What Learning Analytics Data Do You Really Need?

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Steve Finch
Thinqi Ambassador
What Learning Analytics Data Do You Really Need?

Data analytics is a key focus for learning and development professionals in 2020. As we noted in our previous post ‘Learning Technologies 2020: Our Key Takeaways’, many of us are aware of its importance, yet too many L&D departments still aren’t measuring the right data, or are treating it as an afterthought.

This isn’t to say that L&D’s relationship with data and analytics is anything particularly new or cutting-edge. For years, data has played a fundamental role in demonstrating to business leaders and key stakeholders the return on investment of workplace learning. Without data, L&D would be hard-pressed to justify its existence to the rest of the organisation during times when greater accountability is expected.  

If data has always been fundamental to our practice, why is it all of a sudden dominating discussions in workplace learning for 2020? 

What’s Changed?

In 2019, industry events such as the CIPD Festival of Work were very much focused on the roles of humans and machines in the changing world of work. As skills requirements undergo a radical change, the wider adoption of AI and automation looks set to transform the learning industry as we know it. With this transformation comes a shift in the way we see the modern L&D function. 

Once the primary concern for learning managers, LinkedIn’s 2019 Workplace Learning Report reveals that today, budget restraints are no longer the predominant issue. In fact, talent development teams are now enjoying greater investment than they have done in recent years. Business leaders are recognising that, in order to future-proof their businesses, they need to equip their people with the skills required for new roles.

As L&D is increasingly seen as a driver of employee performance, the department is feeling a greater obligation to produce evidence that their solutions have delivered on expectations. This is less about ticking boxes for course satisfaction, and more about measuring business impact and maximising performance. Are people learning the right skills to stay relevant in the changing world of work? Are they able to employ these skills effectively on the job in order to perform to the expected standard? Do you know how to measure performance data?

Data insights are necessary to steer the crucial decisions in organisations. The data you collect can make or break these decisions – which is why the C-suite is relying on L&D to get it right.

“How Do I Know Where to Start With Learning Data Analysis?”

Gathering the right data is key to informing your decisions on what the most appropriate actions are when it comes to learning, or indeed clarifying whether learning is really the right solution at all.  

Krystyna Gadd, author of the widely-praised book ‘How Not to Waste Your Money On Training’, suggests that you begin by asking yourself: “Why bother collecting or analysing data?” Is it to improve skills? Communicate value? Validate decisions? Or perhaps you simply wish to check that things are going to plan? To begin, let’s go back to the organisation itself.

There’s a reason consulting more deeply with the business is one of this year’s top trends in the Global Sentiment Survey. According to research conducted by Brandon Hall in 2018, it also emerged that three out of four organisations believed that aligning learning strategy with business goals was a top priority. By moving closer to the business, L&D becomes more integrated with its objectives and can develop solutions that are in alignment from the outset, rather than simply taking a reactionary approach. 

What are the primary goals and objectives of the organisation? Have you ensured stakeholder involvement and visibility? What sort of data will help you demonstrate progress associated with these particular goals? In order to make sure you’re answering the right questions, it’s worth considering the four types of data analytics that will help improve decision-making. These are:

  • Descriptive analytics – What has happened?
  • Diagnostic analytics – Why has it happened?
  • Predictive analytics – What is likely to happen?
  • Prescriptive analytics – What action should be taken next?

These four categories are necessary to paint the full picture of how the learning has – or has not – delivered on achieving its core aims. Too often we fall into the trap of simply describing the data (descriptive analytics) without supporting it with the context afforded by the various other types.

“What Type of Data Will I Need?”

Now that you’ve established what your goals are, you need to gather the right type of data to demonstrate how L&D is helping to achieve them. 

Different stakeholders will have different priorities. Say you’ve established that you want to demonstrate to business leaders that the skills learned through your current training programme can help boost employee productivity; to identify what data is needed, you’ll have to do some investigating. Here you could start by examining current work output and then collecting evidence and feedback as your baseline to measure against post-training.

The metrics you choose will vary depending on your goals; there’s no ‘one-size-fits-all’ approach to data. For example, different metrics you could use for the example above include:

Adam Harwood, Head of L&D at D&D London, demonstrated at this year’s Learning Technologies conference that even positive data results are meaningless if they do not support actual goals. In the session, he recounted his experience of how, on the surface, an abundance of enthusiastic responses on learner ‘happy sheets’ can be deceptively positive. However, the goal was to evidence an improvement in performance, not to show how much learners had enjoyed the course. The data collected, in this case, was irrelevant to context. 

“Your learners might have enjoyed it and they might have showed up,” said Adam, “but has the learning actually made a difference in terms of performance?” 

This is precisely why it’s vital to keep reflecting back to ensure the data collected is always in alignment with the initial goal – after all, this is the evidence your stakeholders really want to see.

In Summary…

Get clear on your analysis before you start collecting data. Think of the what and the why, making sure the agreed objectives are clear, measurable and trackable. Once you’ve decided on the relevant metrics, keep those objectives in mind throughout. This will ensure your data stays relevant and will provide you with the insights you need to present a strong case once it’s time to present your evidence to the business.

It’s never too late to go back and alter your strategy so that it remains aligned to your objectives; in fact, it’s good practice. In L&D, we’re aware of the importance of being adaptable when the need for change arises – data analysis included.

Data doesn’t have to invoke dread if you go back to basics and break it down. Kept simple and relevant, data analysis is one of the most powerful tools in your L&D toolkit.

If you’re looking for the right technology to collect the best data for you, we’ve got the tools and expertise to help you succeed. Request a demo of Thinqi to arrange to speak to one of our experts.

We’re going to be exploring how to conduct effective data analysis in the next part of our series, so keep an eye on our blog and social media channels to see when these insights are published:

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Steve Finch
Thinqi Ambassador
Steve Finch is Head of Marketing and Brand Ambassador for Thinqi, the modern learning system. With a background in customer success and digital learning programme delivery, Steve has been helping organisations deliver effective modern learning for nearly 20 years.