Fiction #3: Data stories have a beginning, middle and end.
In this case, the fictional aspect is not that this statement is incorrect but that this piece of advice is actually useful to would-be data storytellers. The idea that stories are structured this way came from the Greek philosopher Aristotle. However, a textbook has a beginning, middle and end—so too does a financial report or a phone book—yet none of them would be considered stories. In terms of the dramatic structure of a data story, I find Freytag’s pyramid more useful with its introduction (exposition), inciting incident, rising action, climax, resolution and conclusion (denouement). Establishing the setting and characters at the beginning of a data story is important as well as highlighting how something has changed in the environment that introduces a new problem or opportunity (inciting incident). Leading your audience to the main insight (climax) and then to recommendations / next steps (resolution) is a more helpful structure for data stories than Aristotle’s model.
Fiction #4: Data are the characters of your story.
I often see some confusion about how the different narrative elements are represented in a data story. A common mistake centers around data being perceived as the characters or heroes of a data story. While you might be enamored with a particular insight, people—not data—should be the heroes of your data story. Most business data is either directly or indirectly related to people— your customers, prospects, employees, partners, investors and so on. An audience is going to care about what happens to different groups of people. For example, sales is going to be worried about prospects, and management is going to be focused on customers. So while insights can shape and influence the plot turns of a data story, humans—not numbers—should be at the center of the story (especially if your audience are actually the characters of your story). Adding a human face to your data story will help it to resonate more strongly with your audience.
Fiction #5: Data stories start with a hypothesis or question.
How you start a data story is critical to its success. Just because your analysis started with a hypothesis or question doesn’t mean your data story should begin that way too. Starting with a hypothesis or question places too much emphasis on the analysis journey (your approach to analyzing the data) rather than the insights you uncovered. Just like an author needs to establish the story’s setting (e.g., a galaxy far, far away), a data storyteller needs to provide ample background information to help orientate the audience. The beginning of your data story should focus on providing sufficient context (time period, data source, previous trends, etc.) so your audience can properly understand the insights you’re going to share. It’s also an opportunity to introduce the main character(s) that will bring the story to life for your audience.
Fiction #6: Data storytelling can be automated.
I’ve seen various vendors tout the power of natural language generation (NLG) as a means of providing automated data storytelling. Rather than relying on “confusing” data visualizations, they feel descriptive text about the data is somehow better and easier for people to follow. In a world where humans have a lower attention span than goldfish, text is never going to beat images. Furthermore, the main advantage of having text is not to describe the data but to explain it. I should be able to see revenue increased 72% from Q3 to Q4 in a well-designed chart without descriptive text telling me the same thing. If that’s not the case, fix the chart instead of adding redundant text. However, knowing the 72% increase was attributed to the success of a new marketing campaign would be valuable context. Text can add value if it’s explanatory, not descriptive. Without adequate context and domain expertise, it’s going to be difficult for machines to “automate” data storytelling.
With so many important data stories to tell, it’s our duty and privilege to ensure the insights we discover are properly shared, understood, and acted on. Misconceptions about data storytelling can inadvertently weaken and interfere with the message you’re trying to convey through a data story. It’s often our fault—not the data’s—when an insight fails to resonate with an audience and ends up being ignored.
William Playfair, the 18th-century Scottish inventor of many modern-day charts noted, “No study is less alluring or more dry and tedious than statistics, unless the mind and imagination are set to work.” Data storytelling has the power to open people’s minds to new possibilities and ideas they would have never imagined. As data is democratized throughout more and more organizations, it is no longer left to the back alleys and side streets of your IT department or the gated community of the executive boardroom. Data is increasingly parading down the main street of your business for all eyes to see, and how you choose to craft stories from this valuable resource will be essential to your success. Don’t let the misconceptions I’ve highlighted water down or ruin the power and effectiveness of your next data story.
**This article was originally published on Forbes.com on July 13, 2016.