Cargo cult data science
This is all very old stuff.<p>One earlier version was for AI expert
systems.<p>Then there was object request broker
architecture.<p>Such considerations were ubiquitous for
the biggie operations research (OR) with
optimization, simulation, etc.  OR was so
big that it was required in B-school
programs.<p>Similarly for management science.<p>The lessons for how to make applications,
as in this OP, were all there in the past.
Indeed, operations research (OR) and
management science (MS) merged to become
OR&#x2F;MS with a journal <i>Interfaces</i> that
talked a lot about the points in the OP.<p>I went through a lot of that history and
discovered lessons much like those in the
OP.<p>&gt; Fundamentally, to be a data driven
company, data needs to be part of the
internal dialogue spoken by all members.<p>Okay, let&#x27;s stop right there!  Who the
heck, why, where, when did anyone ever
say, argue, justify that any company
should be &quot;a data driven company&quot;?  Maybe
a &quot;market driven company&quot;, but data
driven?<p>Really, for what kind of company should
have, there is very wide agreement, from a
home based business to Wall Street, and
that is a money making company!<p>What turns on the CEO and the BoD is
making money!<p>But not nearly all projects, data science,
..., Taylor&#x27;s time and motion studies, are
directly connected with making money.
E.g., when I wrote software to schedule
the fleet at FedEx, the main goal was just
a schedule, printed out, on paper, with
departure times, flight times, arrival
times, etc., that would pass expert review
as &quot;flyable&quot;.  Actually, saving money,
i.e., <i>optimization</i>, was of much less
interest.<p>&gt; So, to avoid a cargo cult of data,
organizations should stop chasing
technology and start working with
experienced technologists who can apply
technology to solve organizational
problems.<p>Yup.<p>&gt; Executives, to understand how their
project relates to company goals, and how
success would be reported.<p>Really, reasonably well experienced
problem sponsor executives will ask &quot;Why
should I do that?&quot; and need a good answer
or won&#x27;t do it.  Sure, one reason to do
the project may be just to be playing with
the latest buzz words, but most
organizations have highly sensitive BS
detectors that will be triggered by buzz
words.<p>&gt; With their bosses demanding analytical
results, managers will demand analytical
results from their peers, and so on, down
throughout the subgroup.<p>Why would bosses be &quot;demanding analytical
results&quot;?  How many bosses understand good
analytical results versus a lot of BS,
have an accurate view of the potential of
analytical results, could explain why it
might be good for results to be
analytical, know how to do projects that
yield solid analytical results, or see how
analytical results could help their
careers or the goals of the company?
Answer:  Only a small fraction.  E.g.,
only recently has Wall Street taken
analytical results seriously for trading
instead of intuitive, judgment <i>stock
picking</i>.<p>&gt; My reasoning was simple:  anyone with
data science on their side would be able
to prove that their efforts worked better
than their peers.<p>Then?  How about the peers feel threatened
and mount a gossip and sabotage campaign
against the data scientist and their work?
The management chain can also feel
threatened.<p>&gt; Basically, I had assumed a data-driven
culture exists, when in reality businesses
are struggling to create that culture in
the first place.<p>They are not even &quot;struggling to create
that culture&quot;.  It is a fertile, gullible
imagination that believes that many
organizations believe that they want &quot;a
data-driven culture&quot;.<p>&gt; Data science is best viewed as a form of
company culture, rather than a set of
technologies.<p>No.  Data science is best viewed as a
technique, box of tools, that sometimes
can, likely with work with other tools and
techniques, yield some valuable results.<p>&gt; I argue that it’s best to spread a
data-driven culture from the top of an
organization down, by requiring that
reports be analytical.<p>Neither the spreading nor the requiring
will work.  Only a tiny fraction of the
people in the organizations have
significant ability with data science, and
they will NOT make any such spreading or
requiring of something they don&#x27;t
understand possible in the organization.<p>&gt; Solutions that help measure and improve
the performance of a part of the company
(“we’ll help you measure marketing ROI”,
or “we will introduce predictive
maintenance), will spread and become
enduring organizational strengths.<p>Not really.  For &quot;enduring organizational
strengths&quot; look to, say, high quality
reasoning, writing, and presentations,
powerful innovation, high determination,
careful attention to the markets and the
customers.<p>For &quot;Solutions that help measure and
improve the performance of a part of the
company&quot;, that will be down somewhere near
a good company Web site, good telephone
courtesy, keeping lunch breaks under an
hour, stopping pilfering, having good
computer network management, having good
computer security.<p>Sometimes <i>data science</i>, or just call it
applied mathematics, and the rest of math,
can mean super big bucks for a company:<p>Supposedly a big example is the trading
software of James Simons&#x27;s Renaissance
Technologies.<p>IIRC once the CEO of American Airlines
said that their subsidiary Sabre for
reservations and scheduling was so
important he&#x27;d sell off all the planes and
just keep Sabre.<p>Likely the old linear programming
application of the diet problem is still
used effectively (i.e., save big bucks) in
feed mixing for livestock, cat food, dog
food, etc.<p>Linear and non-linear programming are
likely still pillars of, worth big bucks
for, operating an oil refinery.<p>There may be some big bucks from applying
math to ad targeting on Web sites.<p>For large projects, the old linear
programming application of &quot;program (or
project) evaluation and review technique,
commonly abbreviated PERT, ....  PERT was
developed primarily to simplify the
planning and scheduling of large and
complex projects.  It was developed for
the U.S.  Navy ...&quot;  Closely related is
the &quot;critical path method (CPM)&quot;.<p><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Program_evaluation_and_review_technique" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Program_evaluation_and_review_...</a>