How to use data storytelling in L&D

Digital learning experts Thinqi talk give us some great data storytelling advice.

Imagine you’re in a meeting with business executives following a large-scale learning intervention that’s been rolled out across the organisation. The business has had problems with low engagement and high staff turnover and wants to know whether your learning program has delivered on its promise of boosting engagement and reversing rising rates of attrition.

To prove your strategy has worked, you need to be prepared to present clear evidence through data that demonstrates how learning has delivered a successful solution.

However, overwhelming key stakeholders with reams of statistics and irrelevant metrics will prove futile if it fails to demonstrate the key message you are trying to convey – and this, perhaps, is where L&D’s problem lies.

In order to demonstrate real insight from analytics, you need to become comfortable with the concept of ‘data storytelling’.

Why are learning analytics so important?

Placing third in this year’s Global Sentiment Survey, learning analytics remains a key focus for L&D. Data has long sustained a fundamental role in demonstrating to business leaders the return on investment of workplace learning. Without data or evidence, L&D would be hard-pressed to prove the impact of learning, making it difficult to gain the necessary buy-in for future initiatives.

Use the most relevant visuals for your data set and bear in mind that there is no one type that works in all contexts.

Data analysis is a critical part of the skill set for L&D professionals today. Knowing not only how to collect the data, but also how to present it in a way that’s clear and relevant is key if you want to win over stakeholders.

How to tell a story with data

To create a narrative around the data you’ve collected, you need to focus on making it accessible and relevant to the target audience. After all, if key stakeholders are to make important data-driven decisions, they need to be able to connect the dots between data and outcomes.

1. Start with the question

To ensure relevance, it’s important to link back to the initial challenge you are trying to solve. How does the data support your claim that learning has successfully provided the solution? In the context of the problem presented at the start of this article, the organisation is looking to resolve rising rates of attrition and low employee engagement.

What sort of data will help demonstrate progress? In order to ensure you are answering the right questions, it’s worth considering the four types of data 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 we take next?

These four categories are all 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.

2. Use the relevant visuals

The options for displaying data are wide-ranging, from bar graphs to pie charts and from line graphs to infographics. Use the most relevant visuals for your data set and bear in mind that there is no one type that works in all contexts.

Bar charts might be best in one instance, whereas a pie chart or infographic might be better in another. Look at how others have displayed similar information, or experiment with presenting the data in a number of different formats to see which type people engage with the most.

3. Get to know your audience

Different stakeholders will have different priorities. For example, when it comes to gaining buy-in for learning initiatives, your business leaders will likely want to see evidence that increased performance and return on investment can be attributed to learning.


Merely displaying a line graph that illustrates increased employee engagement will do little to pique their interest. Why has employee engagement improved? What benefits does this bring to the bottom-line? How can this be directly attributed to learning?

Think about what your audience is interested in and how your narrative can persuade them to accept your hypothesis.

4. Tell the full story

When trying to win over stakeholders, it can be tempting to brush over any evidence that works against your argument. However, inconsistencies will inevitably be questioned and you risk losing trust if you’re censoring certain results.

Point out any anomalous results and inconsistencies but be sure to explain how and why these results have occurred. By doing this, you can offer intelligent suggestions on how to improve your strategy and avoid these results in future. Not only will this help you develop your own practice, but will strengthen stakeholder relationships as their trust in you grows.

In summary

A recent LPI survey revealed that learning professionals confess that they still lack the skills needed to measure how their learning resources are performing. What’s more, a study by Towards Maturity revealed that 51% of L&D professionals say they cannot use data effectively due to L&D lacking in-house data skills.

However, by collecting the relevant data and consulting the analysts within the business, you can decide how best to communicate your findings to key stakeholders in a way that is truly insightful. Measurement can seem intimidating, but it’s too critical to simply overlook.

Remember, good data analysis goes beyond merely collecting and reporting: your data has a story to tell.


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