“Data analytics is the science of extracting patterns, trends and actionable information from large sets of data.” – Daniel Burros, Huffpost
You’re in a meeting with your business leaders 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 that your strategy has worked, your stakeholders are going to want to see solid evidence in the form of data. However, just sliding a few bar charts and statistics across the table isn’t going to mean anything if you don’t demonstrate how the data supports the message you are trying to get across – and this, perhaps, is where L&D often tends to get it wrong.
In order to demonstrate real insight from analytics, you’re going to have to become comfortable with telling the story behind the data – in other words, L&D leaders need to become data storytellers.
Why is data analytics so important?
Data analytics is “the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software”. It’s a vital step in linking the outcomes of learning interventions with the initial goals – something which will help you prove the business value of L&D during times where budget cuts are common and departments have to fight for investment.
In fact, data analytics forms a key part of each of the following:
- The ‘Performance and Impact’ area of the LPI capability map
- The ‘Evidence-Based Decision Making’ at the core of the CIPD Profession Map
- The ‘Improving Impact’ section of Towards Maturity’s Snapshot of L&D Capability
- The 2019 Global Sentiment Survey, run by Donald H Taylor, where it ranks at number 3 in this year’s list of hottest trends for L&D
The evidence is clear: data analysis is a critical part of the skill set of the modern L&D practitioner. And if you’re going to future-proof your role, it’s a skill you’re going to have to equip yourself with.
How to tell a story with data
James Richardson, Senior Director Analyst at Gartner, notes that “data and analytics teams have always created dashboards and visualisations, but many are unfamiliar with wrapping those artefacts into a narrative.” And even then, this narrative needs to be made 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.
With this in mind, what steps can you take to ensure you’re telling the best story possible with the data you’re presenting?
1. Start with the question
To make sure your story is relevant, you have to keep in mind the key question you are trying to answer. This will likely link back to the initial problem – in this example, rising rates of attrition and low employee engagement.
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 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
From bar graphs to pie charts, line graphs to infographics, the options for displaying your data can seem somewhat overwhelming. However, the key here is to make sure that the choice you make is relevant to the point you’re trying to prove.
You could start by looking at how others have displayed similar information, or you could try presenting the same data in a number of different formats to identify which type people engage with the most.
Remember, there is no one type of data visualisation that works for all situations. It’s far more likely that you’ll have to employ a range of different options depending on the context of the data and the audience you’re presenting it to.
3. Know your audience
Different stakeholders will have different priorities and therefore different requirements when it comes to gaining insight from data. For example, when it comes to gaining support for L&D, it’s the business leaders that are often cited as the most challenging group to convince. This group wants cold, hard data. They want clear evidence that any improvements to return on investment can be directly attributed to learning. Just showing a line graph that illustrates increased employee engagement will do little to pique their interest if you haven’t put this into context. Why has employee engagement improved? What benefits does this bring to the bottom-line and ROI? And how can this be directly attributed to learning?
Jim Stikeleather made a valid point in Harvard Business Review when he said that “visualisation in its educational or confirmational role is really a dynamic form of persuasion.” Think about what story your audience is interested in and how your narrative can persuade them to accept what you’re trying to say.
4. Tell the full story – don’t censor!
When trying to convince your stakeholders, it can be tempting to censor or brush over any evidence that works against the argument you’re trying to make. However, any inconsistencies will be questioned and trust will quickly be lost if you’re not giving your audience the full story. Remember, nothing in business is ever perfect and your stakeholders know this.
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 you will find stakeholder relationships improve as their trust in you grows.
According to a study by Towards Maturity, ‘L&D’s Relationship With Data’, 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 that data to stakeholders in a way that truly offers value. Measurement isn’t easy, but this blog post from our ‘Learning in the Modern Workplace’ series can provide you with an overview on how to do it effectively.
Remember, good data analysis is not about not about describing and reporting. Your data has a story to tell.
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