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Defining and Differentiating the Role of the Data Scientist

by Doug Laney  |  March 25, 2012  |  6 Comments

The research note, Emerging Role of the Data Scientist and the Art of Data Science, I authored with colleague Lisa Kart just hit the Gartner wires this week. Since most of the data scientist role dissenters¬† we come across seem to believe that the role’s title is is nothing more than a pretentious moniker for a statistician or business intelligence (BI) analyst, we decided to take an…er…scientific approach to making that determination. We thought it would be entirely fitting to perform text analysis of hundreds of job descriptions for “data scientist,” “statistician,” and “BI analyst” to learn what the commonalities and differences are according to those actually hiring for the the role.

Data Scientist Job Description Wordcloud

I’d like to believe that these findings led us to more clearly define and distinguish the role of the data scientist, without speculation, than anyone else to-date. Through our research we learned that data scientists are expected to work more in teams, have a comfort and experience with “big data” sets, and are skilled at communication. They also frequently require experience in machine learning, computing and algorithms, and are required to have a PhD nearly twice as often as statisticians. Even the technology requirements for each role differed, with data scientist job descriptions more frequently mentioning Hadoop, Pig, Python and Java among others.

The piece then goes on to define and describe the three core data science skills: data management, analytics modeling and business analysis. But beyond these, there’s an art to data science. We detail several soft skills that our research showed are also critical to success, i.e., communication, collaboration, leadership, creativity, discipline and passion (for information and truth).

With the need for data scientists growing at about 3x those for statisticians and BI analysts, and an anticipated 100,000+ person analytic talent shortage through 2020, we also included a listing of university programs around the world offering degrees in advanced analytics.

Category: 

Tags: analytics  big-data  busienss-intelligence  data-science  data-scientist  statistician  

Doug Laney
VP Research, Business Analytics and Performance Management
1 years at Gartner
25 years IT industry

Doug Laney is a research vice president for Gartner Research, where he covers business analytics solutions and projects, information asset valuation and management, "big data" strategy, and data-governance-related issues. ...Read Full Bio


Thoughts on Defining and Differentiating the Role of the Data Scientist


  1. Andrew Maclaren says:

    Please forward information on the programs being offered at various academic institutions and other related information.

  2. I agree with Doug. I consider myself a data scientist. The three major elements in my background and continued operation are database architecture, business intelligence reporting, and data mining. I have worked professionally in all three of these areas, and all three are necessary elements of building the “business organism” in a company. Many companies operate in a “brainless” manner, with no integrated circulatory system providing the business information “nutrients” to support the business body, and no “digital nervous system” (a’ la Bill Gates) in which to vend products of data mining done in the business brain. All three of these elements are necessary to provide the communication and support pathways to bring decisions based on data mining analyses into action to create profitability in the company. This is the approach I present in my book, “Handbook of Statistical Analysis & Data Mining Applications”.

  3. Recently, I didnt give lots of consideration to leaving comments on site page articles and have placed comments even much less. Reading via your nice posting, will assist me to do so sometimes.

  4. You actually make it appear really easy with your presentation however I in finding this topic to be actually something that I feel I would never understand. It sort of feels too complicated and extremely large for me. I’m taking a look ahead on your subsequent publish, I will try to get the cling of it!

  5. Doug Laney says:

    Thanks Neil. Certainly not an easy role. Perhaps individually one of the most well-rounded people you’ll find…if you can find any. Otherwise a team approach can work. -Doug

  6. Bob Smith says:

    It’s interesting that I never took “Data Science” seriously since it seemed so easy.

    My background is in theoretical physics so to me this is child’s play and sure beats living in poverty.

    I’ll give it a shot.



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