When Florence Nightingale analysed mortality rates from the Crimean War, she realised that the majority of soldiers hadn’t died in combat, but instead from preventable diseases caused by poor sanitary conditions in the hospitals.

To convince the British Parliament and Queen Victoria to invest in better sanitary conditions, Nightingale created a diagram of the causes of mortality in the army. She used a data story to successfully argue the need for better sanitary conditions and saved the lives of countless soldiers.

The story and outcome are dramatic, but at its heart, this is a data story. It contains data points on time, location, volume, trend, significance and proportion. It uses empathy, and it has a plot and a hero. It ends with a question and some options. Data storytelling was in important in Nightingale’s time — and it matters even more in today’s digital data-abundant world.

“The ways in which organisations deliver business analytics insights are evolving, notably in the rising use of what is called data storytelling,” says James Richardson, senior director analyst at Gartner. “Data and analytics teams have always created dashboards and visualisations, but many are unfamiliar with wrapping those artefacts into a narrative.”

Data stories explore and explain how and why data changes over time, usually through a series of linked visualisations. Although visualisation is almost always a key element in data stories, it is only one piece of a three-part strategy.


Storytelling = visualisation + narrative + context

Self-service BI and analytics platform users now have access to a range of capabilities to help them create compelling data stories. They use an array of data visualisation forms, ranging from chart types to geographic mapping, and more varied and sophisticated charts such as heat maps and candlestick charts.

It is important to note that there is no one visualisation that works for all situations. Data and analytics storytellers must choose a fitting visualisation based on the kind of data they want to present and the audience to which they want to present it. Arranged into a time or conceptual sequence, these visualisations can be shaped into a narrative to help reveal findings, trends or underlying patterns.

“A data story starts out like any other story, with a beginning and a middle,” Richardson says. “However, the end should never be a fixed event, but rather a set of options or questions to trigger an action from the audience. Never forget that the goal of data storytelling is to encourage and energise critical thinking for business decisions.”

A narrative that simply describes data would be of limited use for decision makers. It’s the context around the data that provides value and that’s what will make people listen and engage. Similar to visualisations, the context should be chosen based on the audience.

The sales team may love the story of the gifted salesman who snatched the contract from a competitor with a single well-chosen data point to the prospect’s CEO while in an elevator. However, this will not have the same appeal for the finance team, who wants to hear about predictable outcomes from efficiently executed processes and contract negotiation.


Data stories are about engagement

The audience for the data story is key to getting value when it comes to making a decision based on its findings. They need to be actively engaged, not passive receptors of information, whose task is to explore and question the narrative.

“All human storytellers bring their subjectivity to their narratives. All have bias, and possibly error. Acknowledging and defusing that bias is a vital part of successfully using data stories,” says Richardson. “By debating a data story collaboratively and subjecting it to critical thinking, organisations can get much higher levels of engagement with data and analytics and impact their decision making much more than with reports and dashboards alone.”