3 Underutilized Strategies to Beat the Big Data Talent Gap

Data Center Maintenance


Parker November 03, 2015

Could your next IT innovator come from the field of literature, history, or even music? Maybe so—and the benefits reach beyond analytics.

There is a big crisis in big data analytics. Everyone wants it, and now.

For good reason. According to McKinsey, the U.S. healthcare industry alone could generate more than $300 billion in value every year through big data creativity. And a retailer maximizing analytics could increase its operating margin by more than 60%.

Such attractive numbers have CEOs lighting fires under IT departments worldwide. CIOs who deliver the analytics goods stand out. The problem is, few can.

The holdup isn’t computing infrastructure. Although that’s critical, IT is generally well positioned to handle the complexities, either internally or by outsourcing routine tasks like storage system maintenance.

Missing is the high-performance data analytics talent needed to transform bits and bytes into actionable insights. This makes big data fundamentally a people problem the technologists must nonetheless solve.

The IT Talent Factory Failed

With the field of big data arising from nowhere, the traditional producers of IT talent are trailing demand by a wide margin. Despite some initiatives to better predict emerging needs and ramp-up educational tracks more quickly, things haven’t happened fast enough.

Analytics-focused MBAs now exist, but few enterprises can wait years for their graduates. At the same time, formal career pathways in data analysis—the on-the-job learning that can also generate skilled workers— exist primarily at “pacesetter” companies, from which it is difficult to poach employees.

With stiff competition for any analytics graduate or professional, recruitment results are usually dire. By some estimates, 80% of data scientist positions cannot be filled. Unless your name is Google or Amazon, it can be tough to attract job applications, let alone new hires.

In fact, a Harvard Business Review survey of senior Fortune 500 and federal agency leaders revealed that 85% of organizations have a big data initiative underway or planned. Yet 83% of executives say finding the required data scientists is challenging, very difficult, or nearly impossible.

This leaves CIOs desperate for strategies to grab and hold on to the talent they need. The ones who do so stand to benefit not only in big data but across IT. Here’s how they’ll do it.

Option #1: Buy it
Target the Curious Georges with intellectual challenges and C-suite respect

Most IT leaders would like to open a box and have the necessary data analytics employees come climbing out. The labor shortage limits what can be expected from head-hunting, college outreach, and similar efforts. Refinements in recruitment approach can, however, maximize an enterprise’s ability to buy the talent it needs in a tough market.

Key to success is understanding what draws people to data analytics and what keeps them in their jobs. Research shows the primary reason big data pros leave their current employer isn’t a quotidian issue like salary—although that’s undeniably important—but rather boredom.

That’s right, intellectual curiosity is a driver of a quant’s job hunt. In response, enterprises should demonstrate to candidates how they’ll put interesting problems in their hands.

This takes strategic investment and focused effort. Data scientists are as enticed by the latest toys as average consumers are by the newest iPhone, so IT should make it clear they won’t be working on Excel spreadsheets.

Prospective analytics employees also need to know they’ll be challenged with tough questions. And perhaps most importantly, that they’ll have access to the C-suite. Nothing is more frustrating for the intellectually driven than deriving stunning insights only to have them ignored.

Clearly it takes more than posting online ads to get these points across, so high-performing enterprises leverage everything from in-person networking to social media to differentiate themselves from others begging data scientists to hear them out.

Once the bait is set, hiring processes must be examined to ensure they don’t belie claims of innovativeness. Bureaucratic decision-making and slow-coming offers are enough to lose a potential hire, no matter how stimulating the work prospect.

The good news—this attention to HR detail will help secure top candidates across IT skill sets, so it’s time and effort well invested.

The Takeaway:

Treat big data talent recruitment as the enterprise would a marketing initiative. Know the audience, communicate the position’s most compelling advantages through a variety of mediums, and remove barriers that could lose a “sale.”

Option #2: Build It & Borrow
It DIY the way to big data with internal talent identification, training, and partnerships

If you can’t beat ‘em, join ‘em—and if you can’t buy it, build it. Many enterprises are considering big data a DIY project for which they must create the talent they need and want.

Fortunately, such internal focus may be among the best ways to make data analytics pay off.

According to a KPMG study, 97% of industry leaders are investing in big data but only 19% are “very satisfied” with what it delivers. Most (86%) lay the fault at the feet of talent. But maybe hiring raw math ability isn’t the issue—understanding the business is.

Just ask Gartner research director Svetlana Sicular, whose blog post has been widely quoted:

Organizations already have people who know their own data better than mystical data scientists — this is a key. The internal people already gained experience and ability to model, research and analyze. Learning Hadoop is easier than learning the company’s business.

Or talk to Dell:

Organizations need to look internally first and invest in their existing analyst resources, train them to stand tall on the same pedestal we seem to have placed the scientists on. As with any business, understanding capabilities that exist on the inside could well be a more cost and time effective method than searching on the outside.

To that end, Sicular counsels companies to get over the idea that data science is “a magic that can turn big data into big gold by making sense of vast amounts and multiplicity of senseless bits and bytes…[and that] the data scientist is a savior who (if found) can solve all big data problems, so companies will not have to worry about figuring out how to do it themselves.”

Seen this way, the analytics talent gap is less about recruitment and more about finding insightful go-getters within the enterprise. It’s about having the courage to expand their roles and truly integrae them into the big data mission.

As part of the effort, IT will need to identify what skills each team member is missing and apply learning solutions. Not just formally, but with cross-functional interactions in the workplace as well.

For specialist capabilities, “pacesetting” CIOs will also partner with academia, vendors, start-ups, citizen developers, and others to “borrow” capabilities rather than create them from scratch. And once they get comfortable with the process for data analytics, they might find speedier app development and other benefits along the way.

The Takeaway

A single individual can’t serve as the big data Messiah. Divide the data science role into discreet functions and search for capabilities internally, among employees who already know the business. Then use training and collaborative work to remedy skills gaps, and consider partnerships to complement what the enterprise builds on its own.

Option #3: Re-engineer It
Abandon pre-conceptions about IT workers to expand IT in new directions.

When it comes to talent, CIOs are accustomed to battling it out for the mere 15% of U.S. college graduates with STEM (science, technology, engineering, and math) degrees. Not to mention angling for their share of limited H1B visas to backfill talent the U.S. alone cannot supply.

As we’ve covered, this is the approach most execs are taking with big data. Whether looking internally or externally, IT leaders focus on those with math and science backgrounds. But how limiting is this viewpoint?

As the Harvard Business Review argues, “Data analytic talent is not new. Statisticians, database marketers, and Ph.D. quantitative analysts have long been a staple of sophisticated marketing organizations and have played critical roles in financial engineering…[But] all too often these same individuals have been relegated to the sidelines and not integrated into mainstream business processes.”

And not partnered with their business-oriented colleagues.

In other words, IT tends to believe skill sets solve problems. But finding the desired skills is often insufficient.

Currently making waves are the companies looking beyond IT specialties and thinking differently about what makes an employee valuable. They’re finding success in everything from big data to social business by incorporating non-traditional talent.

Take Slack Technologies co-founder and CEO Stewart Butterfield. With his own master’s degrees in philosophy and the history of science, Butterfield makes other “strange bedfellows,” such as thwarted actress Anne Pickard, at home at a business-to-business software startup. And it’s working—to the tune of 1.1 million users and a $2.8 billion private market valuation.

Or you could look to Facebook’s Mark Zuckerberg, as The Washington Post did, and see he was psychology major with a passion for ancient Greek and computers—but certainly not a coding genius.

Ironically, non-technical abilities might be what set Silicon Valley and other U.S. sources of innovative IT apart. As the Washington Post article observes:

America overcomes its disadvantage—a less-technically-trained workforce—with other advantages such as creativity, critical thinking and an optimistic outlook. A country like Japan, by contrast, can’t do as much with its well-trained workers because it lacks many of the factors that produce continuous innovation.

If true on the scale of nations, why not within enterprise IT itself? If success is as much a matter of creativity as technicality, why not actively avoid overspecialization and get at the big picture?

And where better to try than in big data? Where even those arguing for machine analytics say “the value of big data isn’t the data. It’s the narrative.

More and more (and more and more) people are equating data analytics to storytelling. Proceeding logically, they’re looking to involve storytellers, individuals who communicate visually and verbally, who question and argue well, who are, frankly, trained differently than the “usual” IT worker.

To bring big data projects to fruition and to convince lines of business that results are actionable, IT needs people who are good with people. Stereotypes aside, the IT industry comprises more than socially awkward former hackers. Still, traditional IT workers are often not well suited to non-technical IT roles, for reasons of aptitude, preference, or both.

Sending coders, engineers, and other STEM-oriented employees through communications and management coursework can help. But will their capabilities be enough when the enterprise needs to partner with lines of business to create a comprehensive IT service catalogue, as Cisco recommends as part of Fast IT? Or when the right questions need to be brought to bear on the ever-increasing pile of data at analysts’ disposal, where Bain & Company finds only 4% of the industry excelling?

Nearly 30 years ago, a major aerospace company thought the technology gifted alone could not meet their needs. Departing from the status quo, they sought liberal arts graduates capable of learning the technical details to work as business analysts and project leaders—and well, they continue to be a major aerospace company today.

All indications are this solution is making a comeback. Deloitte CIO Larry Quinlan, among others, is promoting “STEAM” fields—the STEM specialties plus the arts.

There are reasons to think his inclination is right. Plug-in modules are making apps possible with fewer coders. At the same time, deciding on the right functionality, design, and differentiating coolness takes more people than ever.

In this environment, as The Second Machine Age authors argue and Forbes summarizes, tech will soon take care of the routine tasks so that people “can concentrate on generating creative ideas and actions in a data-rich world.” There isn’t going to be a pool of capable employees ready-made to solve such challenges.

In some ways, therefore, IT is shrinking. It is becoming a diminishing group of specialized skill sets, like coding, which are being overtaken by model-driven development and low-code possibilities. Along with routine tasks, such as systems maintenance, which are often best outsourced.

Yet IT is also becoming bigger. As it works more closely with the business to meet strategic goals and relies on critical thinking, creativity, storytelling, graphic communication, and more, it is becoming a new form of IT.

One that doesn’t look, act, or hire much like its predecessors at all.

The Takeaway

Tasking a headhunter to find history and music majors won’t be enough to shift a lackluster IT function and create the evidence-based, agile, customer-focused IT that enterprises are craving. But the right, integrated mix of arts, sciences, math, and business might just be transformative.

About the Author

Parker, Park Place Assistant