Plug in any dataset into a magic box and it spits out a lovely visualization you can show all of your co-workers, friends, and family. That’s the promise of a lot of startups, but it doesn’t quite work that way. Ian Johnson explains by comparing visualization the medium to other forms of communication.
I want to take a deeper look at why this pursuit of automation is misguided, and in the process hope to point out potentially more fruitful paths. I intend to do this by looking at how other communication mediums have come about via technology, what the authorship tools look like and how they evolved. We will start with the most recent medium and go back in time, getting deeper into the essence of augmenting human communication with technology.
Some (many?) might argue that automated visualization is a worthwhile pursuit. And I would agree that some parts of visualization certainly should be automatic, such as standard chart types and recurring geometries. Pieces of visualization, such as annotation and axis construction can be automatic. There are plenty of tools to make our lives easier.
But full on automation where insight fountains out from any dataset is farfetched at this point, because this requires automatic analysis. Analysis is context-specific and requires more than boilerplate statistics. The most interesting visualization is context-specific.
