Along with the usual pomp and celebration of college commencements
and high school graduation ceremonies we’re seeing now, the end of the
school year also brings the usual brooding and questions about careers
and next steps. Analytics is no exception, and with the big data surge
continuing to fuel lots of analytics jobs and sub-specialties, the
career questions keep coming. So here are a few answers on what it
means to be an “analytics professional” today, whether you’re just
entering the workforce, you’re already mid-career and looking to make a
transition, or you need to hire people with this background.
The first thing to realize is that analytics is a broad term, and
there are a lot of names and titles that have been used over the years
that fall under the rubric of what “analytics professionals” do: The
list includes “statistician,” “predictive modeler,” “analyst,” “data
miner” and — most recently — “data scientist.” The term “data
scientist” is probably the one with the most currency – and hype –
surrounding it for today’s graduates and upwardly mobile analytics
professionals. There’s even a backlash against over-use of the term by those who slap it loosely on resumes to boost salaries and perhaps exaggerate skills.
Labeling the Data Scientist
In reality, if you study what successful “data scientists” actually
do and the skills they require to do it, it’s not much different from
what other successful analytics professionals do and require. It is all
about exploring data to uncover valuable insights often using very
sophisticated techniques. Much like success in different sports depends
on a lot of the same fundamental athletic abilities, so too does success
with analytics depend on fundamental analytic skills. Great analytics
professionals exist under many titles, but all share some core skills
and traits.
The primary distinction I have seen in practice is that data
scientists are more likely to come from a computer science background,
to use Hadoop, and to code in languages like Python and R. Traditional
analytics professionals, on the other hand, are more likely to come from
a statistics, math or operations research background, are likely to
work in relational or analytics server environments, and to code in SAS and SQL.
Regardless of the labels or tools of choice, however, success depends
on much more than specific technical abilities or focus areas, and
that’s why I prefer the term “data artist” to get at the intangibles
like good judgment and boundless curiosity around data. I wrote an article on the data artist for the International Institute for Analytics (IIA). I also collaborated jointly with the IIA and Greta Roberts from Talent Analytics
to survey a wide number of analytics professionals. One of our chief
goals in that 2013 quantitative study was to find out whether analytics
professionals have a unique, measurable mind-set and raw talent profile.
A Jack-of-All Trades
Our survey results showed
that these professionals indeed have a clear, measurable raw talent
fingerprint that is dominated by curiosity and creativity; these two
ranked very high among 11 characteristics we measured. They are the
qualities we should prioritize alongside the technical bona fides when
looking to fill jobs with analytics professionals. These qualities also
happen to transcend boundaries between traditional and newer definitions
of what makes an analytics professional.
This is particularly true as we see more and more enterprise
analytics solutions getting built from customized mixtures of multiple
systems, analytic techniques, programming languages and data types. All analytics
professionals need to be creative, curious and adaptable in this
complex environment that lets data move to the right analytic engines,
and brings the right analytic engines to where the data may already
reside.
Given that the typical “data scientist” has some experience with
Hadoop and unstructured data, we tend to ascribe the creativity and
curiosity characteristics automatically (You need to be creative and
curious to play in a sandbox of unstructured data, after all). But
that’s an oversimplification, and our Talent Analytics/International
Institute of Analytics survey shows that the artistry and creative
mindset we need to see in our analytics professionals is an asset
regardless of what tools and technologies they’ll be working with and
regardless of what title they have on their business card.
This is
especially true when using the complex, hybrid “all-of-the-above”
solutions that we’re seeing more of today and which Gartner IT -0.48% calls the Logical Data Warehouse.
Keep all this in mind as you move forward. The barriers between the
worlds of old and new; open source and proprietary; structured and
unstructured are breaking down. Top quality analytics is all about being
creative and flexible with the connections between all these worlds and
making everything work seamlessly. Regardless of where you are in that
ecosystem or what kind of “analytics professional” you may be or may
want to hire, you need to prioritize creativity, curiosity and
flexibility – the “artistry” – of the job.
(Forbes)
No comments:
Post a Comment