The search for data scientists may not always mean IT

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The New Year is expected to be the one in which big data truly rules, and data scientists have been called one of the most in-demand job roles in IT.

But, of course, data scientists do not have to come from the traditional IT ranks at all. Companies that live big data have a lot of insights to offer to CIOs on the prowl for data scientists: where to find them, how to develop them, what makes them tick. The search may lead outside the IT department entirely.

FierceCIO recently spoke with data scientist experts at two leading data analytics companies. Earlier in December, Dr. Michael Wu, chief scientist at Lithium Technologies, discussed the growing demand for data scientists, what the role requires and how organizations can best acquire such talent.

More recently, FierceCIO spoke with Actian CTO Mike Hoskins about his experience with developing data scientists. Actian is one of the leading software development firms, and the company's strengths are in the areas of data management and data integrity.

"We are focused 100 percent on data and infrastructure," Hoskins says.

Hoskins agrees that big data is every bit the big deal that is being made of it. No hype here, he claims.

"We are in the age of data now," Hoskins says, "and data is what really matters. I don't think it can be overstated the significance that this has for business and for all of us."

More specifically, predictive analytics is what organizations are scrambling to master: turning mountains of data into identifiable patterns of behavior. By spotting trends and patterns, companies hope to gain insights into future customer behavior. This will enable a company to be out in front of a customer before they even realize what they would like to invest in.

A good analogy is the film "Minority Report," in which futuristic homicide detectives can predict murders before they happen, and arrive on the scene just in time to intervene. The same film depicts numerous scenes in which marketers know everything there is to know about a consumer's likes and habits when they enter a mall, and bombard that consumer with tailor-made, personalized marketing.

The ability to capture such data, and understand how customers make decisions, is fueling the science of decisioning, Hoskins says.

Like Michael Wu, Hoskins believes there is confusion by many in the industry and tech media on who data scientists really are. First is the notion that 'data scientist' is a single job role. It is actually a blanket term that encompasses a few different, but related, job roles.

Second is the idea that these much-desired professionals will come from the ranks of IT--many will not and need not, Hoskins notes.

Indeed, Wu has identified at least three primary data analysis roles that he says make up the complete data science triangle:

  • Business analyst: "Working at the decision layer, this person is responsible for the final analysis and business intelligence. They present the data to the decision maker."
  • Machine learning expert: "This person focuses on the statistics, builds probailistic data models, ensures that the model is accurate, unbiased, is easy to explain and to understand so that the analyst can interpret it effectively." Wu counts himself in these ranks.
  • Data engineer: "This person works at the infrastructure and platform layer. They ensure data quality, scale and relevance."

The greatest need at many organizations is for the machine learning expert, or machine data expert, Hoskins says. And like Wu, he says these experts are more likely to be found in the fields of science than in IT fields.

There is good news on several fronts for organizations looking for data scientists in 2014, Hoskins says.

First off, more colleges and universities are addressing the skills shortage, and creating new programs centered around big data.

Secondly, more products are coming on the market to help organizations better capture and analyze data.

Third, organizations are taking a more practical approach to analyzing smaller amounts of data--what Hoskins calls "clusters"--rather than trying to manage the entire data pool. He compares that approach to trying to see the forest through the trees.

Finally, Hoskins predicts that 2014 will bring a variety of new as-a-service options to organizations to help them with the data quests--clustering as a service; forensics as a service; data mining as a service. This will be the real solution to the data scientist shortage, Hoskins believes. After all, "no one is creating hundreds of thousands of data scientists."

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