Big Data Talent Quandary: In-House Or Outsource?

Big Data Talent War: 7 Ways To Win

To compete effectively in an increasingly data-driven world, businesses need to leverage all the data that's essential to their operations, and to invest in people and platforms to pull strategic insights from a rising stockpile of information.

If a company lacks the resources, or the interest, to build an in-house big data analytics team, it could always outsource the job to a third-party service provider. Hewlett Packard's business process outsourcing (BPO) division, for instance, provides business analytics services designed to deliver data-driven insights to enterprises.

In a phone interview with InformationWeek, Arindam Dutta, general manager and director of HP's Asia Pacific and Japan BPO unit, said that finding big data talent -- specifically people with the right mix of technical know-how and business knowledge -- is a complex issue facing businesses today. "It's definitely the biggest challenge right now," said Dutta. "It's about finding the right talents, and finding them in abundance."

[ For more on the big data outsourcing dilemma, see Should You Outsource Your Data Scientist? ]

A unique skill set is required to process vast and varied volumes of data, one that goes beyond a graduate degree in advanced analytics. "It's about deploying the right people, which is not necessarily about having just a statistical and mathematical background, but also about having industry knowledge," said Dutta. "It's about making sense of the data and having some meaningful insights that the company can use."

A recent HP white paper with research from Gartner estimates that only 13% of companies currently use predictive analytics extensively, and less than 3% use prescriptive capabilities such as decision/mathematical modeling, simulation, and optimization. And while those percentages are rising, they suggest that most organizations have yet to embrace advanced analytical tools.

A shortage of trained technicians is one reason for the low figures, Dutta believes, but so is the fact that many companies lack a carefully planned big data strategy. "It's about putting together an entire roadmap -- not just doing isolated things here or there, but putting the entire ecosystems in place."

Companies' reluctance to implement big data platforms provides an opportunity for BPO service providers like HP, which can pitch its analytics services not as a cheaper alternative to an in-house team of data scientists, but as a new solution to solve a company's big data problems. "From an outsourcing or BPO point of view, it's not about a service provider going to the client and saying, 'You've got a standard process of analytics. You've got 10 guys doing it. I can do that a lot cheaper,'" Dutta explained. "We are talking about a situation where we are going to a client, and painting a picture of where they are right now." The service provider then shows how it can deploy tools and talent to (hopefully) help the client achieve its data-related goals.

One big data trend to watch is the consumerization of advanced analytics, such as improved user interfaces with gesture and/or voice recognition. Friendlier UIs will allow wider adoption and impact of data analytics, according to the HP/Gartner paper. For instance, voice-enabled controls similar to Apple's Siri will migrate from consumer to enterprise hardware, the report predicts, bringing voice interaction to advanced analytic applications.

In addition, increasingly powerful mobile devices -- including phones, tablets, sensors, and other connected hardware -- will enable robust analytic processing at the "point of decision or action," such as at repair or maintenance facility. "Consumerization is the key to making advanced analytics more accessible to a broader set of users; however, new processes for building advanced analytics must also be implemented," the study says.

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