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To Be A Data Scientist Or Not To Be: The Title Is The Question

To be a Data Scientist or Not to Be: The Title is the Question

Am I a data scientist?  Are the people I work with?  I am not sure which way to go on this one.

For most of my adult life, I have worked with data and analytics, primarily in marketing environments – and I have been called many things, most of them kind!

I have been referred to as a Predictive Modeller, a Business Intelligence Analyst, a Marketing Analyst, a Data Modeler (which is something else entirely it turns out), “The Data Person,” a Customer Knowledge Creation Analyst and a “math geek”.  My favourite was Modelling Analyst – mostly because, at 5’ 2”, that inspired a lot of funny conversations and guffaws from my family and friends!  I never did get that call from the Ford Agency!

As the profession has evolved, so have the titles.

Today, the majority of the people in this field, especially the more recent entrants, are referred to as “Data Scientists.”

And I am a torn over this name.  

On one hand, there is no question that the profession has always been one closely aligned with the disciplines of science; and, as it has evolved, it is even more so.  Today, many ‘data scientists’ are stronger programmers than my peers and I ever were, working with far larger and less-structured data sets and more complex problems.  It requires technical skills in mathematics and programming, and an understanding of data collection and structures, like many other careers in the science realm.

But, there is also another side to the profession – one that I am not sure is well-served by the name “Data Scientist.”  Many of those that are most successful in this field (at least on the corporate side) are not simply smart people with superb technical skills, they are also business people.  Great analysis comes when people ask good questions and can frame the problem properly.  They understand the business and its needs, and are pragmatic in getting solutions at the speed of business.  And, perhaps most importantly, they can present results in a clear, concise, persuasive manner.

Unfortunately, rightly or wrongly, the term scientist often conjures up the image of someone squirreled away in a basement lab, producing very long papers that can only be understood by others in their field, full of complex formulae and Latin derivations.  This image does nothing to ingratiate the Data Scientist with their business partners.

I think part of where I struggle is that I believe there are many types of analytic professionals who play different roles in organizations; the term “Data Scientist” has come to be a catch-all for many of them; and I am not sure it always fits.  Dave Holtz, a data scientist at Airbnb, has pointed out that in some companies everyone is called a Data Scientist regardless of what they do – or as he puts it “A Data Scientist is a Data Analyst who lives in San Francisco.” (https://blog.udacity.com/2014/11/data-science-job-skills.html).  He also points out that, in some companies, however, where data is their business (think Netflix and Google and AirBNB), the work being performed is truly as “scientific” as in any university physics department.

So does the title fit the work?   Although I hate to quote Wikipedia, it defines “Science” as “a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe”.   If we consider that Data Scientists organize knowledge, test hypotheses, draw conclusions and make predictions, I suppose Data Scientist is as good a name as any.

And, of course, Harvard Business Review tells us that being a Data Scientist IS the “Sexiest Job of the 21st Century” (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).  Maybe it isn’t so far from my Modeling career after all!

On Tuesday, January 16 join me and a panel of others who have been called a lot of names over the years, as we discuss Data Science – the reality and the future, at the SORA Business Analytics Seminar.

 

 

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