Successful data experts are coming from a variety of business and computational sciences fields.
It’s no surprise that careers in big data are booming. What is surprising is the new demand for data scientists. These experts are coming from a variety of business and computational sciences fields and, as it turns out, big data is a segment that requires all comers.
Movies like “Moneyball,” companies like Netflix, and statisticians like Nate Silver have all brought big data into focus. Data experts can gain insights to help win baseball games on a budget, predict what we’d like to watch next, and call the state outcomes for presidential elections.
What’s interesting, though, is that the “experts” doing this work aren’t always who you’d think they’d be – top gun statisticians or data scientists with heavy-duty computational skills. According to Virginia Tech business professor Barbara Hoopes, “Equally important are people who can use cost-benefit analysis tools and have deep business understanding about what can be done with this asset, which is the data that companies have access to.” These are the individuals who can hear “the voice of the data,” she adds, and know what kinds of questions to ask of it.
Hoopes and computer science professor Naren Ramakrishnan teach in Virginia Tech’s Online Master of Information Technology Program. The program brings together disciplines from the university’s engineering and business colleges and supplements foundational courses with eight concentrations, such as Big Data and Analytics, and Business Intelligence.
Courses about data topics are bursting at the seams, says Ramakrishnan and that level of interest reflects the nature of the employment outlook for big data jobs. An article in Forbes magazine shared a finding that in the previous 12 months, just three companies – IBM, Cisco and Oracle – had advertised more than 26,000 open positions requiring understanding of big data. The median salary quoted in that article for professionals across the board with big data expertise was $124,000 a year. (The low end of that bell curve was $83,000 and the high end was $165,000.)
A major driver for this growth, Ramakrishnan emphasizes, is just how data-driven we’ve all become in our own lives. “We’re as much data producers as data consumers,” he says. And while we all understand the idea that the digital crumbs generated by the activities of our lives are being picked up and stored somewhere, the truth is that the large share of that data is being left unanalyzed. “Most of the time people just archive it, and it is still sitting in a data warehouse as a curiosity.”
What’s different now, says Hoopes, is that on top of the collection of data, which has “always been there,” we now also have systems that let us explore it. “You have tools that are being developed that make this so much more accessible to your average manager, that are a little more point-and-click. So you don’t necessarily have to be a computer scientist to take advantage of digging down and understanding what’s going on in some of these large pools of data.”
While a computer scientist may be well positioned to “write an algorithm” that mines the data, “without the business understanding, they wouldn’t know the right questions to ask,” Hoopes says. That’s where someone with subject-matter expertise as well as training in business intelligence and data analytics excels, she says. “They have the business knowledge, but they also have an understanding of what’s possible through data mining.” With that “powerful combination” they’re well positioned to make the complex connections that define the use of big data.
Discovery Analytics Center, directed by Ramakrishnan, is a Virginia Tech-wide effort that brings together researchers and students to tackle applied problems in important areas of national interest. One recent project for a government agency examined the use of “surrogate” data sources to forecast societal events. Ramakrishnan’s team analyzed data from OpenTable, the restaurant reservation system, and found that a spike in reservation cancellations can correspond to a disease outbreak. “It could either be an early onset of the flu season,” explains Ramakrishna, “or it could be an episode of food poisoning. We do not know the specific reasons but such observations can serve as an early indicator.”
Curiosity, technical aptitude, and business understanding, are the hallmarks of those excelling in this field. “You have to have domain knowledge,” Hoopes says. “Then there’s this extra curiosity factor and tenacity and a willingness to dig around a little bit and visit a few dead ends.” Those are personality traits, she says, “that are really valuable in this field.”
And they must be able to communicate the story of the data, says Hoopes. “If I told you something about a clustering algorithm, I might lose you unless I was also able to tell you, ‘OK, here’s an example,’ ” she explains. “That’s something that advanced training really helps with – not only on how to do the analysis on the data but on how to communicate the analysis so it makes the most difference and has the most value to a business.”
The formula is working. “Our students are recruited into data-science companies such as Google, Facebook and LinkedIn,” says Ramakrishnan. But alongside those are the non-IT segments, such as automakers and oil and gas. “Many of them are launching data-science teams, so they’re all looking to staff these new positions that are coming up. The fact that our students keep getting requests for interviews is a good sign.”
Virginia Tech’s Online Master of Information Technology program is offered between the College of Engineering and the Pamplin College of Business. Ranked by U.S. News and World Report as the No. 2 “Best Online Graduate Information Technology Programs” the past four years.