Big Data: Catch the 40-Foot Wave

Big Wave

Half a century before Facebook and a quarter century before the Internet, a social anthropologist named John Barnes was the first to identify the concept of social networks. He spent time with the residents of a remote Norwegian fishing village, tracing their family trees and interrelationships. (“Net” was an apt usage—after all, they were fishermen.)

Today we don’t need to go knocking on rugged, windswept doors and inquiring who someone’s cousin, uncle, minister, friend, shipmate, or midwife is—we can use computers to get information on people’s social connections. Who sends messages to whom? When and how does that person respond? A rich picture of relationships, personalities, and capabilities emerges. This is Big Data, and it can yield important information for learning leaders, if—and it’s a big if—we know how to access and interpret it.

CLOs Face Some Rough Surf

Big Data stubbornly resists interpretation by traditional means. It’s basically a whole lot of unstructured information, most of it flooding by faster than we can collect. Not just from social media (Facebook, Twitter, Instagram, or whatever ‘the kids’ are using these days,) but from the “Internet of things”: grocery store checkout scanners, smartphones, personalized music stations, and other basic infrastructural technologies.

waveAnd Big Data can seem like a 40-foot wave to CLOs. They’re hearing that learning leaders “need to know that the big learning data revolution is happening right now. Nearly one-third of global organizations with more than 1,000 employees already are leveraging learning-related big data…” (TD Magazine.) And a piece in Chief Learning Officer commented, “CLOs will be spending lots of time — the rest of their careers, most likely — mastering analytics.” (Hmm, doesn’t that sound appealing.

How to Find Out Amazing Stuff About Your Employees

But the real opportunities for predictive analytics are eye-opening. Some cutting-edge learning organizations are connecting data on employees’ web activities to their job performance records. Data “mashups” like this are helping them predict things such as: Which individuals will be the most valuable contributors in the company? Who’s likely to file a new patent soon? Who’s the kind of person who will attend professional meetings, connect with colleagues, and synthesize ideas around product innovation that could drive the business forward? These companies are intent on capturing this sort of information before they lose people to a competitor.

A growing number of social web companies, including LinkedIn, offer programming interface (API) connections that yield incredibly detailed information on a user’s public web-based activities. You can see whether employees are contacting recruiters, applying to jobs, and connecting with other job-related resources—at a marginal cost that is fast approaching zero.

Measuring the Elusive Impact of Learning Programs

As the vice president of learning analytics at CorpU, it’s my job to wrangle Big Data to help our clients measure and optimize learning outcomes. I perform social network analyses on the very rich, detailed data on learner interactions generated by our cloud-based VLCs (virtual learning community) platform. Are learners sending each other direct messages, commenting on forum posts? Are they discussing opportunities to blend ideas, reaching out to individuals in the organization they haven’t connected with before? We’re now in a position to mash up learning data with key performance indicators, tracking improvements in business-critical areas such as product development, strategic positioning, and supply chain efficiency.

Big Data may be messy—and it defies traditional systems—but if you figure out how to harness it effectively, it can tell you some really interesting things. Is your company catching the wave?

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Matt Holtman, PhD is CorpU’s Vice President of Learning Analytics. At CorpU, Holtman builds systems to measure and optimize learner engagement, quality of collaboration, and business impact. With over 20 years of experience in research, innovation management, and applied data analytics, he is an expert on the use of predictive analytics to increase business value. Holtman has published research on assessment in medical education; professionalism and patient safety in the health professions; and program evaluation with a focus on international development. He has taught a variety of courses on statistics and business intelligence, and holds a PhD in sociology from the University of Pennsylvania.