Data Storytelling Is One Of The Most Underrated Skills In Analytics

Businesses today have more dashboards, KPIs, and predictive models than ever before. But having access to data and actually doing something useful with it are two very different things. According to PBT Group, the missing link is data storytelling, and most organisations are still underestimating how much it matters.

Insights that are technically sound but poorly communicated don’t drive decisions. They get ignored, misread, or filed away. The data isn’t the problem, the story is.

“Data on its own is passive,” says Nicky Pantland, Data Analyst at PBT Group. “It needs to be interpreted, framed, and put into context to be persuasive. Data does not speak for itself.”

Why Data Storytelling Is More Than a Nice-to-Have

In most organisations, people are drowning in information. The real skill isn’t finding the insight — it’s communicating it clearly enough to actually change what someone does next. Data storytelling isn’t a soft add-on to ‘real’ analytics work. It’s the bridge between analysis and action.

A strong data story does three things: it explains what the numbers say, what they mean for the business, and what should change as a result. It turns data from insight into influence.

That’s important because decisions aren’t made on logic alone. Even the most experienced leaders factor in instinct and emotion alongside hard data. A well-told story links the analytical insight with the human context, and that’s what actually moves people to act.

Pantland also points out that good storytelling builds trust. When you walk stakeholders through your methodology, clarify your assumptions, and connect the dots from question to conclusion, you’re not just presenting findings, you’re building credibility.

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Where Most Analytics Teams Get It Wrong

A common mistake is treating a polished dashboard as communication. It isn’t. If there’s too much data and not enough narrative to tie it together, even a beautifully designed report will fall flat. Stakeholders walk away not knowing what matters or what to do next.

Pantland describes data storytelling as a translator between data science and the business. It helps non-technical stakeholders understand what the numbers actually mean for them — and it opens up better conversations about assumptions, priorities, and what the data should be measuring in the first place.

The good news is that you don’t need a design background or writing experience to get better at this. It starts with intention, empathy, and a structured approach. Pantland recommends the following:

Know your audience. Different stakeholders care about different things. Tailor the narrative accordingly.
Lead with the ‘so what’. Don’t bury the takeaway at the end. Start with why it matters.
Use a clear narrative arc. Context, insight, implication, his structure works because people can follow it.
Make visuals earn their place. Every chart should answer a specific question and guide the viewer toward a conclusion.
Ground the abstract in real examples. Analogies and concrete examples make data tangible and memorable.
Treat it as a process, not a deliverable. Test your narrative with someone outside the team, refine it, and keep improving.

What AI Can’t Do (Yet)

Tools like Power BI, ThoughtSpot, and Copilot have made it easier than ever to access, query, and summarise data. That’s a real step forward, but it doesn’t remove the need for human narrative.

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AI can identify patterns and generate summaries. What it can’t reliably do is interpret the ‘why’ behind those patterns in a way that accounts for nuance, business strategy, and organisational context. And it certainly can’t carry the accountability that comes with influencing a major decision.

“As data becomes more ubiquitous, the differentiator is not access to data, but meaningful interpretation of it. And that interpretation must be communicated effectively to matter,” says Pantland.

The technical bar for building dashboards and models keeps dropping. The bar for being clearly understood, by real people, making real decisions, isn’t going anywhere. If anything, it’s rising.

The Bottom Line

Extracting insight from data is no longer the hard part. The hard part and the part that creates real value is turning that insight into something that inspires action, builds alignment, and earns trust. That takes more than code and charts. It takes a story.