Here are a few of my notes (my words not the interviewee's):
- in order to use data science, you have to have creative people thinking about data on the front end
- they don't have to be data scientists, but they need to be creative and want data to support decisions and iteration via feedback loops
- that creativity and desire will lead to "doing good data science"
- management on the receiving end of data science output must be intelligent in terms of synthesizing many inputs and have a strong desire to puzzle through the implications. If management is asking the data science to actually make the decisions - the situation is broken
- data science must be done with provisions for decision support and feedback loops; this is the output that is helping drive the business.
- Lack of desire for decision support and feedback loops leads to "fancy pets" and management using data science as a means to brag about what they are doing; but the data science might not being doing anything to drive the business meaningfully.
- data science that attempts to actually make decisions vs providing decision support is likely in the category of "commodity data science". Corollary : non-commodity data science is the kind that supports decisions in executing higher-level business strategy. Strategy at that level has rather unique attributes and is embedded in unique circumstances for a particular business. This requires a good data scientist to help tackle.
(hope this is useful)
(edit typos,grammar)