The digital transformation of learning and development offers HR leaders new opportunities to embed learning within their talent strategies and make the business case for L&D investments crystal clear. Part of the promise of digital learning comes with the application of data and analytics, enabling organizations to measure and communicate the impact of these programs more precisely than ever before. Unfortunately, as with all new technologies, the rapid emergence of new options can be overwhelming, not every solution is right for every business, and adopting a technology without a clear understanding of how it will generate value can be a very expensive mistake.
To survey this new landscape of learning analytics, Justin Taylor, Director, Talent Solutions at Gartner, moderated a panel discussion at our ReimagineHR conference in Orlando on Monday, bringing together Patti Phillips, Ph.D, President and CEO of the ROI Institute; Dave Vance, Ph.D, Executive Director of the Center for Talent Reporting; and Kimo Kippen, a former Chief Learning Officer at Hilton. The conversation covered the range of new technologies emerging in this space, the opportunities they provide, and the challenge of figuring out how to take advantage of those opportunities.
When considering an investment in learning analytics, the L&D function should keep a few strategic considerations in mind. Based on Monday’s discussion, here are a few of the key questions leaders should ask themselves:
What is your objective?
There are a number of technologies currently on the market that apply analytics to L&D in different ways and to different ends. There’s adaptive testing, in which training modules and skill assessments automatically adapt to each individual’s level of ability. Learning record stores and xAPI record and track learning experience data, allowing organizations to track the progress of learning employee more closely and draw more insights from that data. Learning experience platforms offer new ways of delivering learning to employees on an individualized, self-directed basis. Natural language processing, machine learning, and augmented and virtual reality are also finding applications in learning.
With all these options out there, the panelists agreed, it’s important for an organization to identify just what they hope to get out of learning analytics before buying a new piece of enterprise technology. Don’t chase a shiny toy, Kippen advised, but ask what the business objective is and whether the investment is worth it. You might find that the extra dollar is better spent on fundamentals, Vance added, as new technology won’t fix more fundamental problems in your L&D program. “Without algebra,” he analogized, “you’re not ready for the calculus.”