4 Levels of Analytics in L&D and How They Create Value

The learning analytics landscape is buzzing. Thanks to digital tools and technologies, the capabilities to track, evaluate and assess the impact of learning have increased manifold. This enables organisations to increasingly understand not only the learning process, but also the impact of learning to the business itself. However, there’s also a lot of misconceptions around analytics. We’ve seen a worrying tendency to paint a picture of deep analytics, whereas the real capabilities don’t extend beyond rudimentary statistics. To clarify some of the possibilities in this space, we put together this look at the different levels of analytics in L&D. Let’s take a look!

1. Descriptive analytics: ‘what’

Descriptive analytics, by definition, focus on “what happened”. Whereas there’s a lot of hype in the space, most “analytics” still constitute just this type. One could argue that a lot of the descriptive analytics is not actually analytics, but rather simple statistics. These, of course, are usually displayed in a visual and digestible dashboard format, reinforcing the perception of analytical power.

As mentioned, the focus is on phenomena and their magnitude. Some arbitrary examples of descriptive analytics could be how many employees completed training, how long it took them, how they engaged with the learning resources etc. Although the analysis part is limited, there’s still value to be had in this kind of analytics in L&D as well. A lot of these things provide a good basis for reporting. Engagement statistics can even help to improve the quality of learning resources. However, using this mostly quantitative statistical data, you shouldn’t forget to use also qualitative insights to get a complete picture.

2. Diagnostic analytics: ‘why’

Whereas descriptive paints a part of the picture, diagnostic analytics help to complete it. In general, these type of analytics aim to answer the question “why did it happen”. The focus, therefore, is in the underlying reasons behind the phenomena described above.

Overall, there can be incredible value understanding the ‘why’. For instance, why did the learners pass on an activity? Why did the learning not translate into action? Why is a particular learning experience successful? While descriptive information is important, it’s often useless unless we understand the why. By understanding the relationship between different factors, we can make better learning – and business – decisions.

3. Predictive analytics in L&D

While the segment of predictive analytics is not entirely black and white, e.g. diagnostics may contain generic predictive analytics, we’ll deal with it as one segment. Like the name gives away, predictive analytics deal with the future. In general, the aim is to answer the question: “what will happen”. This focus makes it a powerful decision making support tool for not only L&D teams, but the business as a whole.

For instance, predictive analytics in L&D can provide valuable insights on the expected outcome of training, i.e. what kind of effect or impact can we expect. It’s also possible to predict trends, e.g. which departments are on the rise, which are regressing. On a more granular level, it can also help trainers and L&D professionals to determine which learners may be at risk and intervene early, rather than too late after the fact. Another interesting value scenario could be to predict individuals’ potential in reflection to their performance in learning, something that one could use e.g. in leadership pipeline planning.

4. Prescriptive analytics in L&D

Finally, the fourth level of learning analytics in this mapping of ours is prescriptive analytics. Whereas predictive analytics focus on what the future is likely to look like, prescriptive analytics in turn focus on “how to make it happen”. Similar to the previous, these analytical tools tend to offer businesses significant support and power in their decision making. Just like a doctor, the algorithms prescribe a particular course of action to fulfil a given goal.

In the realm of L&D, prescriptive analytics can come in handy on many fronts. One application is to provide recommendation on learning interventions. For instance, the algorithms can calculate the optimal learning paths for different groups or individuals, and identify suitable resources or courses for them to take to progress. Furthermore, these tools also enable scenario analysis, e.g. how to best roll out certain programs. Overall, the goal is optimisation across the board, and the analytics provide the recommended courses of actions to do that.

Final words

Overall, all the different levels of analytics can provide value to learning organisations. Although, the value tends to increase the more sophisticated the analytics in L&D. The development in the space is rapid, and we are constantly finding new ways of capturing learning impact and delivering value through learning. Tools like learning big data, as well as artificial intelligence, are necessary pieces to the puzzle nowadays. They enable us to constantly develop even smarter solutions. If you’re looking to get your L&D analytics strategy up to speed to be able to visualise the real impact of learning in your organisation, don’t hesitate to drop us a note. Let’s take on the future of learning together.