Data storytelling – an underrated skill in analytics

Nicky Pantland, Data Analyst at PBT Group

In an age where businesses are awash in dashboards, key performance indicators (KPIs), and predictive models, one skill remains notably underappreciated within organisations: the art of data storytelling.

While analysts and data scientists are trained in statistics, machine learning, and coding, few receive any training or guidance in how to present their valuable findings in a way that moves people to action. As a result, powerful insights often go unheard, misunderstood, or ignored – not because the data was wrong, but because the story was never told.

In today’s fast-paced, information-saturated business environment, the ability to communicate insights clearly and persuasively is just as important as the ability to find them. Data storytelling is not a “soft skill” it’s a strategic capability that bridges the gap between analysis and action.

Data on its own is passive; it needs to be interpreted, framed and put into context in order to be persuasive. Contrary to popular belief, data does not “speak for itself”; it is merely the building blocks of what is important – the story.

If there is too much data and too little story to tie the individual data points together, then it becomes very easy for even the most professional and accurate dashboard to miss the mark, leaving stakeholders unable to grasp and use the insights delivered.

More than numbers

Data on its own rarely drives action. Rather, it is the “so what” that can be derived from the data which drives not only action but also understanding. A data story goes beyond the main numbers, providing the context that those numbers are found in, why they are important and what it means in the short, medium and long-term in relation to the business’s strategies and goals – essentially humanising the numbers. In short, the data story transforms data from insight into influence.

Cognitive research shows that narratives activate more areas of the brain than raw facts. When we hear statistics, the language-processing parts of our brain light up. But when we hear stories, our brains engage as if we’re experiencing the events ourselves. Emotions, empathy, memory; these are triggered through stories.

Why is this important? Because no matter the level or experience of the stakeholder, any decision made will be based on a mix of logic, instinct and emotion. Storytelling, through humanising the data, creates the connections between the analytical insights, human emotion and context. This connection prompts an emotional response and its corresponding action. The human brain is wired for stories, not statistics.

Data storytelling helps demystify the analysis process, removing the distrust of numbers that could feel alienating or suspicious without context. It shows your work, explains your assumptions, and walks stakeholders through the journey from question to insight. Transparency is no longer optional; it’s essential for building credibility.

Think of storytelling as a translator between data science and business. Without it, even the most sophisticated models are at risk of being ignored. Not only is storytelling a translator, but it is also an enabler of collaboration. It prompts investigation of assumptions, needs and relevance across multiple business functions. A good data story aligns everyone around a shared narrative. It helps non-technical stakeholders understand the implications of the analysis in their own terms. And it empowers technical teams to advocate for the resources or changes they need.

The human role in data storytelling

So, why is storytelling becoming so critically important?

The influx of modern tools have democratised data access and availability and made huge volumes of it available to all. However, while these tools, such as PowerBI, ThoughtSpot or Microsoft Copilot, create access and visibility of data, they do not and cannot replace the need for human narrative. AI tools can generate summaries and spot patterns, but only humans can interpret the “why” behind the patterns and translate it into a story that accounts for nuance, business context and strategic goals.

As data becomes more ubiquitous, the differentiator is not access to data, but meaningful interpretation of it. That interpretation must be communicated effectively to matter. As a data practitioner, your career will be influenced by your storytelling ability. Storytelling will differentiate those analysts that are remembered and seen as strategic partners from those who are seen as merely back-end support. Developing the ability to structure a compelling narrative – beginning, middle, and end – can transform your influence within an organisation, especially given the abundance of analysts currently available with the capability to write SQL queries and build models.

Improving your data storytelling doesn’t require a design degree or a writing background. It starts with intention and empathy. Here are a few tactical ways to get better:

• Know your audience: Understand what your stakeholders care about and tailor your story accordingly. A CFO and a product manager need different narratives from the same data set.
• Start with the “so what”: Don’t bury the insight, lead with it. What’s the takeaway? Why should your audience care?
• Structure your narrative: Use a simple arc; context, conflict, resolution. Or, in data terms: background, insight, implication.
• Make visuals work harder: Every chart should answer a specific question. Avoid clutter. Use annotations, colour, and hierarchy to guide the viewer.
• Use analogies and examples: Abstract data becomes tangible when grounded in real-world comparisons or user stories.
• Invite feedback: Great storytelling is iterative. Share early drafts of your narrative with colleagues outside your domain to test the clarity.

As data continues to reshape industries, the ability to extract insight is no longer enough. Analysts must be able to inspire action, build trust, and rally organisations around a shared understanding. That requires more than code and charts; it requires a story.

Data storytelling isn’t just a “nice-to-have” skill today. It’s a game changer for analytics. It’s what turns raw numbers into real-world outcomes. And in the hands of a skilled communicator, it can be the difference between a good analyst and a transformative one.