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.

1 – The new tool or method should be cheaper than the one it replaces.

2 - It should be at least as fast as the tool or method it replaces.

3 - It should do work or produce work that is clearly and demonstrably ‘better’ than the one it replaces.

4 - It should be repairable and maintainable by people inside the organisation, provided that they are provided with the necessary tools.

5 - It should not replace or disrupt anything good that already exists, and this includes people and community relationships.

Written on March 27, 2018