How Do You Know When You’ve Closed the Gap Between Strategic Goals and Workforce Capabilities?

One of the recurring questions I hear from clients on a regular basis is:

“How can we measure the success of our learning and performance improvement solutions?”

For years, training and learning professionals have wrestled with evaluations of training effectiveness, return on investment, and impact on the business. Frameworks like Kirkpatrick’s four-level model and Phillips’s five-level evaluation provide an established structure for gathering metrics to evaluate training success.

But, with the increasing digitization of training and growth in the use of automated systems for learning management, performance management, training delivery, and workforce management, new opportunities are emerging for harnessing the power of analytics to evaluate and improve learning.

In the theme of my recent blogs on helping organizations take the first steps to introduce analytics to the HR function, I wanted to look at a specific application for analytics, learning analytics (LA), to provide HR/HC leaders with insight on one way to bring data driven decision making to HR. Learning analytics is the use of data, statistical analysis, and exploratory and predictive models to achieve greater success in training and learning.

If training is all about closing the gap between your strategic goals and your workforce’s capabilities to reach those goals, then learning analytics is the tool that helps you know when you’ve closed the gap.

The beauty of learning analytics is in its simplicity. The best LA programs make use of existing learning systems and data to gauge training performance. For most organizations data from your Human Resources Information System (HRIS), Learning Management System (LMS), Content Management System (CMS), and performance management tools can be combined to provide insight into training program effectiveness.

Here are just a few examples of the types of effectiveness questions you can answer, and actions you can take, by combining data from various sources within the organization:

Question	Decision
Is there a relationship between training performance (LMS) and position type (HRIS) or time-in-grade (HRIS)?	Revise the training program to reduce inequalities across participant groups Document course pre-requisites to encourage enrollment by those most likely to succeed
Can I predict performance in a course (CMS) based on the number of embedded links the participant accesses (CMS)?	Integrate content from the most frequently accessed links into the course
Do participants who complete a training program (LMS) receive improved performance ratings (HRIS)?	Promote the benefits of the training program Customize individual development plans
At the organization level, is there a relationship between training participation (LMS) and business metrics (performance management tools)?	Prioritize training investments on those programs with the best impact on business performance
Is there a relationship between training completion or performance (LMS), engagement with the content (CMS), and a change in employee status (promotion or termination) (LMS)?	Predict and proactively address potential attrition based on training performance Integrate training participation and engagement metrics with career planning

 

As you can see from these few examples, by merging data from a variety of sources, learning analytics can provide valuable insights into your training and learning programs. The measures you can evaluate are limited mainly by your ability to ask the right questions based on your objectives and the available data.

The intersection of training and big data can provide a wealth of actionable information to inform decision making and improve your learning program. If you’ve already started using analytics to support training and learning, share your experience with others in the comments below.