You’ve got the data. Maybe a lot of it.
You’ve analysed the numbers, spotted a few interesting patterns and pulled out some findings. Now comes a surprisingly difficult question:
What’s the story?
Because finding an insight in the data is only part of the job. You also need to work out what it means for your audience, how to frame it and what they should take from it.
That’s where data storytelling comes in.
What is data storytelling?
Data storytelling is the process of turning data insights into a story your audience can understand and use.
It brings together three elements:
- Reliable data
- Narrative
- Data visualisation
The data provides the evidence. The narrative explains what the evidence means. And the visualisation helps people see important patterns and relationships.
That combination is what separates data storytelling from simply presenting data. A chart might show, for example, that sales have fallen. A data story explores what changed, where the decline occurred, why it matters and what the business might need to do next.
So, data storytelling starts well before you choose a chart.
Here’s a practical data storytelling process you can use to get from raw data to a story that helps people make sense of it.
8 data storytelling techniques to turn raw data into a meaningful story
1. Start with the audience – and the decision
Before digging into the data, think about who will be receiving it.
What do they already know? What matters to them? And, importantly, what decision or discussion will this information inform?
An executive team, for example, probably doesn’t need the same level of detail as an analyst. A client may need context that your internal team already understands.
Effective data storytelling is audience-focused from the beginning.
2. Interrogate the raw data
Now you can start looking for the story.
What has changed? What’s higher or lower than expected? Are there significant differences between groups? Has a trend emerged over time? Is there an anomaly that warrants further investigation?
At this stage, don’t worry about making the data look good. Your job is to understand what it’s telling you.
And be careful not to fall in love with the first interesting data point you find. Look at the broader picture before deciding which data insights deserve attention.
3. Find the story – without manufacturing one
Good data storytelling requires judgement.
Perhaps sales increased significantly after a new campaign was launched. That’s worth exploring. But unless your data establishes causation, you can’t automatically claim the campaign caused the increase.
Similarly, an unusual result might be interesting without being meaningful.
Your data story needs to reflect what the evidence can genuinely support. Check your data quality, consider the limitations of your data sources and be transparent about uncertainty where it exists.
A compelling data story still needs to be a trustworthy one.
4. Work out the ‘so what?’
Once you’ve identified your key insights, ask what they mean for this particular audience.
Imagine employee turnover increased from 12% to 18%.
The increase is the finding. The story starts to emerge when you establish where the increase occurred, how it compares with previous periods and why it matters to the organisation.
Perhaps most of the increase came from employees in their first 12 months. Now you have a much more useful question to explore: what’s happening during that first year?
That gives your audience something they can investigate and potentially act on.
5. Give your data context
A number in isolation rarely tells much of a story.
Is a customer satisfaction score of 76% good? You’d probably want to know what it was last year. You might also want to know the target or an appropriate benchmark.
Where relevant, compare data points against:
- Previous periods
- Targets or forecasts
- Relevant benchmarks
- Different groups or segments.
Context helps your audience judge the significance of what they’re seeing rather than leaving them to work it out themselves.
6. Build a clear narrative
Once you know your key message, decide how to take your audience through it.
For many workplace data stories, a straightforward narrative structure works well:
Problem → Evidence → Insight → Action
What are we trying to understand? What does the data show? What can we conclude from it? What should happen next?
You don’t need to force your numbers into a dramatic storyline. But you do need a logical sequence that helps your audience follow your thinking.
Different types of stories may call for different structures. We cover several useful storytelling formulas in our guide to business storytelling.
7. Visualise the story
Now it’s time to think about charts.
Your data visualisation should make the story easier to see. Line charts are generally useful for showing trends over time. Bar charts can make comparisons between categories clear. Scatter plots can help reveal relationships between variables.
Choose your chart according to the message you need to communicate – not because it looks impressive.
Then direct attention to the key insight using descriptive titles, annotations and visual hierarchy.
We’ve covered these data visualisation storytelling techniques in much more detail in our guide to creating clearer data visuals.
8. Decide what happens next
A data story shouldn’t leave your audience wondering why you showed it to them.
What does the insight mean for the business? Does someone need to make a decision? Investigate something further? Change an approach? Keep monitoring the data?
Sometimes the data won’t support an immediate recommendation. That’s fine. The next step might simply be identifying what else you need to know.
The important thing is to connect the data to what happens next.
Before you share your data story…
Run through this quick checklist:
☐ Audience: Do you know who you’re communicating with and what they need from the data?
☐ Insight: Have you identified the most important finding – rather than simply presenting all the data you have?
☐ Accuracy: Does your story reflect what the data can genuinely support?
☐ Context: Have you given your audience enough information to understand whether the numbers are significant?
☐ Narrative: Is there a clear path from the evidence to the insight?
☐ Visualisation: Does your chart or graph make the key message easier to understand?
☐ Focus: Have you removed data points and detail your audience doesn’t need?
☐ Next step: Will your audience know what they should consider, decide or do next?
Help your team tell stronger stories with data
Having access to data is one thing. Knowing how to communicate it effectively is another.
CSA’s data storytelling training gives professionals a practical approach to turning complex data into stories their audiences can understand and use.
Participants learn how to identify and structure different types of data-driven stories, pair storylines with suitable charts and graphs, make visual content more accessible and develop a collaborative content strategy.
Want your team to get better at finding – and telling – the story in their data? Explore our Data-Driven Storytelling workshop.