Big Learning Data?

Elliott Masie, industry analyst, founder of The Masie Center and co-author of the book “Big Learning Data,” argues it is.

“All day long a tremendous amount of data is being created in what we call data exhaust,” he said, pointing to the digital trail left when a worker attends a class, reads an e-book or takes an e-learning course.

While most organizations only store four pieces of data about e-learning — for example, including who learners are, when they took it, how long they took it for and what their final score is — the depth of available information is much greater. Look at it from an enterprise-wide perspective and it grows even larger.

“The reality is there’s probably somewhere between 10,000 and 120,000 pieces of data in the data exhaust,” he said. “How long did that person spend on each question? How long did they mouse over the wrong answer before going to the right one?”

Multiply that by the large numbers of people taking courses and the increasing speed at which that information is generated, and learning data quickly approaches the realm of truly big data. Combine it with other sources of employee data and it becomes even bigger.

“If you start looking at the social graph and looking at the actions that people are doing in the system, potentially we’re collecting hundreds of data points per individual per day as opposed to a salary action once a year,” said Nick Howe, vice president of learning and collaboration at information technology company Hitachi Data Systems. “We’re talking several orders of magnitude more data.”

Rise of the (Learning) Machines
In learning and development, big data is showing up in new ways, such as machine learning and sophisticated, Amazon-like recommendation engines. It’s also reshaping established practices like instructional design and mentoring.

At Hitachi Data Systems, the company’s 7,000 employees use software by Jive to communicate and collaborate on projects. But it’s also a learning and development data engine that collects employee profile information, analyzes actions they take — documents reviewed or discussions participated in — and recommends additional information or introduces a co-worker who might be helpful. And it learns from the data it collects to make better and more targeted recommendations, thus the term “machine learning.”

If “our chief finance officer goes into our collaboration platform and searches for something and I go into the collaboration platform using the exact same search term, we will get back two different sets of results because the system is taking into account the ecosystem in which we participate,” Howe said.

Adding in performance data makes employee learning even more individualized. At Farmers Insurance, the claims division created the Professional Development Center, a learning and development system with a big data engine that crunches data from 360 assessments, employee engagement surveys and individual performance ratings to find individual leaders’ strengths and weaknesses.

It’s simply too costly and time consuming to build curriculum around every one of the company’s 57 identified leadership skills, said Jeff Losey, head of professional development for the claims division. Nor would it be useful for leaders. “It doesn’t make sense for us to take all of that information and shotgun it out there and put it on a website and say, ‘Here it is available to you in case you need it,’” Losey said.

Instead, through the center, leaders get personalized development in areas that will have the greatest effect, such as constructively delivering feedback and developing others. Farmers also used the data to start a mentoring program and included a “find a mentor” button feature in the system that matches leaders with others who can help. “The reason why big data is important is because it helps us determine exactly what we need when they need it,” Losey said.

Building the Professional Development Center, which began in May 2012 and rolled out in November that same year, also had an additional benefit: uncovering overlooked high-potential people. The center provides a more objective data-centric picture of potential leaders than traditional, subjective succession planning by generating detailed individual talent portraits searchable by skill. “There were many top performers that the executives didn’t know — had never heard of, had never seen,” Losey said.

That discovery led Losey’s learning group to develop a three-day simulation program that gave 60 high-potential employees broader business experience and put them in front of Farmers’ executive team. Twenty of them have moved into formal succession planning. “It takes executive exposure to get many of them into management positions and move up in the organization,” he said.

Analytical Thinking for L&D