Along with the usual pomp and celebration of college commencements and high school graduation ceremonies weâre 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 âanalytics professionalâ today, whether youâre just entering the workforce, youâre 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 âanalytics professionalsâ do: The list includes âstatistician,â âpredictive modeler,â âanalyst,â âdata minerâ and â most recently â âdata scientist.â The term âdata scientistâ is probably the one with the most currency â and hype â surrounding it for todayâs graduates and upwardly mobile analytics professionals. Thereâs 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 âdata scientistsâ actually do and the skills they require to do it, itâs 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 thatâs why I prefer the term âdata artistâ 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 âdata scientistâ 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 thatâs 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 theyâll be working with and regardless of what title they have on their business card. This is especially true when using the complex, hybrid âall-of-the-aboveâ solutions that weâre 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 âanalytics professionalâ you may be or may want to hire, you need to prioritize creativity, curiosity and flexibility â the âartistryâ â of the job.
To read the original article on Forbes, click here.