ANALY ZE T H I S !
Yet looking objectively at all the advertisement and public manifestations of ‘analytics’
it is 99.9 percent descriptive stats with either
(A) fancy charts or (B) tables with a gazillion
descriptive statistics.”
Later in his e-mail, he made another
very interesting observation: “The hardest
part about analytics is not, as most people
think, the math. In fact, the math might actually be the easiest part. Analytics require a
lot of thinking and a lot of creativity, ingredients that require time and persistence, both
of which are in short supply in today’s world.
Most managers (and definitely students)
cannot and do not want to spend more than
a few minutes (or is it a few seconds?!) before receiving gratification. Which means
that too often they will take a pie chart or
a summary table and rave about their
analytics!”
I often hear this kind of thing from analytics managers. Patience, persistence and
the ability to function effectively even under a wide variety of pressures (including
a shortage of time) just might be the most
important attributes for successful analytics professionals. Given some foundational
programming and mathematical capability,
the knowledge of a particular coding language or a specific statistical technique can
be acquired more quickly and more cheaply than ever before; however, there are
as yet no effective massively open online
courses for the business effectiveness skills
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A N A LY T I C S - M A G A Z I N E . O R G
(including problem framing, relationship
management, effective communication with
non- and less-technical stakeholders) that
often determine how big an impac t is made.
But don’t get me wrong; I’m not trying
to minimize the importance of what some
of my colleagues call “technical chops.”
The complexity of both the data sources
being integrated and the business problems being addressed under the banner
of analytics is continuing to grow, and the
breadth of capabilities needed to implement effective solutions is often a very
real challenge. With most of my MBA students, I feel like there is a clear ceiling on
how much of the “solution stack” they will
ever truly be able to understand, and I am
frankly unclear on what career limitations
they may face as a result.
On this note, a company recently contacted me because the number of data
scientists on staff had grown substantially
since we had last spoke and these people
had been identified as key corporate assets
to be developed and retained. As part of
this initiative, a few analytics leaders within
the organization had sketched out competencies and job titles for two distinct career
paths – one that led to senior analytics management roles and the other culminating in
a highly esteemed (and very well compensated) senior data scientist title.
When asked for my feedback, I had two
immediate responses. First of all, the very
W W W. I N F O R M S . O R G