The words “data analytics” make many L&D practitioners duck and hide. Whether it’s due to a lack of understanding or from the fear of what the data might unveil, L&D have historically looked the other way when it comes to data. And this needs to stop.

The learning industry has changed. Since 2020, we have (finally) embraced the reality of digital learning. We’ve debunked a myriad of theories (we’re looking at you, ‘learning styles‘). Industry pioneers have forced us to rethink our age-old habits that aren’t working any more (if they ever did at all..) Vendors have (finally) accepted that there is no one ‘silver bullet’ for L&D . And now we’re facing a future of L&D made up of unique, bespoke and data-driven learning. A future of learning that actually works. With all this in mind, it’s no surprise that data analytics is one of the top skills L&D are looking for in 2022.

But these changes have resulted in many practitioners scratching their heads and wondering where to get started. In this blog, we’re going to share with you our 4 steps to getting started with data analytics in L&D.


Four ways to get started with data analytics

1. Understand the data you have

Learning technology platforms provide us with a plethora of data. Dare we say it – learning technology platforms might even provide us with too much data. But the sheer volume of datasets available to us can sometimes make getting started even trickier.

So, before you try to utilise this data in your L&D initiatives, we strongly recommend you write a complete inventory of all the datasets you have. You don’t need to download or accumulate any data at this stage, a simple list will do. But you need to know what data you have available and where.

This process will be super handy further down the line, when you want to cross reference data. For example, you might want to know how engaged learners are based on how long they’ve worked for your company. For this you’ll need data about learning activity from your learning platform, and data from your HR platform about length of tenure. So having a straightforward list of where all information is held will save you time in the future.

2. Give yourself time

To effectively use data in your learning initiatives, you must give yourself the time to process and understand the data you have. All too often we witness L&D  putting too much pressure on themselves. And if that resonates with you: this is your sign to stop! This rush to use data and get the solution out the door as quickly as possible often leads to mistakes, and incorrect evaluation of the data at hand.

So, carve out some time to look through and understand the data. Think about what it’s telling you. Think about how this can influence your future actions. Think about what it teaches you about what you’ve done in the past. Time is your friend here. In fact Lori Niles-Hofmann’s first tip in her LinkedIn Learning course ‘Data Driven Learning Design‘ is just that: Dedicate more time to review data to better diagnose your learning needs.

 

3. Bear causation vs. correlation in mind

When looking at your data always keep in mind causation vs. correlation. Just because two datasets correlate (i.e. move in the same direction as one another over the same period of time), it does not mean that one of these causes the other. Here’s a great demonstration of correlation and causation between ice cream consumption and murder rates in NYC:

Despite being correlated, I’m sure we can all agree that eating more ice cream does not cause more murders. And the same can be said for business datasets too. It’s important to keep this in mind when making decisions based on data in L&D. If you assume a causation effect, and dismiss the notion that a third party might be influencing your datasets, you may end up making ineffective, data-driven decisions.

4. Embark on your own learning journey

There is so much to learn when it comes to data analytics, and nobody is expecting L&D to become fully-fledged data analysts alongside their day job. But you might feel that having a few more a data analytics skills in your toolkit might come in handy. So, we’d recommend embarking on your own learning journey (something L&D often forget to do!)

So, why not start with some of these ideas?

  • Improve your problem-solving skills (we think this Grow with Google video is a great start!)
  • Learn new Microsoft Excel skills for analysing data (in fact, @exceldictionary on Instagram is a great place to get some top tips!)
  • Focus on your communications skills for when you’re explaining and discussing your datasets with colleagues.
  • Improve your project management skills. Devise a logical and methodological approach to analysing your data.

 

So armed with these four steps to getting started, we hope you’re ready to create more data-led learning interventions that actually work. And if you’re a data analyst that loves all things learning, why not get in touch and work with Jam Pan? We’d love to match you up with some of our amazing clients who are looking for people just like you.