Checklist for Technological Innovation in Data Science
Lately I’ve been thinking about how one decides to adopt new technology or change the way one works. There’s a lot of push for Data Scientists to keep up with the latest and greatest innovation in IT or analysis (e.g. change to
git rather than staying with
svn) but there’s rarely any objective analysis or thought put into whether or not a Data Scientist should invest the time and energy in doing this. Therefore I decided to put together a list of principles that I hold and use a a yard stick when presented with such situations. These are presented below.