You may have heard that 80 or 90% of the world's data has been generated in the last two years. Well, guess what?
Not only is that statistic correct, it has been correct for decades. We are approaching a moment in history when the term "big data" is actually an incomprehensible amount of data. An amount too big for mere humans to digest.
Mere data is absolutely useless. Let that sink in. Without a story, without insight into the meaning behind the data, it is worthless. But find the story in the data and tell that narrative to your audience, and suddenly that data is worth its weight in gold (if data had weight).
Enter the Data Storyteller
The storyteller who can transform data into a concrete story with a solid and intriguing narrative will consistently win over readers.
Because with that double whammy of a great story (all humans crave story) mixed with data analysis that readers can really sink their teeth into, you have a story with substance.
Analysts as Storytellers / Storytellers as Analysts
Many have made the astute point that analysts are not usually naturally good storytellers. And many storytellers are a bit gun-shy of big data. So how can we marry the two skills and win over our readers?
Visualize Your Data
The best advice to quickly and effectively tell a story with data is to use visualization. Being able to synthesize the essence of your data into a graph creates an immediate story, something for your reader to grab onto and make meaning out of.
Cole Nussbaumer Knaflic is a data storyteller who teaches how to use visuals to tell a story. Her bestselling book suggests you should use storytelling to make your message resonate with your audience.
By getting to the heart of what you want your readers to know, cutting out (editing) the stuff that doesn't drive that narrative forward, and making sure you understand the context and the importance of that context in your story, you'll be able to lead your reader through the data in a way that will enlighten rather than befuddle. With graphics and other visuals, the story is supported, unfolding as it should to inform and delight.
Nathan Yau was a doctoral student in statistics at UCLA when he started Flowing Data. He posts visually stunning graphs representing data in ways that tell the entire story through their often interactive colours.
Check out "How You Will Die" for example. By playing around with this data and reading Nathan Yau's commentary, you can get a sense for how the data has been compiled to tell a story; and a story that you will have a difficult time pulling yourself away from.
Editing is Important
Cole Nussbaumer mentions culling the data as essential to storytelling. Putting on your editor's hat and sifting through your data (using available tools) creates the space you need to tell an actual story, rather than simply presenting a series of numbers to your readers and hoping they'll find the thread and patterns.
Chris Twigg notes the importance of standard, traditional narrative devices in data storytelling, and I would go so far as to say that editing, culling through your data to find the essence of the story is perhaps the most important of these tools.
Zach Gemignani asks if data storytelling is more storytelling or story finding. I would say that the first and most important step is story finding. If you stop there, you're still ahead of the pack. But storytelling goes one (or more) steps further and completely wins over your readers.
Finding the Story, Then Telling It
You'll ultimately win over your readers if you use the tools available to you to find, then strategically and beautifully tell the story your data wants you to tell.
About the Author:
Wendy Kelly is a content strategist living and skiing in a small mountain town in British Columbia who enjoys storytelling and strategy. Imagine that. You can follow her at @WendyKKelly.