Unlocking the ROI of Learning

Or how to free your data from the LMS

Relevant to: HR, L&D, Operations, Finance

You’re under pressure to show the impact of learning. You need to prove that training links to performance, that learning programmes improve retention, and that investment in learning is moving the profitability needle.

But when you go looking for answers, all roads lead to your learning management system (LMS) — and stop there.

You know the data is in there somewhere. Completion rates, assessment scores, time spent learning, department-level engagement — all the raw material for powerful insights. But what you get instead are clunky exports, static reports, and dashboards that don’t speak the language of the business.

If this sounds familiar, you're not alone.

You’ve got the data!

You know you’ve got the data, that’s why you have an LMS. The bad news is that it’s not always easy to get at – particularly if your LMS is software as a service (SaaS).

If you have found your valuable learning data locked in your LMS, I feel your pain. This wasn’t what you were expecting.

Using your data

There are different ways to use data stored in your LMS. For many, the standard reports are more than enough. But for those that want to analyse learning data together with other data, or do more in-depth analysis, you have to get data out of your LMS and into a reporting data store. Here’s how that might work:

1. API extraction – most modern LMS platforms provide RESTful APIs. With proper authentication (usually via OAuth 2.0 or an API key) you can programmatically extract learning records.
2. Data pipeline & transformation – Data is ingested into a reporting database such as Azure SQL Database, where it's cleaned, normalized, and enriched with metadata.
3. Semantic modelling – Using tools like Power BI, you can then build a semantic layer that defines business terms — e.g., "active learner", "average time to completion", "learning impact score".
4. Dynamic dashboards – These models power interactive visuals, filterable by time, team, location, or training programme — and update in real time or on a schedule.

If you are wondering why you need to store, check and clean data in yet another data store, I talk about that here: Can you trust your data? The bottom line is that your data needs to be clean, up to date, and readily available for analytics purposes. 

Power BI is L&D’s new best friend

With this approach, you’re no longer limited by your LMS’s front end. You get full control of your learning data — and the power to connect it to performance or finance data for deeper insights.

So, although you may sometimes feel that your data is locked in your LMS, there are ways to get at it and analyse it in friendly tools like Power BI.

Here at Anatec AI we’ve been working with data, interfaces, APIs, and learning systems for many years. So, we are well placed to help if you need it. And we can help with Power BI dashboards, scorecards, DAX queries and semantic models. There’s nothing we’d like more. 

If you have a question or want to chat about any challenges you’re facing, get in touch.

Keywords: learning analytics, data analytics, learning management system, LMS, Power BI, L&D dashboard, L&D scorecard

Can you trust your data?

If you’re relying on data to make decisions, here’s a question for you:

Can you actually trust your data?

Bad data leads to bad decisions. And if you’ve ever tried to build a Power BI dashboard using unprepared data… you know what I'm talking about. That’s because good analytics starts before the dashboard—with trustworthy data.

So, let’s talk about what makes data trustworthy—and how to get there.

The 3 C’s of trustworthy data

To be useful, your data needs to be:

• Correct

• Current

• Constant

These three qualities are the backbone of any solid reporting system. Miss one, and your insights could be misleading at best—or flat-out wrong.

1. Correct data: accuracy isn’t optional

Data errors creep in more easily than you think. A common name mix-up might credit someone with attending a course they never showed up for. Or a stock item might never be recorded because “we were going to use it straight away.”

Sound familiar?

Then you’ve got things like:

• Null values

• Duplicates

• Outliers

• Inconsistent fields between systems

All of these distort the truth your analytics are supposed to reveal. Cleaning and validating your data isn't optional—it's foundational.

Question for you:

What’s the most unexpected data error you've ever uncovered?

2. Current data: how fresh is “fresh enough”?

Everyone has a different definition of “up-to-date.”

• For a factory floor, real-time data might be essential.

• For HR reports, yesterday’s numbers might do just fine.

But what matters most is transparency—do you know how current your data is, and can you trust that timestamp?

3. Constant data: reliable, available, and secure

Once your data is cleaned and verified, you need to keep it:

• Securely stored

• Regularly backed up

• Available wherever it’s needed

You don’t want your cleaned dataset disappearing on a lost laptop or overwritten by mistake. Constancy means your data is dependable and accessible, day in and day out.

Choosing the right data platform: why Azure?

The Azure data platform gives you flexible, scalable ways to store your analytics-ready data, depending on your requirements:

Azure SQL Database

• Great for datasets up to a few terabytes (TB)

• Geo-redundant and cost-effective

• Easily scalable up or down

• Supports Medallion architecture using schemas or databases

(If you’re curious about that approach, I wrote more about it here.)

Microsoft Fabric

• Ideal for high-performance analytics at scale

• Better suited for large volumes of data

• Higher cost, but better performance

• Also supports the Medallion architecture.

Once your data is in the cloud, everything gets easier—from sharing semantic models to boosting Power BI performance.

Don’t skip the foundations

Data visualisation tools like Power BI are only as strong as the data underneath them. Trustworthy data isn't just clean—it's correct, current, and constant.

So, here's a challenge:

What’s your biggest headache when it comes to data quality or reporting?

Drop it in the comments—I’d love to hear what you're wrestling with (and maybe swap ideas on how to fix it). And as always, if you want to talk about data quality, you can get in touch here.

Anatec AI has worked with data quality issues for many years. We focus on helping companies make better use of their data to improve their performance and resilience.

Key words: reporting, analytics, data quality, Power BI, Microsoft Fabric, dashboard design