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We hear a lot about the shortage of data scientists, those hard-to-find professionals trained to analyze massive data sets, gain insights from them and communicate that information to an organization's management team. These data gurus must be well-versed in multiple technical and business disciplines, including analytics, computer science, math, modeling and statistics. Oh, and people skills are good to have, too.
Not surprisingly, it's often a challenge to find individuals with such a wide range of abilities, a reality that might soon fork the position of data scientist -- at least as defined today -- into three distinct job categories.
As Teradata Labs' president Scott Gnau sees it, a successful big-data strategy requires a team of individuals, each with his or her own unique skill set: technologists to write algorithms and code, statisticians and quantification analysts -- quants -- to crunch numbers, and creative folks who'll find insights in data that their more technically inclined colleagues might miss.
"They're almost like different personality types. When you get these people working together, you get the value," said Gnau in a phone interview with InformationWeek.
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"Breaking it down, the technologists are the people who build the systems, get data loaded and transferred, all that infrastructure stuff, which is critical," said Gnau, whose job it is to provide "visionary direction" for Teradata's research and development, innovation and sales support efforts.
The creative group, which Gnau calls "artist explorers," will mine massive volumes of data for actionable insights. Not surprisingly, this artist explorer probably won't manage computer systems, but rather work on the operations side of the house.
"It's going to be someone who's completely operationally focused," said Gnau. "It's going to be someone who's a little more creative and strategic in the way they think. Typically I see many of them showing up in the line of business and not in the IT department."
Universities and technology companies must work together to address the "data skills gap" by giving students the tools and training to compete in a data-driven economy, Gnau added.
One such educational effort that's already underway is the Teradata University Network, the database company's online program that offers certification training for students seeking careers in data-oriented professions.
As big data tools grow increasingly sophisticated and automated, demand for highly trained technologists will remain high, Gnau believes. "That's a perennial thing that happens in our industry," he noted. "If I think back to early in my career in data warehousing … a lot of people were saying, 'Hey, we're not going to need really skilled SQL people anymore because the BI tools are going to be so good that nobody will have to write SQL.'"
He added: "Obviously, that's absolutely not true. BI tools became fantastically automated and (provided) great ways to do visualization, but not at the expense of talent." But will this projected division of labor -- technologists, quants and artist explorers -- reduce the need for data scientists as we define them today? "The answer is absolutely not, for the same reason that people who write structured query language are as much in demand, or even more in demand today than they ever were, despite the maturity of the tools in the marketplace," said Gnau.
The six-figure allure of a career in data science will continue to attract talented people as well.
"People starting out on their career (are) looking for something where they're going to be gainfully employed and be able to make some money," said Gnau. "And certainly the market is showing that this is the case for data scientists."
Besides, the job is fun.
"If you can get into this business, and I've been in this line of business for a long time, every day you come to work and it's a different job," noted Gnau. "You can't get bored when you're doing data exploration and business intelligence because every day there's a new competitor and a new marketplace, as well as new data, algorithms and relationships."
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