Illustration from Inventing the Future (Pangnirtung Fjord)
InDesign document
©2019 Charlene Brown
The objective of data mining is to get the largest amount of useful information out of the mountains of data available, and do it without falling into the trap of measuring stuff just because you can, and then tracking useless data to no avail. The way to do this is to glean as much as you can intuitively before you start quantifying and fitting number-crunching formulas.
I figured this out almost fifty years ago while studying what is now called business analytics. It was called management science when I got my MBA in 1973 and operations research before that. There was probably a lot less data to mine in the early '70s, but it seemed like a lot at the time.
Synthesizing information through visualization, pattern recognition, trend analysis and extrapolation, in other words, going as far as you can in parsing the problem intuitively (heuristically), increases the likelihood of formulating a solvable optimization – and increases the chance the answer will actually make sense.
