What is data storytelling? (And why it matters for your team)

Most teams that work with data are good at analysis. They can pull a report, run a model, build a dashboard. The hard part comes after these things, when they have to communicate what they found in a way that changes something.

That's where data storytelling comes in.

What is data storytelling?

Data storytelling is the practice of communicating data insights using narrative. It's how you explain what the numbers show—along with what to do about them.

It sounds simple. But it's one of the hardest things a data professional can learn because it requires a completely different set of skills from analysis.

There's a misconception that data storytelling is the same as data visualisation. It isn't. A chart presents data visually, and that can be genuinely useful. But a data story clearly communicates what the data means. You can have a beautifully designed chart that presents data well but tells no story, and you can tell a compelling data story with no visuals.

What is a data story made of?

A data story typically has three components working together.

1. Data: the analysis that forms the foundation of the story. This is where most teams already have strength. The hard part here is deciding what data belongs in what story. It's an editing challenge.

2. Narrative: the structure that guides the audience from insight to the implication. Without narrative, data has to be interpreted—which is fine if you know how to do this. But with narrative, data pushes people toward a decision.

3. Visuals: charts, graphs, and other representations that make patterns easier to see. Visuals support (or tell) the narrative but they don't replace it (see above). Data stories are often told with the help of visuals, but a graph without a narrative is just a graph.

The goal is for these three elements to work together. Most data communication focuses heavily on the first and third, and skips the second entirely.

Why it's harder than it looks

Data analysis and data communication require different kinds of thinking.

Analysis is about finding what's true in the data. Communication is about helping someone else understand what's true, and why it matters to them specifically.

This means understanding your audience before you build anything. A report for a subject-matter expert looks different from a briefing for a senior leader. A dashboard for a team that checks it daily looks different from a presentation to a board seeing the data for the first time. The same insight needs to be told differently depending on who's in the room and what they need to do with it.

It also means being clear on the business context before you start. Effective data storytelling is grounded in a real problem, a defined goal, a specific action, and a measurable impact. Getting that context right before you build anything changes what you choose to show and how you show it.

Three reasons organisations visualise data (and only one of them is storytelling)

Not every data visual is a data story, and understanding the difference is important.

In most organisations, there are three main reasons to visualise data: to discover, to inform, or to educate. Discover visuals are part of the analysis process—they help you find insights but aren't designed to be shared. Inform visuals present data clearly to an audience that already understands the subject. Educate visuals explain what the data means to an audience that needs that context to act.

Data storytelling is what happens when you're trying to educate. And you can't design an educate visual without first understanding the story you're telling.

Knowing which type of visual your situation calls for is one of the most practical distinctions a data team can make, because the skills required, and the effort involved, are quite different.

Why it matters more now than it used to

There's more data available than ever before. More tools to analyse it. More dashboards. More reports. More charts in more meetings. And yet organisations still struggle to make good decisions from data. The bottleneck is the communication.

This is reflected in how data roles are being hired. Data communication skills (the ability to structure a narrative, understand an audience, and present findings clearly) now appear in the majority of analyst and data science job descriptions.

What changes when a team learns data storytelling

Teams that develop data storytelling skills tend to see a few consistent changes.

The first thing most people notice is confidence. Not confidence in their analysis (they usually had that in spades already), but confidence in communicating it. They stop second-guessing how to present findings and start focusing on what they want their audience to do with them. And the reports get shorter because the team is clearer about what matters and what doesn't.

They also start looking outward. Understanding your audience means understanding what they care about, which means understanding the business beyond your own team's remit. Data storytellers naturally develop broader organisational awareness because the work requires it.

Taken together, the shift is from reporting findings to advising on decisions. That changes how a data team is perceived and used within an organisation.

Where to go from here

If you're an analyst or data professional looking to build these skills, The Data Storyteller's Handbook is a practical, illustrated guide to the full data storytelling process. The first chapter is available to read for free here.

If you'd prefer to learn through doing, The Data Storyteller's Course is a self-paced online programme that walks through the same process with exercises along the way.

If you lead a team and want to build shared data storytelling capability, Rogue Penguin's workshops are designed for exactly that. They're available in half-day, full-day, and extended formats, in person or online.

Kat Greenbrook is a data storytelling consultant, author, and workshop facilitator based in Wellington, New Zealand. She is the founder of Rogue Penguin and the author of The Data Storyteller's Handbook.