Whilst everyone agrees that learning at work matters, not all organisations are good at knowing whether their learning programs actually work. That’s because optimising learning is messy. It needs data from multiple systems, departments, and stakeholders.
Historically, L&D hasn’t rushed toward that complexity. But that’s changing.
Three things have shifted the landscape:
- Learning management systems (LMS) are now widespread, and they hold large amounts of learning data.
- Cloud platforms enable data to be combined and analysed without the need for huge data warehouses. That makes learning analytics projects much more cost effective.
- AI tools are available off the shelf and viable for small, focused experiments.
The result is that learning analytics is no longer theoretical or reserved for large enterprises. It’s affordable, practical, and increasingly hard to ignore.
So where do you start?
What is learning analytics?
It’s always useful to start with a definition, and learning analytics is pretty straight forward:
Learning analytics improves learning outcomes by analysing data.
It may not be complicated, but it’s still useful. And it also gives you the correct order to do things - an order many organisations still get wrong.
Step 1: Define what “better” looks like
You don’t start with data. You start with outcomes. What are you actually trying to improve?
- Fewer errors?
- Higher sales?
- Faster onboarding?
- New skills that are being used in day-to-day work?
There is no universal answer. It will depend on the business context and the problems you’re trying to solve. But until you can clearly describe what success looks like, analytics will only produce dashboards, not insight.
Step 2: Decide how you’ll measure improvement
If you can’t agree on a measure, you can’t tell whether learning made any difference.
Choose metrics that reflect the outcome you care about, and that you can actually measure. That might include:
- Knowledge gained
- Reduced error rate
- Skills demonstrated
- Business performance indicators
Your metrics don’t need to be perfect they just need to be useful. The goal is better decision-making, based on data.
Step 3: Identify the data that supports those metrics
Most organisations already collect far more data than they use: LMS activity, course attendance, assessment results, feedback surveys, HR data, performance metrics. The problem isn’t that the data doesn’t exist, it’s that it’s siloed.
When data from multiple systems is brought together, patterns start to emerge. Training volume alongside retention. Learning pathways alongside performance. Timing of learning alongside error rates.
Putting it into practice
This approach works best when treated as iterative. Small proof-of-concept projects using modern business intelligence tools and AI can deliver fast feedback on existing learning initiatives.
You don’t need a multi-year transformation. You need a focused question, a clear metric, and enough data to test whether your learning is actually helping.
If you are looking at getting more from your learning data, and improving learning outcomes, get in touch for a no-obligation chat.
Keywords: learning analytics, data analytics, learning management system, LMS, Power BI, L&D dashboard, Azure AI
