Our past articles have offered insights on how to transition from a traditional training model to a true value-adding one. We focused on how learning might function both internally and externally as it creates value for the business. This article will frame how you might use analytics to measure the value learning creates to justify further investment, gain senior leaders’ attention and sharpen the strategic focus of your modern model.
The Value of Analytics
It is almost impossible to pick up a business journal or listen to a business podcast today and not hear references to big data, analytics and algorithms. It’s not just potential any longer; it’s driving real value. The ability to predict what consumers want or need before they are able to articulate that need is having significant impact on sales. A sensory chip in a piece of equipment may reveal that a repair is necessary so it can be addressed before the equipment fails. An algorithm applied to positive or negative variances in performance data may yield new ideas on performance improvement, which are not so obvious to the naked eye.
Can learning begin to collect and analyze its own big data and lower costs, amaze learners and add new value? Can analytics become fodder for very different conversations with business leaders? Here are three ways to think about that possibility.
Think of learners as consumers who want to know their options and control their choices. How can learning leaders understand what they want, when they want it and where they want it, with enough advance warning that they have it ready and accessible? How can these leaders make data and insights available to the learners so they can do their own needs analysis and make informed choices? How can they make the same data available to suppliers and partners so they become part of the integrated process of creating the right learner experience?
Systematic and rigorous analysis of data streams will allow organizations to develop numerous learner personas or profiles. Just as marketing firms are becoming more predictive with consumers, you can become more predictive and prescriptive with learners. They value having informed options served up instead of searching for them, and they increasingly expect microlearning to serve learning at their point of need. If you can anticipate that point, you will accelerate learning and performance.
Big Data and Real-Time Continuous Improvement
It’s an app world: Launch with the best you have, and then fix bugs and make improvements. What’s the correlate for learning? Should we delay the launch of learning products till we get it just right? Do we think we know what is just right without hearing from users? Traditional learning feedback is collected and analyzed months after the first group goes though the learning, which is too late.
Do we pay attention to progress in e-learning? What if participants spend three times as long on page four as they do on page two – is that OK? Do we watch to see if people get through 50 percent of the offering and then sign off? Do we care that only 85 percent of the people who exit at that point return? What are the analogs in instructor-led training?
We once ran a leadership program to facilitate the most significant transformation in the organization’s history. Halfway through one offering, a series of questions indicated that the learners were skeptical of the organization’s commitment to funding the change. This resistance could have been a showstopper for the transformation. Had we continued, we would have sent the message that the program was more important than employees’ ownership of the change. We asked them directly what it would take to minimize their skepticism. They said they wanted to hear directly from the CEO, so we suspended the program for three hours and had the CEO call the program participants. How often do we measure (formally or informally) and adjust to what really matters to learners’ engagement?
HR and L&D have typically been laggards on leveraging big data and meaningful analytics, well behind early-adopting IT/Marketing and slightly behind business units, the majority adopters. L&D’s inability to leverage ongoing data streams impedes our conversations with business leaders and the advancement of the learning function. Some discomfort with the unknown, based on limited opportunities to use technology, may contribute to this problem. When exacerbated by the slow-moving nature of traditional learning technologies, this reluctance can create stasis. Given the ongoing war for talent – and the intensifying pressure to attract, retain and engage – this situation is changing.
Successful learning organizations align with, and support, the achievement of strategic imperatives. These metrics can be tracked manually or automated with tools. It takes time and energy to develop models that link business impacts to learning drivers, and it is time well spent. A systematic mapping process conducted with the leadership team ensures a focus on driving impacts that matter.
Ideally, the learner focus and the CEO aspirations are melded, because there should be significant overlaps. It helps the organization and the employees if everyone knows where the organization is headed and what skills are needed today and tomorrow. For example, if productivity and profitability are key focus areas for a company, employees may choose to develop value-chain analysis and negotiation skills. Most employees want ongoing development, and they would like to understand what skills will be valued by their organization.
Focus your analytics strategy on the long term as well as the short term. Most companies focus predominantly, if not exclusively, on the achievement of performance objectives, which are important. But unless you enhance capabilities and culture, performance improvement may not be sustainable. After asking your executives what success looks like this year, you might ask them what kind of organization they would like to lead in the future. If the past is any indication, it will be here before you know it!
Predict, continuously improve and evaluate: just three ways that data and analytics can improve learners’ experience and their ability and commitment to change their behavior. These are the necessary conditions for impact and value. The key is to get started; use the metrics you have or can obtain easily, and then improve as you go. Knowing is the key.