The buzz is all about 'Big Data' and how best to use it to generate actionable intelligence. To do this, companies will need to hire loads of highly trained, highly paid data scientists -- or will they?
The tech media buzz surrounding big data suggests that organizations should invest huge sums into hiring and retaining highly skilled (and highly paid) data scientists. But you might not want to submit that graduate school application just yet.
In reality, most firms will address their big data challenges by leveraging data analytics technology and training employees they already have to turn big data into smart data.
Small Businesses, Big Data
For many smaller and mid-sized firms, the hype surrounding big data doesn't resonate, and probably won't translate into hiring, because the challenges those businesses face aren't truly related to Big Data, says Tim Herbert, director of research at CompTIA.
"A lot of companies don't actually have big data problems -- they have smaller challenges," Herbert says. Many of these data challenges crop up between IT and other departments such as marketing, finance, and business operations, as SMBs uncover their true business objectives and figure out how to make raw data into actionable intelligence, he says.
"While they need to find ways to take their business data and translate that into business intelligence, most of these SMBs are looking at data analytics and technologies like Hadoop and realizing they don't need anything as huge, powerful and scalable," Herbert says.
CompTIA's second annual "Big Data Insights and Opportunities"study, released last month, shows that many of these SMBs will instead invest in training and education for their existing employees rather than hire a formally trained data scientist.
"Hiring these costly, highly trained, highly educated data scientists just isn't practical for most businesses, so they'll put their money into what resources they already have," Herbert says.
In most cases, Herbert says, CompTIA's study found that most SMBs will rely on existing business analysts and financial analysts, though these folks will need additional technical training, he says.
In many businesses, this is already happening. According to the study, sales and research departments saw their participation in big data-related initiatives climb from 17 percent and 13 percent, respectively, to 27 percent and 25 percent.
Based on these numbers, it seems big data already is moving from its niche in the IT department and into many other business units and departments, Herbert says, but that's just the tip of the proverbial iceberg.
"Most companies may not realize that they have to first invest in making sure their data's in a format that can best be analyzed to derive value," Herbert says. "Finding the technology to do that is the easy part -- it's finding and nurturing the analytical talent and expertise that's hard, and that will take some more time," he says.
Large Enterprises, Larger Data Challenges
Initially, says Roger Gaskell, CTO of Kognitio, the demand for data scientists will be higher at larger enterprises as these organizations try and squeeze every last drop of value out of their data and their sophisticated business intelligence solutions.
Most large enterprises, looking for a competitive edge, will jump at the chance to hire folks who can use the data they've collected to try predicting the future of the markets, sales cycles and trends, and customer behavior rather than just react to what's happened, Gaskell says.
"If you want some predictive analytics, you're going to need to hire a few of these data scientists, but whole armies? That'll be too expensive," Gaskell says. Instead, enterprises will find ways to train business and financial analysts, as well as the 'average business user' to perform data analytics using automated tools.
"The feedback we're getting from customers is that they want just a handful -- maybe three, or five, or seven -- skilled and highly educated data scientists," says Michael Hiskey, Kognitio's vice president of business development and marketing.
Then, enterprises will surround those with business analysts, then surround those folks with a group of interns," Hiskey says. Kognitio itself has seen great success with such a hierarchy that it calls the Kognitio Analytics Center of Excellence (KACE), he says.
Big Data Reality vs. the Hype
For now, it seems, the rush to mint highly trained, highly educated data scientists can be chalked up hype and buzz, especially since the IT industry is still in beginning stages of big data usage and relevance, according to Ankur Gupta, head of global sales and marketing at MetaScale, a Hadoop solutions provider.
"Sure, enterprises are looking for folks with a statistical and predictive modeling background that can dig deep into data, understand trends and how to spot them, but they also have to have the patience to wait until trends and activity become clear," Gupta says.
"Right now, we see a trend toward companies wanting and needing that human touch, the expertise to parse data and use it to make decisions. But we're in the early stage of big data development, and as technology catches up, we believe that companies will offload these functions to machines, software, to technology. You don't need to hire ten data scientists to do the job," Gupta says.
Sharon Florentine covers IT careers and data center topics for CIO.com. Follow Sharon on Twitter @MyShar0na. Email her at email@example.com. Follow everything from CIO.com on Twitter @CIOonline, Facebook, Google + and LinkedIn.
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This story, "How Many Data Scientists Does the World Really Need?" was originally published by CIO.