From an early age, I developed a love for reading and collecting comic books. As a young reader, I remember being curled up in a bean bag chair and devouring the adventures of Lucky Luke and Asterix by Morris and René Goscinny. As a teenager, I loved following the tales of Spider-Man, the X-Men, Wolverine, and Batman in Marvel and DC comics. As an adult, I have enjoyed the recent wave of superhero films (mostly) and have once again returned to collecting comic books.

As I’ve thought about my two passions for data storytelling and comic books, I have discovered there are many interesting parallels between them. When compared to other traditional forms of storytelling such as television, films, or novels, data storytelling has much more in common with comic books than any other medium.

While the other forms of storytelling rely more heavily on either visuals (TV/films) or written words (novels), data stories are more similar to comic books as they draw on both visual and text elements to tell their tales. While you can tell data stories with just words, everyone would agree the visuals enrich the storytelling experience. When you combine them in a skilled manner, you have a data communication that is engaging, memorable, and persuasive.

With data storytelling being a relatively new genre, we can learn several lessons from the world of comic books, which has been telling engaging stories with visuals and text since the end of the nineteenth century. Here are five key observations from comic books that I know can help you improve your storytelling:

1. Storyboarding

After developing a script, a comic book is roughly sketched out in its entirety before any ink, color, or text are added. This storyboarding approach ensures that the storyline and visuals are cohesive before finalizing the panels within the pages. By sketching the panels, the creative team can iterate until they’ve determined what visual approach works best for the story.

In data storytelling, it’s equally important to decide what your narrative flow will be first, so you don’t waste time building and refining data charts that aren’t needed. In a data story context, I’ve found it helpful to write your key points on sticky notes and evaluate their position within a narrative framework like my Data Storytelling Arc™. Before you spend anytime polishing your visuals, you’ll want to verify you’ve chosen the right charts to convey the key points of your story. At this stage, it’s about ensuring the narrative is properly crafted and organized before refining each scene.

2. Panel structure

One of the hallmark characteristics of comic books is their grid layout of four to seven panels per page. Each of these panels is designed to have a single focal point, and they are designed to be read in sequential order. For data storytelling, the grid layout is less important than the sequential flow of the visual storytelling—panel to panel. A poorly formed or misplaced panel can quickly disrupt the entire story as readers become confused when the panels don’t progress from one to another.

Just like comic books, data stories must ensure each data scene serves a purpose and has a focal point. They must advance the narrative in a sequential manner. A data story can’t be just a loose collection of interesting data points delivered in a random order. When you arrange your data scenes, they should be connected and lead the audience down a pathway to a conclusion, focusing on inspiring a decision or action.

3. Panel composition

The composition of each panel is designed to guide the reader’s eyes through the visual elements in a deliberate manner. Artists place characters and objects in the foreground, middle ground, and background. They also show the scenes from different angles or perspectives to enhance the visual storytelling. Once the right imagery is established, a letterer arranges the word bubbles and captions in a way that is aligned with how readers naturally consume content. Artists also balance the use of text and imagery so that neither overpowers the other.

Within each data scene of a data story, you need to determine what will be highlighted in the foreground (bold color) and what will be pushed to the background (grayscale). As the data storyteller, you need to decide what slice of the data will be most compelling for your message. In addition, it’s helpful to determine how the audience will navigate each scene. The placement of your annotations must be aligned with how people will naturally consume your content. You must also be aware of how much text is being shared in your data story, especially when your intent is to present the content and not have it read as a document.

4. Story pacing

If you’re a fan of comic books, you may be familiar with the use of compression and decompression to control the pacing of the story. A comic book doesn’t cover every facet of the entire narrative. Instead, it provides meaningful glimpses into the most important and entertaining parts. Creative writing expert Nick Macari describes compression pacing in comic books as “taking ten pounds of story and stuffing it into a one pound container.”

In Japanese (Manga) comic books and longer form graphic novels, you see the opposite approach of decompression pacing. A decompressed approach spreads a key moment in the story across multiple, similar panels with few accompanying words. This approach adds more weight to small but significant moments in the story to enhance the overall emotion and tension.

In data stories, you want to apply the right level of pacing needed for your story. You’re not going to cover all facets of the analysis that shaped your story. In fact, you have to leave out many extraneous details and focus your story on the key aspects and main takeaways. However, at times, you may want to use a decompression approach to slow down and unpack an essential part of your story that is crucial to your audience’s understanding. For example, you may spend more time on a key chart or a specific customer example that is unfamiliar or difficult for your audience to process.

5. Character focus

Up until the 1960s, comic books had focused primarily on superheroes who were extraterrestrials, gods, or billionaires. In 1962, Marvel writer Stan Lee introduced a different kind of hero. Peter Parker, a New York City teenager, gained his powers by being accidentally bitten by a radioactive spider, becoming the Amazing Spider-Man. But unlike previous superheroes Parker also struggled with familiar life challenges such as adolescence, finances, relationships, and personal trauma. Spider-Man became a relatable hero.

As Marvel’s friendly neighborhood hero connected with readers on an emotional level, it made his adventures both engaging and memorable. It’s not a coincidence this web-slinging hero has become the most popular superhero of all time, selling more than 360 million comic books.

Data stories can and should feature characters just like comic books do. However, one misconception is that the data is the “hero” of these fact-based narratives. While data forms the foundation of every data story, the statistics are not the heroes. The heroes and characters of data stories are the people behind the numbers—customers, employees, partners, etc.—that your audience cares about. For example, if your data shows customers are unhappy, your audience must feel their disappointment not just see it in a chart. It’s the only way they will be inspired to help them.

It’s important to humanize the numbers by highlighting the impacts to real people. When you bring out the human element, your data stories will connect with audiences on an emotional level, inspiring more urgency and action.

Whether you’re a comic book fan or not, there are unique parallels between comic books and data stories. While their approaches are different from the perspectives of visuals (graphic art vs. data visualizations) and content (fiction vs. non-fiction), both comic books and data stories rely on visuals and text to convey their narratives in a static, sequential manner. They are both designed to take audiences on immersive, enlightening journeys—one with the purpose of entertaining and the other with the purpose of explaining and educating.

When I was younger, I dreamed for a time of becoming a comic book artist. While my business career has led me down a different path, I am encouraged that visual storytelling is still a part of my focus. While I might not be crafting the amazing superhero adventures I envisioned in my youth, I am helping people communicate their insights more effectively, which helps them become revenue-generating and cost-saving “heroes” within their organizations. By highlighting some of the key similarities between these two related communication genres, I hope the comic book pratctices that I’ve highlighted inspires data storytellers—seasoned and aspiring—to create more engaging data narratives for their audiences.

Note: If you're interested in learning more about comic books and visual storytelling, you'll want to check out two books: Scott McCloud's Understanding Comics and Nick Sousanis's Unflattening.

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