When we think of training programs—regardless of industry, level, or type—we often think of a classroom setting where a group of professionals receive packaged information that will, ideally, teach them how to do their jobs better. Typically, these types of training programs are measured by how well participants do on a test or how well they apply what they’ve learned in their everyday jobs.
While these can yield some good data, there’s a trend in the learning and development industry to follow what other corporate functions have done with data and analytics. The main reason behind this:  Training departments need to continually prove not only their ability to train, but to have an actual impact on a company’s overall performance.
For example, knowing that X percent of trainees passed a test doesn’t tell us anything about how well they use their training in their jobs, or if that training has had any effect on the company as a whole. However, a data analysis of a training program might find that employees that have gone through a particular training outperform their untrained counterparts by X percent. An even more sophisticated analysis might uncover that trainees are more efficient, completing their tasks faster than those who haven’t been trained. These are the types of insights that simpler measurement techniques just won’t reveal.
In fact, training departments are just starting to integrate analytics in similar ways that other corporate functions have already done. For example, it’s common in marketing departments today to use data analytics to refine marketing campaigns and to calculate their return on investment (ROI). In training, data analytics can help training departments not only understand if they’ve been effective, but to also find weak spots in the training materials and/or delivery and refine to the entire training experience.
What’s more, using data analytics, training departments can develop more personalized approaches to learning. It’s no secret that different people have different learning styles. Traditionally, training models generally provide the same type or style of training to everyone who needs it, regardless of their own preferences or ability. Using data analytics, though, training departments can identify individuals’ learning styles and deliver a training that’s more effective. What’s more, that effectiveness can be measured (again using analytics) and connected to the various other business outcomes.
Perhaps even more importantly, data analytics can be used to identify areas where training is needed. Skills assessment tests are useful here, but even more helpful is real-time tracking and data analytics. This might uncover some hidden obstacles, such issues workers may be having with a piece of software to accomplish a task. By uncovering better insights into operations, data analytics can help training departments know which skills or tasks need the most training and develop programs to improve those areas.
To that same end, data analytics can be useful for training departments to refine their own approaches. It can help identify materials that aren’t working as well as intended, such as items that trainees find confusing or take too long to complete. Continual improvement approaches, such as those found in agile software development, might seem difficult to implement in a training context where all participants leave the training with the same information and enhanced skills, but in fact it can be used to deliver training programs that work better.