Is Your Data Story Actually A Story?

How we’re diluting the power of stories, and other paradigms for compelling and coherent communication with data

Joshua Smith
Aug 19, 2019 · 11 min read
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There’s my “not-a-story” disclaimer, in small font and in the sub-subtitle.

Earlier this year my visualization Pasture and Crop won the first feeder for Tableau’s 2019 Iron Viz competition. It scored well across all three categories: design, analysis, and storytelling. As the (biased) author, I agree with the first two but not the third.

I would have given myself a 1 in storytelling.

Now, I understand why I received higher scores in storytelling: based on the given definition for the competition, I met the judges’ expectations for storytelling. I worked on presenting the information with coherence and continuity, with clear organization and a concise “so what” message. I believe I put together a really good expositional piece.

But I didn’t really tell a story, at least not a good one. There aren’t any well-defined characters. There isn’t a plot with impactful events. You can carve out a “beginning,” a “middle,” and an “end,” but those would at best be arbitrary divisions based on logic, not a rise to a climax and a fall to a resolution.

Pasture and Crop describes the predominant types of farmland, and explains the nuances of the relationships between the different types and why it matters. That’s not a story.

My Iron Viz entry is one of many examples of us labeling something a “story” that doesn’t resemble a story at all. “Storytelling” is often used as a blanket statement to describe how well the information is presented in an interpretable presentation with a logical flow.

Note that I’m not saying data visualization can’t tell a story. I’ve seen some visualizations that weave emotional narratives as beautifully as a written short story. Music Memories, an Iron Viz entry by Robert Janezic, literally brought tears to my eyes as I read his mother’s accounts of events both global and personal, all framed in data about her favorite music.

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Robert Janezic’s moving Iron Viz entry on Tableau Public.

In last words, Mike Cisneros provides data on death, paired with touching letters written by the deceased as they wrestled with their eventuality (Mike Cisneros is now part of the Storytelling with Data team).

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Mike Cisneros touching tribute, shared on Tableau Public.

um by Lilach Manheim provides a more abstract example because the character in the story is the narrator. The line graph below, then, serves as a sort of lopsided version of Freytag’s pyramid, with the count of “ums” creating a pattern of rising action, climax, and a slight falling action that reconstructs her presentation at Tableau Conference.

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Lilach Manheim’s counting of “ums” during her conference presentation, on Tableau Public.

So, what qualifies Music Memories, last words, and um as stories, where Pasture and Crop is not? The former contain meaningful characters, whereas mine does not. I describe farmers’ behaviors, in terms of what they plant, what chemicals they use, etc. However, I spend my time zoomed out to the county level, aggregating hundreds or thousands of people and events into a single data point. These “characters” lack names or personalities and, as I’ve described them, autonomy. In contrast, the work by Robert, Mike, and Lilach, present well-defined characters. Individuals with discernible personalities and emotions, who experience and react to relatively climactic events.

In this sense, the difference is strongly related to data humanism. It’s really hard to tell a powerful story in aggregate when all of the humans and all of their lives and moments and emotions are plotted under a single data point, often represented through a behavioral variable, e.g. “sales”, or “likes”. In aggregate, we lose all the parts and pieces that make characters relatable and memorable.

It may seem like this is simply an argument of semantics. The word “story,” especially in the business world, has shifted away from the roots of historical accounts to more casually refer to messaging or collections of facts. In fact, some definitions simply broadly include news and rumors, without references to characters or events at all:

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A quick Google search will show the term has cast a pretty broad net.

Today, “story” and “narrative” are used less to describe the craft and more to reference coherent flows placed within the necessary context to deliver meaning.

I realize that my take here is swimming against larger cultural trends, and perhaps my creative writing background has turned me into too much of a purist. One can make a strong argument that I need to more flexibly accommodate the evolution of language, and I’ll probably agree with that.

But, I’m really not trying to argue about language. I’m trying to speak to the craft. In this sense, I’m not alone: John Schwabish wrote an excellent series of posts on stories and notes:

“…what I’m primarily focusing on here are the line charts, bar charts, area charts, and other charts that we all make every day to better understand our data, conduct our analysis, and share with the world. Even though we often say we’re telling data stories, with those kinds of charts we are not telling stories, but instead making a point or elucidating an argument.”

Especially in the data visualization space, I see patterns that indicate some problems that come from the way we think about stories: first, that we’re diluting the power of stories and setting a low bar that prevents us from recognizing a visualization that actually tells a really powerful story; second, using storytelling as a golden hammer ignores other paradigms that can provide us with powerful techniques that more closely align with our goals.

Diluting the power of stories

Stories are an incredibly powerful rhetorical tool. Stories help us discern chronology through imagination. They present us with characters and events that we can follow, and good stories deliver strong impact through strong emotion. Stories help us understand emotionally through experience, rather than just logically through information.

Cole Nussbaumer, in the seventh chapter of her excellent best seller Storytelling with Data, dives into the magic of stories:

“A good story grabs your attention and takes you on a journey, evoking an emotional response. In the middle of it, you find yourself not wanting to turn away or put it down.”

She then goes on to explore techniques from masters of written word, plays, and more to look for ways we can harness the power of stories to captivate and compel our audiences.

That power becomes diluted when we call everything a story. A well-organized dashboard that presents information in a logical order is more analogous to a well-written essay than a good film or novel. Stories have a logic with continuity and coherence, but logic by itself isn’t a story.

We can see the effect of diluting storytelling in our community: when was the last time a data visualization caused you to emotionally relate to characters that weren’t like you? When was the last time you felt anxiety over a plot heading toward some sort of climax in a data visualization? When was the last you felt compelled to keep reading because you’d become so attached to some characters?

While I can think of a few visualizations that accomplished this for me, the number is low. However, rarely a day goes by that I don’t see someone on Twitter call an interesting, well-designed visualization “incredible storytelling,” or reference someone as a “great storyteller” because of their ability to logically organize information and place it within context.

It’s important to highlight that a visualization isn’t more or less powerful, beautiful, or important because it does or doesn’t tell a story. Therein lies the problem: storytelling is talked about as one of the critical things every data visualization should do. It’s part of how we evaluate our work, baked into many models for feedback or judging (like the Iron Viz). But, sometimes it feels like we’re trying to stick a couple of extra wheels on a motorcycle that actually functions better with two: data visualizations don’t need to tell a story to be good.

When storytelling is diluted, we aren’t challenging ourselves to really harness the rhetorical power of narratives. How do we humanize the data and present characters that our audiences love, hate, love to hate, or hate to love? How do we create emotion by narrating these characters’ movements through impactful events over time? How do we show that our characters’ lives are forever changed after a meaningful resolution, whether happy or tragic?

I’m not claiming to be an expert that has these answers — but I’m challenging the fact that we, as a community of practitioners, aren’t actively looking for them.

We’ve diluted the power of stories so much that we don’t even care if a story actually accomplishes the things stories are known for; we only care if the insights are interesting and presented in a logical flow.

Pretending our hammer is made of gold

The other problem with our use of the term “storytelling” is that, from a rhetoric perspective, we’re using a “golden hammer” — one tool to solve all of our problems. There are other paradigms, each with their own powerful techniques, that we can draw on to deliver different kinds of meaningful impact.

For example, we could look at various types of writing:

  • Descriptions use our senses to “show” us something, often integrating multiple senses for a deeper understanding. Often, the authors draw comparisons with similes and metaphors, in order to allow us to individually anchor our definitions of the subject to our own experiences. Descriptions deliver impact by turning a concept into something that we can see (or hear/touch/taste/smell).
  • Expositions go a step further by interpreting and explaining information. There’s a collection of what’s pertinent, and then a synthesis of what it means and why it matters. Expositions deliver impact by allowing us to understand a subject, not just see it.
  • Persuasions present a specific viewpoint, with the goal of getting a reader to adopt a perspective. Persuasions often include facts to support specific opinions, and often try to drive a specific behavior from the reader. Persuasions deliver impact by driving action.
  • Narratives portray characters experiencing, and reacting to, specific events over time. These events, and the way they impact the characters, are known as the plot. Good stories create emotion with meaningful events that meaningfully change the characters. Narratives deliver an impact by letting us witness how someone experienced a thing. Note: in narrative and literature theory, the story is the chronological collection of events and characters within a setting, and the narrative is the telling of the story.

Note that the above techniques are typically used in combination, although pieces can usually be described as primarily functioning in one of these categories. Narratives need descriptions of characters and events. Persuasions often use stories containing an emotional appeal. Expositions often describe something before explaining it and sometimes use narratives to provide concrete examples.

For example, this article is persuasive, but I’m also describing different types of writing, explaining the trends in data visualization, and I’ll close out with a narrative.

My Pasture and Crop visualization is definitely an exposition — for most of the piece, I display patterns and provide context to explain those patterns. There are elements of description, persuasion, and narrative: to set the stage I have to briefly describe the types of farming and their geographic distributions; my key takeaways are somewhat persuasive, although what specific behaviors I’m trying to change remains ambiguous; and there is a brief, narrative-like moment where I attempt to establish credibility:

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Here I inserted myself, briefly, as a character and took some time to highlight the setting.

However, through the rest of my visualization, characters are ambiguous and the events aren’t established into any sort of a chronological plot. This narrative element is simply a rhetorical technique to support my exposition by establishing a bit of credibility in my knowledge of agriculture.

I would argue that the overwhelming majority of visualizations I see are descriptions or expositions, with the rest mostly falling into the category of persuasions. This isn’t a bad thing; in fact, I would argue this is the right balance. Describing and explaining are two primary goals of analysis. However, these aren’t stories.

Working from the right paradigm helps us work with the right analogous tools. For example, descriptive pieces often emphasize multiple perspectives to give us a more holistic view. For a descriptive visualization, it may be important to show our data from multiple perspectives, whether through multiple visualizations, multiple categorical breakdowns, or multiple metrics. Or, we might compare two different things as “analytical metaphors” to show patterns of similarity to better understand something new.

Expository pieces provide great structures for deeper analysis. We may take the academic approach by stating a thesis, offering supporting evidence, and finishing with a conclusion. Or, we may take a “peeling back the onion” approach where we start with the superficial layer and continue to further dive into the more complex aspects. We could just focus on connecting the dots, explaining the relationship between how our X’s impact our Y.

Persuasive pieces can help us use data to create a compelling perspective. We can emulate good persuasive pieces by proactively addressing counter-arguments. We may use different language to create empathy, or we might include some background about ourself as the author to demonstrate credibility.

These are just some examples of the different techniques unique to those different styles of writing. The way each type of writing is masterfully crafted depends on the style — the tools a writer uses to make a great expository piece are different than the tools they used to craft an exemplary narrative. Likewise, different styles of communication through data visualization can draw on different tools, each powerful in its own right. If we’re just thinking of storytelling, then we’re ignoring other tools that can help us effectively communicate what we’ve found in the data.

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This perspective is in some ways antagonistic to the culture of data visualization as a discipline. I’m swimming against the current here, and not just in data visualization.

But the ways we use — and abuse — the art of storytelling became really apparent to me in a conversation I witnessed among some creative writers. Let me leave you with a final narrative, and feel free to replace “business” with “data visualization.”

One of the writers was describing his struggles with constructing a meaningful plot that would demonstrate character development and was starting to feel like he wasn’t “making the cut” as a storyteller and an author.

“Well, you could just go into business,” another writer joked. “There, you don’t need to worry about characters or plots or creating an emotional impact. All they care about is organizing your thoughts like a high school essay prompt, and they’ll call you a master storyteller.”

Love y’all.

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Thanks to Jason Forrest

Joshua Smith

Written by

amateur poet, amateur forester, amateur farmer, amateur amateur. IronViz Co-Champion, Tableau Zen Master + Public Ambassador. water bender. Love y’all (he/him)


The Journal of the Data Visualization Society

Joshua Smith

Written by

amateur poet, amateur forester, amateur farmer, amateur amateur. IronViz Co-Champion, Tableau Zen Master + Public Ambassador. water bender. Love y’all (he/him)


The Journal of the Data Visualization Society

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