Business Analytics: Do we need data scientists?

Moderated by Lawrence Dignan | May 13, 2013 -- 07:00 GMT (00:00 PDT)

Summary: In a world where data drives everything, do we need data scientists to make sense of this tidal wave of information?

Robin Harris

Robin Harris




Andrew Brust

Andrew Brust

Best Argument: Yes


Audience Favored: Yes (56%)

The moderator has delivered a final verdict.

Opening Statements

The required skill set is real and uncommon

Robin Harris: Big data is a new field where we're still figuring out needs, wants and tools. It is too early to commoditize data analytics. Beyond that, the kind of mind that can do deep analysis on massive data is not common. Just as Adobe hasn't made everyone a skilled designer, easy-to-use data analysis tools - if we had them - won't make analytics accessible to everyone or even most. The combination of curiosity, domain expertise, technical understanding, statistical skill and insight will never be a commodity. The term "data scientist" may be pretentious, but the required skill set is real and uncommon.

Even in the era of hundreds of millions of handheld computers, we still have - and need - computer scientists. It's the same with big data. 

We need tools that empower knowledge workers

Andrew Brust: “Data scientist” is a title designed to be exclusive, standoffish and protective of a lucrative guild.

Big data and analytics are powerful, and the technologies around them are exciting. But if they can only be harnessed by highly-paid specialists, then they haven’t fully evolved. We need data and analytics technologies, but we shouldn’t need expensive, scarce, Shaman practitioners to use them. We don’t need data scientists, we need tools that empower knowledge workers to do big data analytics on their own.


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  • Knowledge Workers Need to Become Data Scientists or They Should Hire One

    The data scientist is a crucial resource for corporations dealing with massive data. A data scientist is a knowledge worker that knows how to tap into data using the tools we have today. Knowledge workers don't usually have the complete skill set to leverage today's analytical tools.

    As for the argument that analytical tools need to evolve, the only way an analytical tool will "evolve" is if it is built specifically to analyze a specific data set in a specific way. In other words, someone needs to tell the tool how to think and how to analyze the data, which limits the scope and usefulness of the tool. For data scientist not to be needed, each company will have to build it’s on set of BI tools to perform the specific analysis it is trying to do.

    So you can hire a set of developers to build single purpose tools, or you can hire data scientists to work with the tools available today. (Either way it is going to cost you!) General tools designed to work with general data will never "evolve" and will always need a data scientist to harness their power.
    Reply Vote I'm for Yes
  • It's the Data Economy Stupid

    Maybe they are scientists, or really good data analysts, but the bottom line is simple: In order to do "big data," meaning forward-looking analytics, predictive and/or discovery, you need to have some people that (a) really understand the data, and (b) really understand statistics. Despite heroic attempts at more sophisticated visualization by the likes of Tableau Software, SAS, and most recently SAP to bring big data "ease of use" to the common business analyst or user, the really high-value stuff requires an expert. Organizations that want to enjoy positive economic impact from big data will need some experts.
    Reply 1 Vote I'm for Yes
  • In the world of Computer Science, we would say ...

    ... that your business needs people trained in, and familiar with, Data Structures. The old saying "garbage-in, garbage-out" applies here. When businesses first move their data into an on-line database, they store it as if it is still on paper. Well, sorting electronic "sheets of paper" is no more efficient than sorting physical sheets of paper.

    The larger the organization, the more important it is that your IT department have on-staff people who not only know how the business works, they need to know how all of the data the business depends upon is related to each other. If that is not well understood, your DBMS (database management system) will be no better than that file cabinet full of sheets of paper stored in folders.
    M Wagner
    Reply 1 Vote I'm for Yes
  • It may be more common than you think.

    "The required skill set is real and uncommon"

    It may be more common than you think.

    Just because somebody doesn't use the exact words "data scientist" on a resume doesn't mean he/she doesn't have the skill. The vast majority of computer scientists are taught a wide variety of skills related to information theory, database programming, statistics, and other skills useful for "data science."

    I'm willing to bet there's actually plenty of "data scientists" out there, it's simply that employers don't know where to look.
    Reply Vote I'm Undecided
  • I don't know about "data scientists"

    But you certainly need people who understand logic and statistics - and there are not many of these people about.
    Reply 2 Votes I'm Undecided
  • How can any logic support "no"?

    It is like asking: "Do we need teachers?" or "Do we need doctors?" or "Do we need software engineers?"

    "We don’t need data scientists, we need tools that empower knowledge workers to do big data analytics on their own."

    Is a cop-out of someone that has no knowledge that these tools are derived, developed and modeled by "shamans" with extensive knowledge of math, probability, statistics and the current business models driving the data. These are combined to create models and the models are then tested and rolled into tools. Take away the data scientist and you are left with a 1990's database and nothing more.
    Reply 2 Votes I'm for Yes
    • Specialists

      +1. Mr. No doesn't get it; he thinks a tool as easy (or probably easier) to use as MS Excel should be created so Mom and Pop can DIY their own big data. The Data Scientist is a specialist, just like other fields.
      beau parisi
      Reply 1 Vote I'm Undecided
  • Data Science has always been with us and is more important than ever

    No matter what you call the person, those who use probability and statistics to help us continue past success and avoid past failure will always be important and useful to any organization with analyzable data, and that is especially true with ad model online publishing where targeted marketing of whatever is more critical than ever.
    Reply Vote I'm for Yes
  • Analysis and analytics

    While it is true that, as we gather more and different varieties of data, we need tools to extract, interpret and assemble them - often buried in the term "analytics" - analysis is quite a different beast. The simplest form of analysis could be spotting "outliers" - the highest, the lowest, the most expensive, the least expensive etc. Then we move to statistical analysis using various regression approaches. But to go beyond that requires ingenuity and expertise that defy simple categorization.

    It's worth looking at the development of economics during the last 60 years. There were the mathematical models of studying correlations that started the craze of econometrics. But very soon we started to see sophisticated applications from linear programming and game theory being used in models. Would you call these people "data scientists?" Modelers, perhaps. Economists, definitely. Often they are, like, John Nash, mathematicians, or like Daniel Kahneman, psychologists. Once data is generally accessible then experts from other fields can also bring their special analytical skills to bear on the topics - and the field will evolve. Could Economists on their own developed the Game theory of Von Neuman? Perhaps. But it is unlikely since their education does not include such mathematics.

    In short, prescribing the skills when we don't even know where the field is moving is not prudent. Just make the data available and the people with skills will come. Once the field stabilizes then we can replicate skills through appropriate training. Innovation is not the result of training.
    Reply Vote I'm Undecided
  • The tools are easy

    The real work of a data scientist is putting the data together from different sources and to clean the data. Tools will not do it for you, there are too many variables. So either you can wait until the tools and AI is ready to do the job and see your business falling far behind the competition, or you pay someone and stay current with your business.
    Reply 1 Vote I'm for Yes