How to Leverage Data in Training Needs Analysis?

The training needs analysis is a real staple in the corporate L&D field. Everyone does it, yet the real value-add is ambiguous. The traditional ways of doing it are not employee-centric, which results in irrelevant and inconveniencing rather than enabling learning activities. While extensive use of data to support that analysis is clearly the best direction to take, organisations don’t often understand how. Thus, we wanted to explain a few of different ways you could leverage data in your training needs analysis.

Using search data to understand what your learners really need

One of the biggest problems in training needs analysis is that the people doing it don’t often really talk to the end user of the training. And naturally, they don’t have the time either. While it would be nice to sit down for a 1-on-1 with each learner, often that’s not a practical nor feasible possibility. But what if we could have the learners talk to us anyways? That’s where data collection comes in handy.

By monitoring e.g. what your employees search during their work can be a really good indicator of the types of things they would need to learn. As most of workplace learning happens that way – employees searching for quick performance support resources – you should really aim to understand that behaviour. So, why don’t you start playing Google? You already should have the capabilities of tracking search history on company devices or within your learning systems. These searches are highly contextual, as they happen within the direct context of learning or work. It’s just a matter of compiling this data and using it to support your training decisions.

Using real-time learning data to identify organisational skill gaps

Another stream of data that you should be looking into when doing training needs analysis comes directly from the learning activities themselves. First of all, you should make sure that the learning data you collect is relevant and actually gives an accurate representation of learning. If you’re not yet using xAPI, start now. You’ll unlock a whole new level of analytical power.

Once you’ve got that covered, you should track that data across the board. This enables you access to individual-, group- and subject matter level insights. For subject matter (i.e. training topics), you’re better off tagging all your learning content appropriately. By having an up-to-date representation of what learning experience related to what topic or competency, you enable quick glances into your organisation’s learning. For instance, a skills heat map might aggregate this “tagging” data and learning data to give you a visual representation on which areas your learners are lacking in competence. Then, you can start drilling down on the group- and individual levels to determine why some are succeeding and some are not. This helps you to craft better and much more personalised training activities and learning solutions.

Using performance data to understand the business needs

Naturally, organisational learning should always support the business rather than inconvenience it. Therefore, it’s important to measure and understand performance. If you don’t keep track of performance, it’s impossible to measure real learning impact and consequently do effective training needs analysis. Performance data is everywhere, often scattered across the business in various systems and silos. Different departments might have their own data and some of it may be centralised. But whether it’s sales, marketing, customer facing staff, operations, finance or HR, the data is often there already. And it’s incredibly important to tap into this data, regardless of where it is.

However, one extremely important thing to note is not to use performance data in isolation. Rather, you should always compare it with your learning data. For instance, if looking at performance data alone, you might see that performance of department X is lacking. The easy answer would be to “assign” more training. However, looking at learning data could reveal that training has not solved the problem before and thus you should be looking at completely different solutions to it. Furthermore, you should always be careful in jumping to conclusions when linking learning to performance impact. Again, the L&D department might see performance improvement as a quick win, but a deeper cross-analysis with learning data could reveal that the performance improvement wasn’t actually caused by the training.

Final words

Overall, there are tremendous amounts and types of both learning- and non-learning data we can leverage in training needs analysis. The above provides just a few examples. With better analysis we can provide better learning experiences and positively impact business performance. To not leverage the vast amounts of data available to do that is simply foolish.

If you need help in crafting more data-driven learning strategies or adopting technology to do so, lets talk. Just drop us a note here.

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