Why your team's data isn't landing with stakeholders

Most data professionals are good at their jobs. They can pull the numbers, run the analysis, build the report. The hard part comes when they have to communicate what they found.

Maybe their presentation gets a polite nod. Their dashboard goes unopened. The insight that took two weeks to produce gets filed away without anyone acting on it. This is one of the most common frustrations in data work (and something I've personally experienced), and it rarely has anything to do with the quality of the analysis.

The problem usually isn't the data

When data doesn't land with stakeholders, the instinct is often to improve the visual—a better chart, a cleaner dashboard, a more polished slide. But communication breaks down long before visuals are designed.

The real issue is usually one of three things: the wrong context, the wrong audience, or the wrong message. Sometimes all three at once.

Data visuals are easy to ignore because organisations are saturated with them. Charts in emails, dashboards in meetings, reports that arrive every Monday and get skimmed on Friday. The volume is high enough that stakeholders have learned, largely unconsciously, to filter most of it out. But what cuts through all this is a clear message—one that connects the numbers to something the audience already cares about.

The audience decides what works, not the analyst. A visual that makes perfect sense to the person who built it may mean nothing to a senior leader seeing the data for the first time. Without added context, this data is too easy to misread or to ignore entirely.

Starting in the wrong place

A common pattern is an analyst completes their work, then figures out how to present it. The communication is treated as the last step, something to sort out once the analysis is done.

The problem with this approach is that it puts the data before the audience and the organisation's goals. By the time the analyst is thinking about how to communicate, the choices that shape the story (what to analyse, what to include, what to leave out) have already been made.

More effective data communication starts earlier. Before diving into analysis, it helps to zoom out. What problem is this work trying to solve? What goal is it serving? What action do you want your audience to take, and what would they need to understand before they could take it?

Those questions change what you look for in the data, what you then choose to present, and how you frame the findings. The analysis becomes easier to communicate because it was shaped by a communication goal from the start.

The message gets lost in the method

Another pattern: analysts present their work the way they experienced it. They walk through the data, the methodology, the caveats, the findings. This makes sense from their perspective. But stakeholders aren't interested in the method. They're interested in what it means for them.

People connect with the message, not the method. When presentations lead with process, anyone not interested in this process (which is usually most people) will disengage. Alternatively, when they lead with a problem the audience faces and a finding that speaks directly to it, people listen.

This is why the story matters as much as the data. A narrative structure tells stakeholders what this data means for the organisation and what to do about it. People remember the story. They repeat the story. The numbers, on their own, tend not to stick.

What changes when teams learn to communicate strategically

The change that makes the biggest difference is learning to think about communication as part of the work, not something that happens after it. That means understanding the business context before building anything, knowing your audience before choosing your format, and leading with a message rather than a dataset.

Teams that develop these skills start producing work that gets acted on. Their recommendations carry weight, and over time, they move from being the people who supply the numbers to being the people who shape the decisions.

That shift—from analyst to trusted advisor—is available to most data teams. But it requires a specific set of skills that most people haven't been formally taught, because training in data work has historically focused on the analysis side. The communication side is where the gap tends to sit.

The Data Storyteller's Handbook covers the frameworks that underpin strategic data communication, including how to ground your work in business context before you begin. If your team is ready to develop these skills in a structured way, the Rogue Penguin workshops are designed to make that shift happen.

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.