Skip to content
7 tips for developing a successful learning analytics strategy post-Covid

7 tips for developing a successful learning analytics strategy post-Covid

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 and two free planning tools 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.

Tools for the journey

We have some tips to give for how you go about shaping this strategy, but before we do let’s focus in for a moment on the area of learning analytics capability. At both an individual, team and organizational level, this is a key consideration of your learning analytics strategy, because it is the area in which L&D most often feels it is at a disadvantage. While appreciating that data is perhaps the most important area where it needs to develop capability, L&D also feels it lacks the skills and knowledge it needs to effectively implement a data analytics strategy.

It is for this reason that Learning Pool has introduced two important new additions to the L&D planning toolkit, two free, freely available resources:

Follow the links to learn more about and access these resources [in the ebook, refer to relevant chapters]. Here, it is worth just briefly talking about the significance of each for strategy planning.

Learning Analytics Maturity Model (LAMM)

Often we are held back in our efforts to advance capability by ‘unknown unknowns’. Specifically, we don’t really know how to judge our level of capability in learning analytics against that of other organizations. Are we laggards or trailblazers? Are there experiments we have undertaken in the past whose significance we are overlooking? Taking the LAMM’s questionnaire allows you to benchmark the ‘state of the art’ of learning analytics within your organization against a broad sample of similar organizations, in four functional areas.

Most likely you will find you are in a similar place to many of them, or even slightly ahead of the pack. But that knowledge is really helpful to you in moving forward.

More importantly, perhaps, the LAMM can also tell you what your next steps are, and in which particular areas you are more or less advanced. This helps you plot your path forward in developing capability and essential parts of strategy planning.

The Learning Analytics Canvas (LAC)

Based on the Business Model Canvas, which will be familiar to many, the LAC is a planning tool and checklist that can be used by anybody embarking on a project or program that is going to involve learning data, regardless of their level of data maturity.

Too often, learning analytics takes place within an organization only on a project or program-related basis. A particular initiative needs to be evaluated (usually after the fact), and the methodology to be adopted is specific to that program. The LAC allows you to use a common planning methodology for any type of analytics activity, and turn analytics planning into a regular, embedded and sustainable part of L&D activity. 

The LAC also helps clear away some of the confusion that surrounds the subject of learning impact evaluation. It is easy to be baffled by the plethora of evaluation methodologies that have been thrown up in the 60 years since the four-step model known by the shorthand ‘Kirkpatrick’ was first introduced. Using the LAC will give you a sound basis on which to judge which evaluation model you would be best to use in any given circumstance.

Seven tips for developing a learning analytics strategy 

Equipped with these practical tools, you will be better able to plan a learning analytics strategy 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.

Got a learning problem to solve?

Get in touch to discover how we can help

CTA background