If your sister were a heart surgeon you may
want her to write you a prescription for your allergy pills or antibiotics for
your child's ear infection, but you certainly will not ask her to see if your glaucoma
is ready for surgery. Most advanced fields have specializations that are
fairly distinct in terms of skills, knowledge, and aptitude, that’s something
we all take for granted. Why is it then that job openings in the
analytics sphere are so convoluted or have one-size-fits-all kind of air that
reek of the hiring managers’ incompetence or ignorance? Either they do
not understand what they hope to achieve, or their organizations are just not
ready for serious analytics. Hospitals hiring general physicians would be
absolutely unremarkable but if all the positions were allocated for general
physicians then that would definitely raise eyebrows. Yet, we see most job
requirements in analytics looking like someone wants a heart surgeon, a
gastroenterologists, and an ophthalmologist all wrapped in one person. Such
a knee-jerk manner of hiring analytics experts would at the least create
inefficiency and on the other hand it may also turn out to be a massive
opportunity cost with regard to potential for growth of the firm had the
analytic investments been optimized.
This defeatist attitude towards hiring of
analytics experts happens primarily due to lack of awareness about the field of
analytics, its various segments, skills and aptitude of analysts. And that is
what I would like to address with this piece. For the uninitiated, here is a
typology that should be helpful in understanding the House of Analytics.
Data Hoarders: These are technologists whose lives revolve around storing data
efficiently and reliably. They have
little to do with analytics.
Data Gophers:
They are programmers trained in data manipulation. They can extract,
transform, and load data for analysis and they respond to requirements. These
people straddle technology and communicate with crunchers and modelers.
Data Crunchers: Crunchers are reactive data analysts mostly. They respond to
requests and mainly create reports and solve structured business problems given
to them by business decision makers. . Often times, proactive
crunchers come up with insights of their own but for most part they are focused on "What is going on" and the
objectives given to them by decision-makers.
Data Modelers:
Data modeling is the organization
of data into tables, and accounts for access, performance and storage. Data
modelers are not your typical data analysts. They work with business decision-makers and users of
information in an organization to create data models that fits their requirements.
They mainly make predictive models and come from the
statistics/computational side of the world.
Data Evangelists: Though short lived, they will be responsible for bridging the
chasm between data and business decisions. Data evangelists need great
communication and sales skills as they have to understand the world of data in
their organizations as well as manage the proclivities of business decision
makers. These people look for
problems and define them for solutions. They go beyond "what is
going on" to "why is it going on". They interface well
with the data users.
Data Users:
Today an investor does not need to download and analyze financial data on a
spreadsheet. All of that is readily available. Nor does a
statistician need programming training to run models or a photographer need
sunlight measurements. We are well on our way towards reducing the need
for modelers & crunchers because technology will make all of that obsolete.
This means that the user will need to be better trained than they
currently are about analytics. When that happens, we would be able to say
analytics has become pervasive in business. Today, however, that is not the
case. Till then data users will need Data Evangelists to help them become proactive
data users.
It is possible that a single person may be
doing all of the above in a given organization but the roles are distinct and
in some organizations each function will be an independent responsibility.
The jack-of-all-trades analytics expert model is highly inefficient and
organizations will need to change to the centralized analytic team model if
they are to compete on analytics. The other problem is one of aptitude. You
would not expect a professional chef to work as a computer programmer as well. Similarly,
a modeler would not like to, or have the temperament to do the gopher’s job and
an evangelist would feel like a fish out of water if she has to spend bulk of
her time with statistical models. Top Gophers have spent 20 years with
SAS/SQL and love their life that way. Hiring managers need to be
cognizant of these differences if they are to build great analytic teams.
Published by The 8020Strategy Group,