At year end 2013, a probing article appeared in CMS Wire, “The End of Data Scientists and Other Predictions.” I remember reading it at the time and thinking to myself, “Oh well, I better fire up a screenplay template in Word like everyone else here in LA and start typing because I’ll be out of work soon.” Well, we’re now well into 2014 so I thought I’d take a look around to survey the climate for the technical field I’ve been active in for decades.
To set the stage, let me provide some excerpts from the article to see how they apply right now:
- Tableau said that the technological advances made in 2014 will enable ordinary workers to become capable of generating the same kind of insights as data scientists are known for. The company added that data analysis will become a skill that business users possess vs. only experts with “analyst” in their titles.
- Alteryx echoed Tableau’s sentiment: “Analysts will matter more than data scientists,” they said. “Empowering analysts in business departments with big data and analytics will become more important than filling the perceived need for millions of data scientists.”
- SAP KXEN’s VP of Marketing, Andy Savitz, said that his company’s product InfiniteInsight, revolutionizes forecasting in two ways; first, it empowers business analysts with data scientist-like capabilities by automating much of their hard, messy and time-consuming work; and second, through the same automation, it can build more models with more variables (thousands versus hundreds) faster than large armies of data scientists.
- Splunk’s big data director Brett Sheppard said that line-of-business employees will soon be able to leverage technology to ask and answer their questions using raw, unstructured data from disparate sources. “They won’t need to rely on specialists to unlock the value of big data,” he said, adding that they’ll soon be able to use data to help make decisions that they previously might have made on gut-feel alone.
Reading these “predictions” now and realizing how much the need for data scientists has in fact accelerated, I get the distinct impression that the above comments were likely imagined by a bunch of marketing-types just wanting to increase the value of their ready-made analytics solutions, and dismissing the need for the people who really understand the principles behind the products in terms of mathematical statistics, probability theory, machine learning, and computer science. Data science is not some passing fad. It’s been around for decades under different names like “business intelligence,” “data mining,” “knowledge discovery in databases,” and just plain old statistics.
All this automatic data science talk reminds of years ago when people actually thought that programmers would be made obsolete by “automatic-programming” products. Ha! You all know how that turned out. Programmers won’t be obsolete any time soon as the field of data science still requires human insight and intuition. So for now, I’d say that while data scientists indeed may be losing their sex appeal, SkyNet is not just around the corner.
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