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Learning Data Analytics

Learning Data Analytics: What’s changed?

At times, the forces that previously held L&D back from exploiting the use of learning data analytics have seemed to have the solidity and durability of permafrost. But now it’s hard not to see all around us the signs of a thaw.

We’re seeing crucial underlying drivers force changes to the situation for L&D. These importantly, offer a route out of the current condition of being ‘stuck’.

These drivers include:

  • A broader view of data
  • New data sources
  • New ways of using data

Second to these underlying factors, we now have the shorter-term (hopefully) and highly compelling impact of the COVID-19 crisis. 

Dr. George Siemens, founding president of the Society for Learning Analytics Research (SoLAR), posits the crisis has created a unique moment: “The experience we are in right now is the necessary condition for corporate learning analytics over the next several years to take off enormously.”

A broader view of learning data analytics

Part of this stuckness for learning analytics lies in the fact that it tends to focus narrowly on evaluation after the fact. Meanwhile, the rest of the world has moved to a view of data in real-time, seeing the uses beyond describing what has happened in the past. In his most recent book, Donald Clark put forward a data schema which is a useful corrective to this evaluation-centric view. 

Clark writes: “It is almost pointless to gather data if it remains unsolved and unused. There must be a purpose to the endeavour.” In response to that, the schema is goal-based and can be categorized into the following: describe, analyze, predict and prescribe. 

But even before the pandemic struck, L&D was beginning to operate in an increasingly data-rich environment. Global data volumes have shown “extraordinary” growth since 2010. Companies like Capital One and Amazon have been using data to optimize customer service since the mid-1990s. Since then, a number of organizations have employed so-called “data-plumbing” tools. These aim to improve consumer decision-making, improve analytics/recommendations/personalization, discover business insights, and manage data growth. 

New data analytics sources

The amount of data potentially available to L&D has also increased with the use of new tools/techniques. For example, responses to employee surveys and requests to a chatbot. Social media channels also produce a great deal of this unstructured data. And with the right tools, can all now be easily analyzed.

If we look at the ways in which some of the more adventurous people in L&D are actually beginning to use data, there is a much more broadly focused picture.

We would never want to suggest that people stop doing learning evaluation and impact evaluation, far from it. Instead, we emphasize that learning professionals need to look at the whole topic of learning and data through a wider lens. And in doing so, work to put data at the center of their practice. 

New ways of using data analytics

Data is so omnipresent in our environment that we are constantly using it without even realizing that we are. Telling people they should get more engaged with data is like saying they should get more involved with the written word. Both are unavoidable in the modern world. The point is more about putting data to work. Getting data to perform an active role in design, decision-making, and communication—and ultimately in learning.

To read the rest of the blogs in this series, download our new whitepaper, Data & learning: A common-sense approach.

About the author

Alongside our CEO, Ben Betts, this blog and the rest of the ‘Data & learning’ series has been authored by writer, speaker, podcaster and Communications Consultant, John Helmer.

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