Senior Product Manager Learning Pool
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Seven tips for developing a successful learning analytics strategy post-Covid

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Learning Pool offers seven valuable tips to help with the creation of a successful and sustainable learning analytics strategy.

28th Oct 2021
Senior Product Manager Learning Pool
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L&D stands poised at a moment of opportunity to up its game in using data and learning analytics, but many learning professionals are held back by worries about their own capability gaps and their organization’s readiness to embrace data-driven learning. This post gives seven valuable tips to help with the creation of a successful and sustainable learning analytics strategy.  

Without in any way wishing to downplay the wreckage of lives and livelihoods wrought by the global pandemic, it can’t be denied that the tragedy had a transformative effect on learning.

Almost overnight, in 2020, digital became the default means of supporting learning as the staple of face-to-face “classroom” training became untenable. Digital transformation programs were massively accelerated, long-nurtured pet innovation projects in digital learning that had been back-burnered and sidelined for years were suddenly mainstream, urgent requirements.

Learning curves were sharp for many, and adapting to the new world has not been without pain, but through this unpleasant necessity, we were catapulted into a place of centrality for digital learning that seemed to occupy a spot on the far horizon only months before.

It is difficult to say with any certainty what the coming years might have in store, but research indicates that many of the gains made in online learning will be retained. One of these gains is a more data-rich environment for L&D since digitally supported learning necessarily produces more data than in-person activities.

Today’s organizations already are awash with data—and as L&D comes increasingly to use the common platforms of the business—such as Zoom, Teams, and Slack—it brings L&D, potentially at least, into closer alignment with the day-to-day workflow of a modern, data-fuelled enterprise.

Strong underlying drivers, therefore, as well as increasing demand from business leaders for a more data-driven approach, move L&D professionals into a closer relationship with learning analytics, and necessitate their having a forward-looking, sustainable learning analytics strategy. Here’s some help to do that.

Seven tips for developing a learning analytics strategy 

When planning a learning analytics strategy it's important to make use of data in supporting working people as they strive to improve their knowledge and skills while making sense of a confusing, complex, fast-changing business reality. So as you move to do that planning, here are some things to keep in mind along the way.

1. Take a 360 approach to learning data—You wouldn’t drive using only your rearview mirror, so don’t restrict your focus to learning evaluation (as important as it is); a more holistic approach will also give you side mirrors, dash-cam, and a clearer view of the road ahead. It’s not all about evaluation or analytics.

2. Be proactive—If you wait to be asked for better data by the business, the conspiracy of convenience theory suggests it might never happen, but you will run the risk of irrelevance and, ultimately, obsolescence. Start today in making data a central part of your practice and educate your internal and external customers on the benefits of taking a data-led approach.

3. Make a realistic assessment of your and your organization’s maturity using learning data as the first step in making improvements. The Learning Analytics Maturity Model (LAMM) will help you do this.

4. Start from where you are using the resources you have to hand. Today’s organizations are awash with data, although it might mean forging new alliances and learning new skills to get hold of it.

5. Take an agile approach—Start small, fail fast, learn by doing. Use the Learning Analytics Canvas as a starting point for your next project and see how the project could be driven differently when you start with data.

6. Use multiple data sources to evaluate and take a portfolio-of-evidence approach—and don’t expect there to be a smoking gun when it comes to evaluating impact. Chances are you will only be able to prove that you might be the reason something changed, but you can stack that deck higher with more data points.

7. The customer is always right—In order to progress the adoption of learning analytics in your business, you’ll need to convince customers that the output of your work is worth it. Once customers start asking for analytics, data from the LAMM shows, the business seems to fall in line.

You can view more of our learning analytics examples via our case studies page. Or download our new eBook, ‘Adding data and learning analytics to your organization’ to find out more about good analytics practice.

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