Forbes Says Data Science is a Fad. I Say Forbes is Wrong.

“Any field of study followed by the word “science”, so goes the old wheeze, is not really a science, including computer science, climate science, police science, and investment science.”—Ray Rivera, Forbes Magazine

I too have engaged in my fair share of hand-wringing over “data science”, how the term is used and mis-used, the high quantity of snake oil available, and some generally sloppy practices that seem to be becoming the norm in the internet’s new data-based gold rush.

However, as my mama used to say, “I can beat up on my brothers all I want, but you, sir, are not family.”

Data, harnessed for good, is going to transform our world and the way we do business. People who understand data, the mathematics of how data streams relate to each other, and how computers interact with that data, are going to be indispensable to this process. I don’t always agree with Thomas Davenport and DJ Patil. For example, I don’t think code is the most important skill a data scientist can have (though my Ph.D. makes me susceptible to bias). I do, however, listen to them because they think about data as much as I do. They care about this field as much as I care about this field, and they, generally, are working to make it better. There are a lot of open questions, and as scientists, it’s our job to collaborate in solving them.

You are right on a lot of points, Forbes. For example, I have worked with data for 16 years. In that time, my title has changed from Data Warehousing Specialist, to Data Miner, to Data Analyst, to Data Scientist. I currently hold the official title of “Senior Mathematician” because that is what the industry I work in understands. My job has never changed. I have always been responsible for understanding my customer’s data and turning it into actionable information. I have better tools now, and I have a clearer understanding of how that job can best be done. The field has progressed by leaps and bounds, and so what we call ourselves has changed. That doesn’t mean we think we’re doing something new. It means you think we’re doing something new.

I was going to say “It’s not my fault if you don’t get it,” but that’s not really true. Communicating what I do is part of my job. The title change from Data Analyst/Mathematician/Data Miner/Data Warehousing Specialist to Data Scientist is part of my attempt to communicate with a business community that understands very little that can’t be condensed into a buzz word. The word Data tells you that I transform raw information into actionable information. The word Scientist emphasizes my commitment to making sure that the analyses my colleagues and I produce are verifiable and repeatable—as all good science should be.

Understanding data processing, i.e. code, is part of the repeatability. We need to write good code because we need to make sure someone who doesn’t have our complete skill set can repeat what we’ve done. It’s a service to you, the business community, so you can work on your own. If you think it’s our way to make ourselves indispensable, then you or someone with whom you work are doing it wrong.

I have already expressed my excitement, here and elsewhere, about data science and the many new opportunities ahead. I look forward to collaborating with members of the business community who understand the value of what I do.

I also wish Mr. Rivera and people who think like him the best of luck. They’re going to need it.

About Melinda Thielbar

Melinda Thielbar is a co-founder of Research Triangle Analysts, Ph.D. statistician, spinner of fine yarn, martial artist, fraud analyst, and fiction writer. In other words, she's a polymath. Follow Melinda on Twitter @mthielbar, or join the Research Triangle Analysts group on G+ to join the conversation about data science.
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9 Responses to Forbes Says Data Science is a Fad. I Say Forbes is Wrong.

  1. Rob Klopp says:

    A serious question… not meant to be a loaded question… and a possible blog topic for you…

    Why is “data science” not a repeat of the “data mining” fad of the mid-1990’s… which fizzled for the lack of enough qualified data miners? The tools are not much different. The math is no different. The availability of people is not significantly different? Only the amount of and type of data is different (i.e. it is now “Big” data). I was for it then… am for it now… but I have a hard time seeing where Forbes went wrong? And your note states that you disagree… but you do not really refute his thesis.

    Rather than compare Data Science with BPR (a very weak argument by Forbes, methinks) I want to know why Data Science is not Data Mining Part Deux?

    • That is a really good question, Rob (and not one I consider “loaded”).

      My short answer is this: Data Science, in a lot of ways, recognizes the shortcomings of data mining. Data mining requires a qualified analyst, as you pointed out, and there are not enough of them. It’s arguable that there cannot be enough of them. Data science should (and I agree doesn’t always or even mostly right now) include a way to repeat and verify the results. The repetition and verification should be something that a business expert can do, without the aid of yet another data scientist.

      The long answer does deserve a blog post of its own, and it’s a great topic. Thanks for the comment!

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  4. Reblogged this on Kenkyuu and commented:
    On Data Science:
    The word Data tells you that I transform raw information into actionable information. The word Scientist emphasizes my commitment to making sure that the analyses my colleagues and I produce are verifiable and repeatable—as all good science should be.

    Not sure I agree on the whole argument in the post, but the definition of data science is the best I have seen so far.

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  8. Mei Chiu says:

    Hi Melinda,

    I really admire and appreciate that you highlight a positive definition and perspective for the term Data Science. Data Science: “The transformation of raw information into actionable information by making sure that the analyses my colleagues and I produce are verifiable and repeatable.”

    The argument around the internets I believe, potentially stems from the idea of applying the word Science to the study of something that constantly changes. The analyses of things that change constantly may not necessarily be verifiable and repeatable.

    I am of course assuming that the data in question is data primarily from the behaviors and buying decisions of people. Behaviors change. For example: there are constant patches and fixes to web application bugs and holes, yet hackers find new weaknesses, they adapt, and they can adapt quickly.

    Human behavior in general when it comes to buying and purchasing decisions may not adapt as quickly, but eventually adapt they do. When the term science is applied to the study of something that doesn’t adapt as readily people may be able to stomach that definition a little better.

    Thanks for reading,

    Mei

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