Is your data failing to persuade your audience despite its accuracy? Why do some presentations move people to action while others leave them unmoved, even when the numbers are solid?
Transforming raw data into compelling narratives requires specific techniques and thoughtful planning. In her book Storytelling With Data, Cole Nussbaumer Knaflic breaks down how to tell a story with data into three essential steps.
Continue reading to discover how you can translate your numbers into stories that stick with your audience and inspire meaningful action.
How to Tell a Story With Data
You’ve probably heard the phrase, “The numbers speak for themselves.” But, do they really? Raw data rarely tells a compelling story or changes anybody’s mind on its own—even when it’s beautifully visualized. This is where the art of storytelling comes in. As Knaflic explains, once you’ve interpreted your data, your next challenge is to transform those insights into a narrative that resonates with your audience. It’s like being a translator: You’re converting the language of numbers into the language of human experience and meaning.
Knaflic explains how to tell a story with data, breaking it down into three steps: distilling your data to its essence, choosing the right narrative structure, and borrowing proven techniques from traditional storytelling. Let’s explore each of these in detail.
| Storytelling and the Brain Storytelling is such a fundamental human tendency that even science amounts to storytelling. Our brains are wired to construct narratives to make sense of the world around us, and they do so to puzzle through complex concepts that cannot be fully explained by physical facts alone. This storytelling tendency arises from the brain’s pattern recognition system, which tries to find meaningful connections between events and rewards us with the release of dopamine (a neurotransmitter associated with pleasure) when we perceive a coherent pattern. While science deals with objective data, it involves constructing hypotheses and thought experiments, which are stories we must test and refine to understand our world. Researchers have found that stories make information uniquely engaging and memorable on a neurological level. According to neuroscientist Paul Zak, compelling narratives engage more regions of the brain, making the information more impactful, compared to just stating facts. Stories that create tension and allow us to emotionally connect with characters capture our attention and evoke empathy. The brain also releases oxytocin, a neurochemical linked to trust and cooperation, when we become immersed in stories, motivating us to help others—or to work together toward a shared goal, like acting on the information data stories tell us. |
Distill the Data Down to Its Most Basic Form
Even the most interested listeners can only absorb so much information, which is why Knaflic emphasizes the importance of reducing complex information to its essential elements. The principle is simple but powerful: Before you can tell an effective story with your data, you need to identify the handful of key points that really matter. Knaflic offers two frameworks for this process of distilling your data down to its most fundamental form:
- The “3-minute story”: A concise narrative that you can tell in a short amount of time. This forces you to focus on what’s truly important and eliminate everything else.
- The “Big Idea”: A single, compelling sentence that captures your core message. Think of it as the headline you want your audience to remember.
You can use a three-minute story or a big idea to communicate your message succinctly. For example, imagine you’ve analyzed a year’s worth of customer feedback data for a subscription service. Instead of presenting every detail, you might distill it down to this Big Idea: “Our response time to customer complaints directly predicts their likelihood to renew.” Your 3-minute story would then focus on the key data points that support this conclusion, leaving out the non-essential details. By starting with this distillation process, you can then build your visualizations around the points that matter most, making your critical insights clear.
Choose a Narrative Structure
The second skill you need to turn the numbers into a story is choosing a narrative structure. Every great story needs a structure, but not every audience needs to go on the same journey. Some audiences will need you to talk through your process from start to finish, while other audiences just want to know what you recommend, based on your research. Knaflic explains that the narrative structure you choose should match your audience’s needs and their relationship with you. She identifies two primary approaches to structuring your data story:
The Chronological Approach
A chronological approach to your data walks your audience through your analytical process step-by-step. You might start by explaining the question you needed to answer, then go on to how you gathered your evidence, what you discovered, and finally what it all means. Knaflic explains that this structure works best when you’re building credibility with a new audience, your methodology matters as much as your findings, or your audience needs to understand your reasoning to trust your conclusions.
(Shortform note: The chronological approach Knaflic recommends mirrors what literary scholars have discovered about successful storytelling through AI analysis of narrative structures. Many acclaimed stories follow a “journey” or “quest” archetype, where protagonists move step-by-step through a process of discovery, challenge, and resolution—much like how analysts progress through data collection, analysis, and insights. Just as readers care more about a hero when they witness their full journey, audiences have more confidence in data conclusions when they can understand the analytical journey that produced them. This might explain why Knaflic finds the chronological approach particularly effective when methodology needs to be explained.)
Leading With the Call to Action
Leading with the action you want your audience to take puts your recommendations front and center. You might begin by explaining what your audience needs to do, then explain why (using data to support your argument), and then explain how the audience can make it happen. Knaflic explains that this structure works best when you have an established relationship with your audience, your audience is primarily interested in outcomes, or time is limited and decisions need to be made quickly.
(Shortform note: Writing an effective “call to action” is a strategy many marketing experts rely on. They’ve found that an effective call to action not only leads with the desired action or outcome, but also creates a sense of urgency to motivate your audience to act quickly. To create a sense of urgency, highlight the benefits of the action to show how it will add value or solve a problem, and use clear, concise language with actionable verbs. You may also find it helpful to test different variations of your call to action, so you can refine it over time.)
To choose the right narrative structure, think about who your audience is. For example, imagine you’re analyzing customer churn data. With your data science team, you might use the chronological approach to explain your machine learning model’s methodology and findings. But with your CEO, you might lead with a call to action, like, “We need to invest in customer service training to reduce our 25% churn rate” and then back it up with supporting data. Whichever structure you choose, remember that your visualizations shouldn’t stand alone. Always provide clear context that connects your data to your audience’s needs and decisions.
(Shortform note: Data analyst Brent Dykes (Effective Data Storytelling) explains that when you put the insights from your data into context, you not only help your audience better understand the numbers but also put your interpretation through a valuable vetting process that can help you find any flaws. Dykes recommends six ways to provide a frame of reference for your data: comparisons that highlight similarities or differences, historical trends to show improvements or declines, analysis scaling up or down the impact to a shorter timeframe to convey significance, background information on factors that influenced the results, examples that make your data more relatable, and validation of surprising or anomalous details.)
Borrow Techniques From Other Storytellers
Data analysts aren’t the only people who need to tell compelling stories. Novelists, journalists, and filmmakers have been perfecting the art of storytelling for centuries. Knaflic suggests that we can learn from their tried-and-true techniques to make our data stories more engaging and memorable. She points out three powerful storytelling techniques that work just as well with data as they do with drama.
Repetition
Remember how fairy tales often repeat key phrases three times? Repetition isn’t just effective in stories intended for children: It’s a proven memory technique that works for data stories too. Knaflic explains that in the course of presenting your story, you can:
- Tell your audience what you’re going to tell them
- Relate the point you’re trying to get across
- Remind them of what you told them
For example, in a quarterly sales presentation, you might start with an idea like, “We need to expand our West Coast operations.” Then, you could support this recommendation with a detailed market analysis. Finally, you could conclude by reiterating the West Coast expansion recommendation and summing up all of the evidence you gave in favor of this course of action.
A Logical Framework
Just as every good movie has a clear structure, your data story needs a logical framework. Knaflic recommends thinking about both horizontal and vertical logic:
- Horizontal logic: how your story flows from one point to the next (like scenes in a movie)
- Vertical logic: how each individual element (like a chart or slide) is organized to guide understanding
To check your story’s flow, Knaflic recommends going through your presentation and writing down the main point of each slide on a sticky note. Then arrange these notes to see if your story flows logically, just like a filmmaker would do when storyboarding a movie. This exercise can help you spot any flaws in the logical framework of your story and figure out how to make adjustments.
| From Storyboard to Story: Lessons from Wes Anderson’s Films Director Wes Anderson’s meticulous storyboarding process demonstrates how repetition and logical frameworks work together in storytelling. Before filming The Grand Budapest Hotel, Anderson created detailed animated storyboards that he narrated himself, allowing him to test and refine both the narrative flow (the “horizontal logic”) and the composition of individual scenes (the “vertical logic”). This iterative process—moving from rough sketches to animated storyboards to final film—creates opportunities to see how the film can best reinforce key story points. Anderson even uses repetition, in much the same way that Knaflic recommends, within scenes: When introducing important information, he often presents it multiple ways (like showing both a character reading a letter and the letter itself). His storyboards also demonstrate how a clear logical framework helps audiences follow complex narratives: Each scene flows naturally to the next while maintaining its own internal coherence. |
A Second Set of Eyes
Next, Knaflic recommends getting feedback from someone unfamiliar with your data and the story you’re trying to tell with it. She explains that getting a new perspective on your presentation can reveal:
- Where your story loses momentum
- Which points need more explanation
- What might confuse your audience
(Shortform note: Even if you don’t have another person to give you a second set of eyes on your story, as Knaflic recommends, you can still get the benefits of a fresh perspective. Psychologists have discovered the “vicarious construal effect,” where imagining an experience from a new perspective helps us see something as if it were new to us. By adopting the viewpoint of someone encountering your data for the first time, you can experience the novelty they feel and see your story as if for the first time. Researchers find that trying to see something through another person’s perspective can even help us appreciate things more—a valuable strategy to fall back on when you’ve been working with the same data for a long time.)
