Hilary and Roger touch on some interesting topics in the most recent Not So Standard Deviations, specifically on scalable and automated data analysis.
At the surface, it can seem like computers should be able to do the bulk of any analysis. Plug in the data, crunch the numbers in an algorithmic black box, and presto change-o you get a list of actionable insights. From that point-of-view, you should be able to build software that does almost everything for you. That’s almost never the case, and you realize it quickly once you dig into the data yourself.
It’s the same deal with visualization.
You see the end result, and it’s easy to imagine applying the same chart to another dataset. Geometry and color are easy to make with a couple lines of code. The chart should be generalizable, right? Sure, but the challenge is getting to that final point. There are various paths you can take when you start with a dataset — what it means, the questions you want to ask — along with various decisions along the way.
Automating the process. That’s the hard part.