s sorting through data and denoting and selecting what’s important and re-presenting it. It’s simplifying data.
That gargantuan pile of words and numbers sometimes needs most of those characters arranged in their specific ways to make perfect sense. Scientific research is expanding our circle of that which is known and the scientific method demands proof, beyond evidence, of all assertions. To convey all that is found to the exacting standard of method, the encoding language must be incredibly specific. Simplifying the language can often obscure or misconstrue the actual meaning.
However, the huge amount of technical information a reader would need to exactingly interpret a scientific text does make conveying new information to your average media consumer difficult. Simplifying information, and in effect obscuring its meaning; this is something science just does out of necessity. In high school I remember being taught simplified versions of processes one year, and more complicated versions (that actively contracted previous teachings) the next. Tertiary friends report the same phenomena.
In an area as technical as scientific inquiry and research I suppose you have to do the best you can to communicate it efficiently, and hope that the visualizers, reporters, and others who interpret the data make careful decisions about which information to select and how to use it.
(See the paired comic about science publishing here)