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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'

Data storytelling and data visualisation are often treated as the same thing. They aren't, and understanding the difference changes what your team builds, and whether it actually works.

“Women just choose lower-paid jobs” is a common answer to the gender pay gap. Here’s how comparing people in the same job can hide the rest of the story.

It's one of the first questions we ask when we meet someone. For parents at home with young children, the answer is often "no", and official data agrees. This post explains the McNamara fallacy, how it leaves unpaid work out of the numbers, and what to do about it if you work with data about people.

This is something I often hear about health. But people can only choose from the options they have, and data about people often reflects those options. This post explains the determinants of health and what they mean for anyone who analyses data about people.

Success stories and datasets can leave out the people who didn't make it. How survivor bias props up meritocracy, and how to spot it in your data.

Each generation judges the world by the conditions it first met. Shifting baseline syndrome, a concept from fisheries science, shows why older and younger generations talk past each other on housing in Aotearoa.

Most of what organisations care about is hard to measure, so we measure something else instead. Those proxy metrics work until someone attaches a consequence to them. Here's what happens next, and four things you can do about it.

Dashboards and data stories are often treated as interchangeable. They aren't, and using the wrong one for the wrong situation means the communication fails regardless of how well it's built.

The average is one of the most used numbers in business. It's also one of the most reliably misleading. Here's what it tends to hide, and why that matters for how you tell stories with data.

Most data teams are good at analysis. The harder part is communicating what they found in a way that stakeholders actually act on.

Knowing who your audience is gets you started. Knowing what motivates them is what gets your data story acted on.

Most data professionals think about their audience after they've built something. The Stakeholder Matrix is a tool that shifts that thinking to where it belongs—before you start.

AI has made its way into most data teams, and it can help with data storytelling too — if you give it the right context first. Here's where AI is useful, and a free set of customisable prompts to put it to work.

Most advice about data storytelling assumes a receptive audience. But that's not always the room you're in. Here's what helps when it isn't.

In meetings, you'll hear "the data speaks for itself." But data comes from a world already shaped by systems of power. Here's what that means for how we communicate it.

Most data stories fall flat because they're missing a layer of context. The PGAI Framework (Problem, Goal, Action, Impact) is a structured way to find it before you start communicating.

Every dataset is the product of choices. Most of the time those choices go unexamined because defaults are invisible. Social data literacy is the ability to see them.

Most people working with data have access to more of it than they can meaningfully use. The result is paralysis. This five-step framework gives your analysis direction before you begin.

Compliance reports don't explain themselves. The data stories are there, but someone has to find them. Here's the process I used to pull one out of Wellington City Council's 2024/25 annual report.