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Data Science is Dead - Long Live the Data Scientist

While there is much hype and confusion around "data science" and the hot new professional "data scientist", there appears to be a growing number of articles and blogs asserting a data scientist bubble and the coming death of data science.


But dear reader, not to worry, because new data tech will replace the data scientist. These claims are usually made by denizens of tech firms with interest in selling new tech and data engineers jealous of real professional data scientists (not fake data scientists) who create meaning and value from data for strategic, tactical and operational advantage - and thus earn higher status and more money. Data engineers and IT departments are understandably furious that organization leaders consider them a burdensome "cost" yet data scientists are perceived as "revenue generators" creating value and real advantage (in reality data engineers and data scientists need each other and are part of a team). 

To be fair, data engineers and IT shops lost credibility with leaders when they bought the "big data" hype and wasted time and money on dubious and immature new data tech. When the result was little, if any, value from the data, the blame game followed and leaders looked to the new profession of data science in attempt to obtain value. However, many folks confuse real data scientists with garden variety data and business analysts - some of whom are starting to call themselves data scientists thus confusing the market. As a result, there may indeed be a fake data scientist bubble, while real professional data scientists are in short supply.
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While data tech is very important and data engineers and data analysts a critical part of the data science team, at this time - and for the foreseeable future - tech cannot replace highly educated and trained data scientists who have deep analytical skills, scientific training to detect signal from noise and avoid deadly data traps that create an illusion of reality, and the ability to find valuable, actionable insights.

If something appears too good to be true, it usually is too good to be true. It would be wonderful if new data tech could replace the professional practice of data science. The builder of such tech would add huge value and make a massive fortune.

Unfortunately, like premature claims of real artificial intelligence (not fake AI like brute force calculations), such tech has not yet (knowingly) been invented (may be reality in future). While sophisticated machine learning algorithms can add significant value - they are not artificial intelligence (yet) and still require data scientists to design, execute, constantly modify and interpret meaning.

Beware of tech firms selling you data tech with fantastic claims of finding meaning in data and creating competitive advantage.
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Tags: Analysts, Bubble, Data, Death, Engineers, Profession, Science, Scientist, of

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Comment by Sean McClure on October 20, 2014 at 9:45am

"tech cannot replace highly educated and trained data scientists who have deep analytical skills, scientific training to detect signal from noise"

Best thing I've read all day. There is no lack of evidence that REAL data science is the future. The problem is with vendors selling rehashed BI tools and convincing organizations that this is data science. The problem is with these ridiculous data science courses being offered that tell people they can become a scientist in 12 weeks (what I like to call "certificate scientists"). The problem is people are being told that data science is something new. Data science is science and it has been around for a very long time...well before businesses ever decided to utilize its power to push their bottom lines. Of course there is gap of real scientists...we have exploded into the information age and companies are now playing in a market that is driven my massive amounts of data.  Scientists have been analyzing data and building models since the scientific enterprise began. Data is simply 'captured activity' and the entire purpose of science is to analyze this captured activity and develop theories and models that explain the phenomenon and use these explanations to predict what will come next. This requires training in SCIENCE. It is in no way different because businesses are now competing by using data. You will not compete with data by relabeling an analyst as a data scientist.  You will not build machine learning products by repurposing IT teams with vendor-based out-of-the-box 'solutions' to Big Data.  The ROI on data science requires the exact same effort and depth of expertise that science has always required. Data science is science. That isn't going to change. 

 

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