For many years, if you asked people what makes a data story “good,” most people would point to the design of the data visualizations. Today, I believe people are beginning to recognize there is more to data storytelling than just well-designed data charts. As I mention in my book, data storytelling is built on three main pillars: data, narrative, and visuals. The quality of a data story will not be determined solely on the effectiveness of the data visualizations but also on how meaningful data points are weaved into a compelling narrative that inspires action.
If you take an even closer look at a good data story, you’ll discover one distinguishing feature—an insight. Even if a data story contains lots of useful information, it may not be a “good” data story if it doesn’t offer the audience a meaningful insight. Before I explain why an insight is a core part of every good data story, it’s important to clarify what an insight is since the word is often overused and misunderstood.
Recently, I published a Forbes article on the need to clarify what we define as insights. The best definition I’ve found comes from psychologist and author Gary Klein who defined an insight as “an unexpected shift in our understanding.” Insights are more than just interesting observations as they challenge our existing assumptions and shift our perspectives in new directions. If an insight is about something we care about (i.e., related to a personal goal or business priority), it will inspire us to act and introduce change.
The purpose of a data story is to help explain new information to an invested audience. While you can certainly share a variety of information in a story format, your data story will be trivial or boring without a meaningful insight at its core. If you don’t share something substantial with your audience that they don’t already know, they will leave more informed but not enlightened. With data storytelling, you’re not just sharing a discovery you made in the data but also helping your audience make that same discovery.
In a good data story, a main insight serves as the climax, which is the apex of the narrative arc. Without a clear destination to structure your findings around, your narrative will meander along and struggle to reach a meaningful conclusion. However, when you have a compelling insight at the center of your data story, it shapes the rest of the narrative structure as it helps clarify and streamline what context and supporting details are needed. In addition, as people are invited to shift their understanding in unexpected ways, the insights will naturally inform the recommended solutions and next steps.
Recently, I came across a February 2021 journal article by Sorin Adam Matei and Lucas Hunter titled, Data storytelling is not storytelling with data: A framework for storytelling in science communication and data journalism. The Purdue University authors highlight how the best stories employ surprise to generate suspense and engagement. As an example, if a dog bites a mail carrier, it’s not a story because “nobody cares about a predictable chain-of-effects.” However, you have a potential story if the mailman bites the dog because you're offering your audience something that is unique and intriguing—a twist.
In the context of a data story, an insight serves as the source of the main surprise. It invites the audience “to replace well-established explanations that are considered facts with new, unexpected ones.” An insight represents a new causal relationship such as “an unexpected cause to a known effect or a known cause with an unexpected effect.” As Matei and Hunter note, “adding a bit of data to a narrative” won’t convert something magically into a data story, the insight is a critical ingredient.
In addition, the placement of the main insight in a data story is also crucial. Many business communication experts advocate for key insights to be featured upfront in an executive summary. However, while this top-down pyramid approach is more efficient for reporting purposes, it is not effective for storytelling and negates the emotive power. By disclosing everything at the very beginning, you remove the suspense that’s essential to telling an engaging data story. It’s important to recognize which method—reporting or storytelling—is most appropriate for each scenario and your audience's needs.
Matei and Hunter view science as having a “natural advantage over conventional storytelling because it consistently provides new, unexpected explanations.” I would broaden their viewpoint to all forms of business analysis, not just scientific use cases. Data scientists, analysts, and data-savvy business professionals have an opportunity to “feed the curiosity of the audience by revealing something the audience does not yet know.” Whether your data story is about a scientific discovery or a business problem/opportunity, a good data story will always “induce a change in the audience through learning.” For example, in a business setting, data stories can help audiences learn more about current business practices and processes as well as customer behaviors and preferences.
Without insights and only information, data stories will have a muted effect on people and organizations. As Matei and Hunter point out “a narrative in which an expected cause is connected to a known fact is a trivial story, neither a good story nor one worthy of telling.” However, a data story that features an insight is worth telling as it challenges organizations to think and act differently. In my view, an effective data story has the power to drive positive change, and it can’t do so unless it has a compelling insight at its core.
Effective Data Storytelling teaches you how to communicate insights that influence decisions, inspire action, and drive change.