In a sense Data Science is like the Cult of the MBA. MBAs believe a trained manager can manage anything because management skills are generic. A data scientist believes they can analyse anything because analysis is generic. Both fail in the real world because they discount domain knowledge.
Is there a field that does not discount domain knowledge? Or is that just "judgment" and custom analysis? I am trying to understand how all fields map together. Thank you.
The divisions are very confused. I think that sensible people all wish to use domain knowledge if possible. There are two tiers of this, firstly the use of domain knowledge in the manual or procedural construction of the insight system. Secondly the use of formalised knowledge in the creation of models that can then be fused with data.
The first case is where data science has got a bad name; people swing into domains and companies full of cocksure ideas, produce insights that are risible or obvious and get ejected. Sometimes it takes years for sufficient knowledge to be acquired by analysts to deal with difficult domains.
Lots of people use Bayesian inference to do the second. Tools like Stan and PyMC3 are really popular and effective.
