That’s our lead designer, James (out on the town in Nashville with one of our talented developers, Jingwei).
James knows data stories. He appreciates that a data story needs to have beautiful, intuitive visualizations and people-first descriptions. But before James worries about all that, he starts by finding the structure of a story. The structure is the narrative flow that will grab a reader’s attention and carry them through the analytics to find valuable, actionable insights.
We’ve been designing data stories, dashboards, and analytical tools for over a decade. In that time, we’ve found a lot of common patterns, regardless of the industry or function. When we teach people about how to design data stories*, we emphasize that the most critical starting point is the Juice’s (patent-pending) three-part framework.
Context: What does my audience need to know or choose to make the story relevant to them?
Heart: How can my audience see and explore the data to reveal insights?
Action: What should my audience do with their newfound knowledge?
* Contact us at info@juiceanalytics.com to find out about our popular Data Storytelling Workshops.
This pattern mirrors a three-act play: the set-up of the situation followed by complications (“messy middle”) and finally the resolution.
I want to show you how this plays out in a thoughtfully-designed data story. Killer Heat Interactive Tool is brough to you by the Union of Concerned Scientists and built by our friends at Graphicacy.
1. The story starts off by setting the context:
2. We immediately get to the heart of the matter: the radical increase in the number of extreme heat days.
3. Finally, the story encourages the reader to take action on what they have learned.
I love this data story for its directness. It has one key question it wants you to answer and makes that question relevant to you locally (context). The answer is displayed in a few key values (heart) -- not a complex collection of trend charts and maps. And then the data story authors ask you to take action on your knowledge.
Not every data story can achieve this level of simplicity. All data stories should follow our three-part recipe.