Data Storytelling: The last mile of data literacy, by Director, Upuli de Abrew

When telling data stories, don’t focus only on the story (the data) – this is simply a set of findings, observations and facts.

Most organisations today understand the importance of data-driven decision-making as a cornerstone for success. Our data-rich world has created an expectation that all workers are knowledge workers, operating within a data-centric culture to generate value using information and insights.

Companies are launching data literacy programmes, aimed at upskilling knowledge workers in terms of how to interpret and present data, but many stop here, often neglecting to focus on the last (and arguably the most important) mile of data literacy – data storytelling.

As long as humans have existed, they have told stories to communicate with others and to record accounts of their daily lives. From cave paintings to modern novels, storytelling methods have changed over time; however, the psychological power of storytelling remains true today.

Yuval Noah Harari, in his iconic book, Sapiens: A Brief History of Humankind, postulates that storytelling is the single contributing factor to the survival and success of the human species on our planet. It is written into our very nature as human beings, and it is therefore easy to understand why we are returning to the ancient art of storytelling to survive, and master, the burgeoning volumes of data with which we are bombarded daily.

Focus on the storytelling by engaging the audience, and helping them make sense of what they are seeing and hearing.

Data storytelling is the act of turning raw data into a compelling narrative, which is significant, relatable, interesting and has a clear call to action. Facts present raw data, while a compelling narrative provides context, which connects the facts to events, and highlights the significance of those facts. When executed skilfully, data storytelling can help a business make data-driven decisions that drive overall performance.


There are three components to data storytelling, which can be combined in different ways depending on the nature of the message, the intended audience and the desired outcome.

Data analysis

Accurate, complete data is the foundation of the data story. Descriptive analysis (which describes the data set), diagnostic analysis (which diagnoses what might have happened to the data), predictive analysis (which forecasts trends based on past data), and prescriptive analysis (which recommends actions based on future expectations) are all useful tools for understanding a dataset.

Data visualisation

Data visualisation is the practice of aggregating raw data into visual information (such as maps, charts, graphs, infographics and animations), which are more easily understood by the human brain. The goal of data visualisation is to identify patterns, trends, relationships and outliers.

Data journalism (storyline)

Data journalism is the use of journalistic skills to find data-driven stories that help people understand complex topics in a way that is more accessible than traditional reporting methods. A narrative is a story that communicates insights gleaned from data, the context surrounding it, and actions you recommend. A well-crafted narrative, or story, helps communicate actionable insights to the audience.


Become a storyteller

Jay Acunzo, in his blog “Inside the Storytelling Approach of Stephen King”, sums up this legendary storyteller as follows:

“It’s clear that King understands the difference between ‘good stories’ and effective storytelling. His ability to tell a good story stems from a keen understanding of details that transcend genre, while his ability to be an effective storyteller is derived from a combination of process and attitude − and the nebulous magic at the intersection of those two things.”

Similarly, when telling data stories, don’t focus only on the story (the data) – this is simply a set of findings, observations and facts. Focus on the storytelling by engaging the audience, and helping them make sense of what they are seeing and hearing.

Know your audience

Who is your audience, how much do they already know, what do they want or need to know, what are their expectations? These are just some of the questions that you should have at least partial answers to whilst crafting your story. The better you know your audience, the more likely it is you will be able to tap into their specific wants and needs, to tell a story that is compelling and at the right level of detail.

Structure the story

Good stories have common characteristics like characters, plotlines, settings, themes and symbols. These elements are also important for effective data stories because they help readers understand the message you’re trying to convey.

A well-structured story has a clearly defined beginning, middle and end. It starts with an introduction that sets up what the audience will learn about. Then it moves into the body of the story, which is where it provide examples, evidence and other supporting material to prove the point. Finally, there’s a conclusion that offers a summary of what been covered, and ideally, a call to action.

Storyboarding can help sequence the flow of events and spot gaps where more supporting information is needed. It is a plan for the narrative, and helps the storyteller to stay on track without getting lost in the details.

Visual best practices

Make use of best practices when it comes to selecting and creating visualisations for the story. Some of these include:

  • Keeping visual consistency, which ensures a rational visual narrative that is easy to follow.
  • Selecting the most relevant visuals for the story; for example, line charts show trends, pie charts show contributions, scatter plots reveal outliers.
  • Colouring appropriately to make items stand out (for example, red for bad, green for good).

Context is key

Contextualise the story by including relevant information about where the data comes from, how it was gathered, why it is important, and why you have chosen to visualise it in the way you have.

As with any craft, data storytelling is an art that takes time to hone, and necessitates so much more than just the ability to create and understand beautiful visualisations. It requires practise, empathy, interest, research and preparation to tell a story that is evocative enough to create truly actionable insights for the audience.

“Description begins in the writer’s imagination, but should finish in the reader’s.” − Stephen King, On Writing.

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