that's often the current explanation for continued use of Pyhton and R. Often it is a sign that the problem is not "big" enough (eg: not crunching truly large

that's often the current explanation for continued use of Pyhton and R. Often it is a sign that the problem is not "big" enough (eg: not crunching truly large data sets) OR data science team gets disproportionate amount of goodwill (thus money) to spend on its foibles. :)