Storytelling with Data: A Data Visualization Guide for Business Professionals

by Cole Nussbaumer Knaflic

Understand the context Choose an appropriate visual display Eliminate clutter Focus attention where you want it Think like a designer Tell a story

Six key lessons: 1. Understand the context 2. Choose an appropriate visual display 3. Eliminate clutter 4. Focus attention where you want it 5. Think like a designer 6. Tell a story

Before you start down the path of data visualization, there are a couple of questions that you should be able to concisely answer: Who is your audience? What do you need them to know or do? This chapter describes the importance of understanding the situational context, including the audience, communication mechanism, and desired tone.

What is the best way to show the data you want to communicate?

Picture a blank page or a blank screen: every single element you add to that page or screen takes up cognitive load on the part of your audience.

When it comes to the form and function of our data visualizations, we first want to think about what it is we want our audience to be able to do with the data (function) and create a visualization (form) that will allow for this with ease.

Stories resonate and stick with us in ways that data alone cannot.

A story has a clear beginning, middle, and end; we discuss how this framework applies to and can be used when constructing business presentations.

Before we get into the specifics of context, there is one important distinction to draw, between exploratory and explanatory analysis. Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others. When we do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell—probably about those two pearls.

Never share exploratory analysis unless you’re submitting to a research journal.

After undertaking an entire analysis, it can be tempting to want to show your audience everything, as evidence of all of the work you did and the robustness of the analysis. Resist this urge. You are making your audience reopen all of the oysters! Concentrate on the pearls, the information your audience needs to know.

When it comes to explanatory analysis, there are a few things to think about and be extremely clear on before visualizing any data or creating content. First, To whom are you communicating? It is important to have a good understanding of who your audience is and how they perceive you. This can help you to identify common ground that will help you ensure they hear your message. Second, What do you want your audience to know or do? You should be clear how you want your audience to act and take into account how you will communicate to them and the overall tone that you want to set for your communication. It’s only after you can concisely answer these first two questions that you’re ready to move forward with the third: How can you use data to help make your point?

Avoid general audiences, such as “internal and external stakeholders” or “anyone who might be interested”—by trying to communicate to too many different people with disparate needs at once, you put yourself in a position where you can’t communicate to any one of them as effectively as you could if you narrowed your target audience.

Identifying the decision maker is one way of narrowing your audience.

You should always want your audience to know or do something. If you can’t concisely articulate that, you should revisit whether you need to communicate in the first place.

In general, those communicating with data need to take a more confident stance when it comes to making specific observations and recommendations based on their analysis.

This is a concept that Nancy Duarte discusses in her book, Resonate (2010). She says the Big Idea has three components: It must articulate your unique point of view; It must convey what’s at stake; and It must be a complete sentence.

Storyboarding is perhaps the single most important thing you can do up front to ensure the communication you craft is on point. The storyboard establishes a structure for your communication. It is a visual outline of the content you plan to create.

When you have just a number or two that you want to communicate: use the numbers directly.

Using a table in a live presentation is rarely a good idea. As your audience reads it, you lose their ears and attention to make your point verbally.

Use table data in presentations if you want your audience fo ignore you.

Borders should be used to improve the legibility of your table. Think about pushing them to the background by making them grey, or getting rid of them altogether. The data should be what stands out, not the borders.

While tables interact with our verbal system, graphs interact with our visual system, which is faster at processing information.

Line graphs are most commonly used to plot continuous data. Because the points are physically connected via the line, it implies a connection between the points that may not make sense for categorical data (a set of data that is sorted or divided into different categories).

Slopegraphs can be useful when you have two time periods or points of comparison and want to quickly show relative increases and decreases or differences across various categories between the two data points.

Sometimes bar charts are avoided because they are common. This is a mistake. Rather, bar charts should be leveraged because they are common, as this means less of a learning curve for your audience.

If I had to pick a single go-to graph for categorical data, it would be the horizontal bar chart, which flips the vertical version on its side. Why? Because it is extremely easy to read. The horizontal bar chart is especially useful if your category names are long, as the text is written from left to right, as most audiences read, making your graph legible for your audience. Also, because of the way we typically process information—starting at top left and making z’s with our eyes across the screen or page—the structure of the horizontal bar chart is such that our eyes hit the category names before the actual data. This means by the time we get to the data, we already know what it represents (instead of the darting back and forth our eyes do between the data and category names with vertical bar charts).

Horizontal bar graph = Go to graph for categorical data.

avoid: pie charts, donut charts, 3D, and secondary y-axes.

If you find yourself using a pie chart, pause and ask yourself: why? If you’re able to answer this question, you’ve probably put enough thought into it to use the pie chart, but it certainly shouldn’t be the first type of graph that you reach for, given some of the difficulties in visual interpretation we’ve discussed here.

F* pie charts in particular.

One of the golden rules of data visualization goes like this: never use 3D. Repeat after me: never use 3D.

If you’re wondering What is the right graph for my situation?, the answer is always the same: whatever will be easiest for your audience to read. There is an easy way to test this, which is to create your visual and show it to a friend or colleague. Have them articulate the following as they process the information: where they focus, what they see, what observations they make, what questions they have. This will help you assess whether your visual is hitting the mark, or in the case where it isn’t, help you know where to concentrate your changes.

I identified six major changes to reduce clutter. Let’s discuss each. 1. Remove chart border Chart borders are usually unnecessary, as we covered in our discussion of the Gestalt principle of closure. Instead, think about using white space to differentiate the visual from other elements on the page as needed. 2. Remove gridlines If you think it will be helpful for your audience to trace their finger from the data to the axis, or you feel that your data will be more effectively processed, you can leave the gridlines. But make them thin and use a light color like grey. Do not let them compete visually with your data. When you can, get rid of them altogether: this allows for greater contrast, and your data will stand out more. 3. Remove data markers Remember, every single element adds cognitive load on the part of your audience. Here, we’re adding cognitive load to process data that is already depicted visually with the lines. This isn’t to say that you should never use data markers, but rather use them on purpose and with a purpose, rather than because their inclusion is your graphing application’s default. 4. Clean up axis labels One of my biggest pet peeves is trailing zeros on y-axis labels: they carry no informative value, and yet make the numbers look more complicated than they are! Get rid of them, reducing their unnecessary burden on the audience’s cognitive load. We can also abbreviate the months of the year so that they will fit horizontally on the x-axis, eliminati...

Six ways to eliminate clutter

One question regularly raised in my workshops is around novelty. Does it make sense to change up the colors or graph types so the audience doesn’t get bored? My answer is a resounding No! The story you are telling should be what keeps your audience’s attention (we’ll talk about story more in Chapter 7), not the design elements of your graphs. When it comes to the type of graph, you should always use whatever will be easiest for your audience to read.

DO NOT switch up colors to "add variety".

Roughly 8% of men (including my husband and a former boss) and half a percent of women are colorblind. This most frequently manifests itself as difficulty in distinguishing between shades of red and shades of green. In general, you should avoid using shades of red and shades of green together.

Color blindness

When I’m designing a visual and selecting colors to highlight both positive and negative aspects, I frequently use blue to signal positive and orange for negative. I feel that positive and negative associations with these colors are still recognizable and you avoid the colorblind challenge described above. When you face this situation, consider whether you need to highlight both ends of the scale (positive and negative) with color, or if drawing attention to one or the other (or sequentially, one and then the other) might work to tell your story.

Positive and negative colors

In his book Airman’s Odyssey, Antoine de Saint-Exupery famously said, “You know you’ve achieved perfection, not when you have nothing more to add, but when you have nothing to take away” (Saint-Exupery, 1943).

Here are some specific considerations to help you identify potential distractions: Not all data are equally important. Use your space and audience’s attention wisely by getting rid of noncritical data or components. When detail isn’t needed, summarize. You should be familiar with the detail, but that doesn’t mean your audience needs to be. Consider whether summarizing is appropriate. Ask yourself: would eliminating this change anything? No? Take it out! Resist the temptation to keep things because they are cute or because you worked hard to create them; if they don’t support the message, they don’t serve the purpose of communication. Push necessary, but non-message-impacting items to the background. Use your knowledge of preattentive attributes to de-emphasize. Light grey works well for this.

Identify Distractions

The title bar at the top of your PowerPoint slide is precious real estate: use it wisely! This is the first thing your audience encounters on the page or screen and yet so often it gets used for redundant descriptive titles (for example, “2015 Budget”). Instead use this space for an action title. If you have a recommendation or something you want your audience to know or do, put it here (for example, “Estimated 2015 spending is above budget”). It means your audience won’t miss it and also works to set expectations for what will follow on the rest of the page or screen.

Presentation slide title bar real estate

There are a few strategies you can leverage for gaining acceptance in the design of your data visualization: Articulate the benefits of the new or different approach. Sometimes simply giving people transparency into why things will look different going forward can help them feel more comfortable. Are there new or improved observations you can make by looking at the data in a different way? Or other benefits you can articulate to help convince your audience to be open to the change? Show the side-by-side. If the new approach is clearly superior to the old, showing them side-by-side will demonstrate this. Couple this with the prior approach by showing the before-and-after and explaining why you want to shift the way you’re looking at things. Provide multiple options and seek input. Rather than prescribing the design, consider creating several options and getting feedback from colleagues or your audience (if appropriate) to determine which design will best meet the given needs. Get a vocal member of your audience on board. Identify influential members of your audience and talk to them one-on-one in an effort to gain acceptance of your design. Ask for their feedback and incorporate it. If you can get one or a couple of vocal members of your audience bought in, others may follow.

When meeting chane resistance

Stories unite an idea with an emotion, arousing the audience’s attention and energy. Because it requires creativity, telling a compelling story is harder than conventional rhetoric. But delving into your creative recesses is worth it because story allows you to engage your audience on an entirely new level.

Presentation slides are to persuade using reason. Meanwhile stories convince emotions.

When asked about writing a captivating story by International Paper, Kurt Vonnegut (author of novels such as Slaughterhouse-Five and Breakfast of Champions) outlined the following tips, which I’ve excerpted from his short article, “How to Write with Style” (a great quick read): Find a subject you care about. It is this genuine caring, and not your games with language, which will be the most compelling and seductive element in your style. Do not ramble, though. Keep it simple. Great masters wrote sentences which were almost childlike when their subjects were most profound. “To be or not to be?” asks Shakespeare’s Hamlet. The longest word is three letters. Have the guts to cut. If a sentence, no matter how excellent, does not illuminate your subject in some new and useful way, scratch it out. Sound like yourself. I myself find that I trust my own writing most, and others seem to trust it most, too, when I sound most like a person from Indianapolis, which is what I am. Say what you meant to say. If I broke all the rules of punctuation, had words mean whatever I wanted them to mean, and strung them together higgledy-piggledy, I would simply not be understood. Pity the readers. Our audience requires us to be sympathetic and patient teachers, ever willing to simplify and clarify. This advice contains a number of gems that we can apply in the context of storytelling. Keep it simple. Edit ruthlessly. Be authentic. Don’t communicate for yourself—communicate for your audience. The s...

My blog is a personal journal dedicafted to self-reflection. But perhaps I can gain the same benefits by writing for others.

Story is what ties together information,

The first thing to do is introduce the plot, building the context for your audience. Consider this the first act. In this section, we set up the essential elements of story—the setting, main character, unresolved state of affairs, and desired outcome—getting everyone on common ground so the story can proceed. We should involve our audience, piquing their interest and answering the questions that are likely on their mind: Why should I pay attention? What is in it for me?

Module/Chapter/Lesson introductions

The idea is that you should first tell your audience what you’re going to tell them (“Bing,” the introduction paragraph in your essay). Then you tell it to them (“Bang,” the actual essay content). Then you summarize what you just told them (“Bongo,” the conclusion). Applying this to a presentation or report, you can start with an executive summary that outlines for your audience what you are going to cover, then you can provide the detail or main content of your presentation, and finally end with a summary slide or section that reviews the main points you covered (Figure 7.1).

When it comes to communicating data, I don’t typically recommend anything other than a white background.

Background color

When creating your visual in your graphing application (for example, Excel) and refining to get from good to great, you can leverage what I call the “optometrist approach.” Create a version of the graph (let’s call it A), then make a copy of it (B) and make a single change. Then determine which looks better—A or B. Often, the practice of seeing slight variations next to each other makes it quickly clear which view is superior.

A-B testing is "the optometrist approach"

At any point, if the best path is unclear, seek feedback. The fresh set of eyes that a friend or colleague can bring to the data visualization endeavor is invaluable. Show someone else your visual and have them talk you through their thought process: what they pay attention to, what observations they make, what questions they have, and any ideas they may have for better getting your point across.

UX/UI testing when stuck

There are a number of great blogs and resources on the topic of data visualization and communicating with data that contain many good examples. Here are a few of my current personal favorites (including my own!): Eager Eyes (eagereyes.org, Robert Kosara): Thoughtful content on data visualization and visual storytelling. FiveThirtyEight’s Data Lab (fivethirtyeight.com/datalab, various authors): I like their typically minimalist graphing style on a large range of news and current events topics. Flowing Data (flowingdata.com, Nathan Yau): Membership gets you premium content, but there are a lot of great free examples of data visualization as well. The Functional Art (thefunctionalart.com, Alberto Cairo): An introduction to information graphics and visualization, with great concise posts highlighting advice and examples. The Guardian Data Blog (theguardian.com/data, various authors): News-related data, often with accompanying article and visualizations, by the British news outlet. HelpMeViz (HelpMeViz.com, Jon Schwabish): “Helping people with everyday visualizations,” this site allows you to submit a visual to receive feedback from readers or scan the archives for examples and corresponding conversations. Junk Charts (junkcharts.typepad.com, Kaiser Fung): By self-proclaimed “web’s first data viz critic,” focuses on what makes graphics work and how to make them better. Make a Powerful Point (makeapowerfulpoint.com, Gavin McMahon): Fun, easy-to-digest content on creating and giv...

Examples of beautiful data stoytelling

Often, you can learn as much from the poor examples of data visualization—what not to do—as you can from those that are effective. Bad graphs are so plentiful that entire sites exist to curate, critique, and poke fun at them. For an entertaining example, check out WTF Visualizations (wtfviz.net), where content is described simply as “visualizations that make no sense.” I challenge you not only to recognize when you encounter a poor example of data visualization but also to pause and reflect on why it isn’t ideal and how it could be improved.

Learn from bad examples of visual data

Storytelling with data book club: read a chapter at a time and then discuss it together, identifying examples specific to your work where the given lesson can be applied. Do-it-yourself workshop: after finishing the book, conduct your own workshop—soliciting examples of communicating with data from your team and discussing how they can be improved. Makeover Monday: challenge individuals to a weekly makeover of less-than-ideal examples employing the lessons we’ve covered. Feedback loop: set the expectation that individuals must share work in progress and offer feedback to each other grounded in the storytelling with data lessons. And the winner is: introduce a monthly or quarterly contest, where individuals or teams can submit their own examples of effective storytelling with data then start a gallery of model examples, adding to it over time via contest winners.

Team application of this book

Understand the context. Build a clear understanding of who you are communicating to, what you need them to know or do, how you will communicate to them, and what data you have to back up your case. Employ concepts like the 3-minute story, the Big Idea, and storyboarding to articulate your story and plan the desired content and flow. Choose an appropriate visual display. When highlighting a number or two, simple text is best. Line charts are usually best for continuous data. Bar charts work great for categorical data and must have a zero baseline. Let the relationship you want to show guide the type of chart you choose. Avoid pies, donuts, 3D, and secondary y-axes due to difficulty of visual interpretation. Eliminate clutter. Identify elements that don’t add informative value and remove them from your visuals. Leverage the Gestalt principles to understand how people see and identify candidates for elimination. Use contrast strategically. Employ alignment of elements and maintain white space to help make the interpretation of your visuals a comfortable experience for your audience. Focus attention where you want it. Employ the power of preattentive attributes like color, size, and position to signal what’s important. Use these strategic attributes to draw attention to where you want your audience to look and guide your audience through your visual. Evaluate the effectiveness of preattentive attributes in your visual by applying the “where are your eyes drawn?” test. Think li...

Summary