For people who want to tell stories with data, narrative can be somewhat of an enigma. We know it when we see or hear it, but we’re not always sure how to create it ourselves. Whenever I do a poll on which of the key elements of data storytelling is the most difficult, the narrative aspect consistently receives the majority of the votes. If you search for help on this topic, most of the information you’ll find is vague, confusing, or misguided. Without clear guidance on how to craft a proper narrative for your data, you’ll continue to wonder why your data stories don’t perform as well as you expected.
With many people seeing data storytelling as an offshoot of data visualization, it has led to an overemphasis on the visual aspects and an underemphasis on the narrative elements. Today, some individuals will only evaluate data stories by how well-designed the data visualizations are. However, with time, people are discovering that eye-appealing data charts don’t always translate into compelling data stories. Without a strong narrative, your data story will lack structure, inspiration, and purpose.
If you want to become a better data storyteller, it’s not about learning more data visualization techniques or studying more advanced visualization tools. Understanding how a narrative complements your insights is the key to creating better data stories. Fortunately, humans have been telling stories for thousands of years; therefore, we can borrow many lessons from traditional storytelling. While there are many different narrative-related elements such as characters, conflict, setting, theme, point of view, and so on, the single most crucial factor in crafting compelling data stories is the narrative structure.
When I first started exploring the topic of data storytelling more than a decade ago, I set out to find a narrative model that would be useful in formulating data stories. I discovered there were several different narrative models, most of which were geared towards fiction writing. While some slight adjustments would be necessary for fact-based stories, I was confident the narrative structure used in fictional storytelling, such as novels or films, could be adapted to data stories.
When you examine the different narrative models, there are five common attributes:
In evaluating different narrative models for data storytelling, I wanted to balance simplicity with utility. If the model was too complex, it could be too overwhelming for people or impractical with real-world data scenarios. On the other hand, if the model was too simple, it wouldn’t provide adequate guidance on how to showcase insights, so they resonate deeply, and not just superficially, with audiences. I narrowed down my list to the following three narrative models:
This popular model divides a story into three acts: setup, confrontation, and resolution. Its approach is often summarized as having a beginning, middle, and end. While this model is often attributed to Aristotle, he only identified two acts in his study of Greek tragedies (a complication and a dénouement/unraveling). Instead, screenwriting expert Syd Field is credited with popularizing this model in the late 1970s.
Assessment: This narrative model is too simple and lacks enough pragmatic direction to help people consistently craft good data stories. The suggestion that stories have “a beginning, middle, and end” just isn’t that useful. This simplification doesn’t do justice to narrative structure's crucial role in stories. For example, many business reports have all three components (beginning, middle, and end) but are far from resembling engaging stories.
Mythologist Joseph Campbell developed this well-known model, outlining several stages a hero experiences on an adventure. Campbell divided his archetype into 17 stages with three main sections: departure, initiation, and return. Screenwriting expert Christopher Vogler further consolidated the model into 12 stages to make it slightly less onerous.
Assessment: The detailed nature of this narrative model is a helpful template for screenwriters and novelists working with creative content. However, it is less practical for business use cases that don’t align as easily with its multifaceted approach. In addition, the heavy emphasis on a central character won’t be as applicable to most data stories.
German playwright Gustav Freytag introduced this familiar model, and it divides a story into five main parts: exposition/introduction, rising action, climax, falling action, and dénouement/resolution. The rising and falling action sections create a triangle that looks like a pyramid. In his study of Shakespearean plays and Greek tragedies, Freytag was among the first to notice the importance of an inciting incident or Hook that sets the story in motion (which he called the “exciting force”).
Assessment: This model possessed a good balance of simplicity and utility. It doesn’t have too many components, where it becomes overbearing or impractical. It also has enough detail to help define what data stories should look like.
After I found the Three-Act Structure was too simple and the Hero’s Journey was too complex, Freytag’s Pyramid became the foundation of my narrative model for data storytelling. Rather than having five parts like Freytag’s Pyramid, I condensed the Data Storytelling Arc™ down to the following four sections:
Like a traditional story, you will set up your data story by providing background information. For example, you’ll want to establish key details such as the area of focus and timeframe. While you won’t necessarily have actual characters, your data story will be based on data from people that your audience cares about—customers, employees, investors, partners, etc.
For the Setting, you should aim to provide “just enough” context to set up the rest of the story. A common mistake is spending excessive time summarizing the analysis process, which is rarely necessary and could sidetrack your storytelling with irrelevant minutia.
The Hook is a notable observation that reveals a potential problem or opportunity. It will capture your audience’s attention and pique their interest in hearing the rest of the story. Sometimes, your audience may have given you the Hook when they asked you to investigate something unusual in the data.
After establishing your Hook, you reveal what you discovered while analyzing the problem or opportunity. Rather than providing a loose collection of random facts (data dump), you share supporting details in a focused manner. This section aims to help your audience understand what’s happening and build up to the climax of your data story. The content will be a combination of analysis findings and helpful context. The number of rising points you include will depend on the complexity of the topic.
Note: I’ve referred to this section for many years as “rising insights.” Over time, my thinking has evolved. To avoid diluting the word “insight,” I’ve decided to switch to “rising points” instead.
The climax of your data story is where you share your analysis’s main finding or central insight. The Aha Moment combines an insight (an unexpected shift in your understanding of something) with an explanation of why your audience should care about it (the ‘so what’). Unlike a traditional story where the theme may be implied or open to interpretation, the Aha Moment represents a clear and explicit theme for the data story. An effective Aha Moment will motivate an audience to address a problem or pursue an opportunity.
After enlightening your audience with your Aha Moment, they may need your help determining what they should do next. Without a clear understanding of potential courses of action, your audience can become paralyzed by the information you’ve shared. Providing options and making a recommendation prepares your audience to decide or discuss what action(s) to take.
In my Data Storytelling Arc™ diagram, you’ll notice that the arrow on the right side exits the arc at a higher position than it started. It represents my belief that each data story is an opportunity for the audience to elevate their domain knowledge and learn more about customers, processes, competitors, industry, etc. In addition, with this framework, they will also be better equipped to act on the insight with a greater sense of urgency.
Screenwriting expert Robert McKee said, “Storytelling is the most powerful way to put ideas into the world today.” When sharing insights, there isn’t a more effective method than data storytelling. Mastering narrative structure lets you organize your analysis findings into meaningful flows that resonate with your audiences. Having the thread of narrative weaved through your observations and insights may be the missing link that your data stories needed to be more engaging, persuasive, and memorable.
Data storytelling requires a thoughtful and pragmatic approach. If your organization wants to improve its mastery of narrative or data storytelling in general, please get in touch with us about a customized virtual or onsite workshop. If you want to learn how to craft more compelling data stories, please consider picking up a copy of my book, Effective Data Storytelling.
Effective Data Storytelling teaches you how to communicate insights that influence decisions, inspire action, and drive change.