Why now is the time to leverage new technology and data analytics in L&D

The game-changer has a name and came into workplaces across the globe, leaving few lives untouched. In 2020, Covid-19 altered the way we live, the way we work and the way we learn. It forced upon us radical changes in record time and at a record scale, with little chance to reflect, plan and strategise.

Organisations needed to pivot their usual work practices to stay operable; many unfortunately had to downsize and learn how to achieve the same output with fewer resources. Activities that usually happen in person were forced to move online, including learning and development and performance management. 

Now, a few months into this new awareness of what is possible in the online space, it is time to take stock and examine how best to take forward any lessons learned. One thing seems inevitable: digital learning solutions are critically entrenched in businesses – at a minimum due to risk management requirements for future crises, in the best case because they are a proven, enriching element that drives workforce engagement and performance.

Image shows data analytics on a tablet

Arguably, the current situation lends itself well for experimenting with new ways to operate L&D; it is a forgiving time to embrace data analytics and digital learning and put in place what can work for your organisational context in a more agile, staged approach. 

In parallel to the Covid-19 return to the “new normal”, here is a roadmap for your L&D technology staged release.

Stage 1: Understand Learning Data

In this first step, L&D configures a learning record store (LRS) and learns how to read and track learning activity statistics such as completion rates, view and interaction statistics, average time spent per screen. Here, it is not enough to merely look at raw data because external factors have an influence which needs to be considered in the analysis. 

For example, take a mobile responsive course where a learner spends 5-10 minutes “time on screen” as it is a longer vertical scrolling piece. The equivalent learning artefact built for a tablet/PC screen could have a different interactive functionality such as “click next” or “swipe next” and as a result produces a totally different value for “time on screen” – which has nothing to do with the screen content viewed by the various learners.

Analysing data requires an awareness of influencing factors that can throw your metrics out. It is why taking time to experiment, learning how to read and interpret data and understand the direct influence the build of your training has in relation to the data, is time well invested.

Stage 2: Create a Data Analysis Governance Framework

The lessons learned in Stage 1 will enable your L&D team to identify valuable themes and patterns to consider before rolling out learning data analysis in your specific organisational context:

  • What are meaningful metrics that can support your L&D function for continuous improvement?
  • What method will be used to measure performance consistently?
  • What are some metrics that will be useful in the compliance and risk management context?
  • What are the privacy and data protection implications of collecting, recording, and using digital learning data? 
  • What policies and procedures need to be put in place to turn digital learning and data analytics into business as usual?
  • What data sets do you need to track for what purpose?
  • What information can support performance management and support, and to what extent?
  • What tools and functional guidelines will be used in relation to data governance and consistency?

Stage 3: Implementation

Depending on the granularity of your tracking, data analysis from L&D activities can potentially inform analysis of performance, productivity, efficiency, management, leadership, contributions – all the way to gauging the effectiveness of training programs.

It can motivate individuals to take more ownership of their learning, ideally because the data analysis leads to improved learning content, or, to quote Peter Drucker, just because ‘What gets measured gets done.’

Gone are the days where learners can quickly flick through a learning piece without spending the appropriate time and attention to learn what they need to know and do for their job. Moreover, data analytics is also valuable in regard to supporting AI and machine learning programs.

Learning and development can save lives. That is another hard lesson we have collectively learned during these pandemic times. Let’s keep it digital and accessible – and let’s keep learning to make it better, based on data, not opinions or inconsistently measured statistics.

This article originally appeared in Training & Development Magazine, December 2020 Vol. 47 No. 4, published by the Australian Institute of Training and Development.