With all the hype around Big Data, one of the most pressing concerns for organizations is finding qualified data scientists who’re able to translate that mish-mash of numbers into something that makes sense – actual insights that can be used to pursue businesses’ goals and strategies.

The opportunities for data scientists are said to be enormous, mainly because there aren’t enough of them to go round, so the story goes. But does that mean the time has come for our universities to start churning out armies of data scientists to fulfill Big Data’s potential? Or is the lack of Big Data scientists simply more hyperbole on top of the Big Data hype?

Chris Taylor, a marketing executive and co-founder of Successful Workplace is of the opinion that there are plenty of data scientists to go around already, and by the time we train more, most analytics will be an automated process anyway.

Writing in Wired.com, Taylor cites a recent article by Meghan Kelly in Venture Beat, which claims that data scientists “may soon become one of the most sought-after people in your industry.” An accompanying infographic notes that there was 15,000 percent growth in the demand for data scientists from 2011 to 2012.

But Taylor brings up three cruical points that Kelly and others have overlooked. Firstly, he points out that the numbers don’t really add up – after all, the word “data scientist” was only really invented a couple of years ago, and this explains why there’s a sudden demand for it.

“The job’s high level of attention is a factor of its newness more than its explosive growth. 15,000 percent can be as simple as going from 1 to 15,000,” Taylor writes.

Taylor argues that data scientists have in fact, “always walked among us”. He points out that until a couple of years ago, data scientists just had a different label, and worked in only a few select industries.

Finally, and this is the kicker, Taylor believes that Big Data scientists could soon find themselves replaced by emerging technologies being developed to make sense of the massive data sets organizations are now producing:

“A new breed of applications are launching now that allow business users to create their own big data applications in the cloud with a much lower level of IT and data scientist support,” Taylor argues.

For these reasons, Taylor argues that organizations should refrain from rushing to hire data scientists by the dozen. He advises that patience is the name of the game:

“There’s a pretty high likelihood that by the time to recruit, hire and put those data scientists to work, someone will be selling tools that reduce the complexity and cost of what your team does,” he warns.

photo credit: Eric Constantineau – www.ericconstantineau.com via photopin cc