(A) The worker, statistician or data
scientist, sends resumes looking for jobs.
Well, that job has to be provided by
someone else, likely someone who at least
eventually gets some real value from the
worker. And the person's training has to
be at least close to what the job needs.
So, the person is one step away from the
actual money making.
(B) The academic teaches those workers to
be but is two steps away from the actual
money making. So, what the academic
teaches has to be quite powerful quite
Should be able to get a better fit if
the academic or the worker is the one in
the business with the problem that has a
very valuable use for statistics, data
science, or some such. Then the academic
or worker can focus on what is really
valuable for the business. Else the
worker and academic are a bit far from the
money, so far they may have a tough time
ever seeing the money.
A famous recipe for rabbit stew starts off "First catch a rabbit ...."
Well, a first recipe for applied math/stat is "First get an application ..."
Still better, pick in a coordinated way the pair of the problem and the method of solution. For business want lots of people/money to have the problem and like the solution. Also want a Buffett moat: Network effect, natural monopoly, a brand name with a lot of power to get and keep customers, maybe some crucial core technology secret sauce difficult to duplicate or equal. Now try to exploit computing and the Internet. Then want LUCK of timing, etc.
New data science with predictions is another way.
There's also a third way!