When Florence Nightingale analyzed mortality rates from the Crimean War, she realized that the majority of soldiers hadn’t died in combat, but instead from preventable diseases caused by poor sanitary conditions in the hospitals.
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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.
“ Data stories explore and explain how and why data changes over time, usually through a series of linked visualizations”
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 organizations 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 visualizations, but many are unfamiliar with wrapping those artifacts into a narrative.”
Data stories explore and explain how and why data changes over time, usually through a series of linked visualizations. Although visualization is almost always a key element in data stories, it is only one piece of a three-part strategy.
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Storytelling = visualization + 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 visualization 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 visualization that works for all situations. Data and analytics storytellers must choose a fitting visualization 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 visualizations can be shaped into a narrative to help reveal findings, trends or underlying patterns.
“ It’s the context around the data that provides value and that’s what will make people listen and engage”
“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 energize critical thinking for business decisions.”