March dataviz madness: table vs graph

March madness is here—this three-week period when college basketball fever sweeps the States on the path to crowning the NCAA national champion. We’re pulled into the drama and tension of a single elimination tournament (who will emerge as the Cinderella team to upset a No. 1 seed?) and the stakes are high for teams: one sub-par performance and you’re out.  

When it comes to communicating with data, the stakes can also be high. Maybe not quite as ruthless as a single elimination tournament (one ineffective graph usually doesn’t mean our season is over) but a subpar visual might mean a missed opportunity for our audience to make a data-driven decision.

In data visualization, well-designed visuals are buzzer beating 3-pointers: they capture our attention because they get the main point across quickly and effectively. In today’s post, we’ll look at a dataviz match-up: will it be the table or the graph for communicating an underlying message?

Imagine you’ve encountered the following table: either in a live setting (someone has shown this on a PowerPoint slide) or own your own (said PowerPoint slide has been emailed to you).


What’s your initial reaction to this much data? If you’re like me, you’d probably groan and move on, totally disregarding all the hard work that was done behind the scenes to produce this table. Ouch.

When deciding whether to use a table or a graph, consider what the audience needs to do with the data: Do they need a certain level of detail? Are there different units of measure that need to be relayed together? Will they need to refer to a specific line of interest or compare things one by one? If yes, then a table may meet those needs. However, if there’s an overarching message or story in the data, think about making it visual for your audience.

Back to our match-up—imagine that the underlying story is that in recent years, packaging costs have increased at a higher rate and are projected to exceed budget at the end of the fiscal year. Refer back to the tabular data—how long does it take you to find the data that supports this?

Contrast that time-consuming process with the visual below, where I’ve visualized the relevant pieces and added explanatory text and focus through sparing color to make the data more accessible:

after visual.png

So what is the appropriate use case for a table? When your audience needs detail on specific values or when you have multiple units of measure to report simultaneously. In my previous roles, we used tables frequently in monthly status meetings when the main goal was for participants to give updates on their lines of business and participants wanted to be able to go row by row (or column by column) and refer to specific lines of data. Over time we realized many of these tables weren’t being used and we’d push them to the appendix—they remained there for reference but weren’t competing for attention with the main takeaways.

While we won’t know who wins it all in March Madness until the national championship on April 8, in this match-up we can choose a clear winner: the graph!

In fact, the graph will typically win when there’s an overarching message in the data. A well-designed graph simply gets that information across more quickly than a well-designed table. Don’t make your audience do more work than necessary to understand the data!

For more examples of how to consider if a table is more effective than a graph, check out our previous posts:

Elizabeth Ricks is a Data Visualization Designer on the Storytelling with Data team. She has a passion for helping her audience understand the ’so-what?’ as concisely as possible. Connect with Elizabeth on LinkedIn or Twitter.

recommended reading: Info We Trust


Info We Trust: How to Inspire the World with Data is a beautiful book. It feels nice to hold. The colors are vibrant. The language is poetic. The content is inspiring.

If you work with or have an interest in data, you should own this book.

I read it from cover to cover in a two day sprint a few weeks ago in preparation for a conversation with author—and friend of mine—RJ Andrews. It’s the first book I’ve read so thoroughly in quite some time: pouring over not only the main content, but also the preface and end matter. Nearly every inch of this book is filled with information: margins are full of quotes from wide-ranging sources (RJ read hundreds of books over the course of development) and other relevant tidbits. The text and margins are interspersed with hand-drawn images (even the graphs are drawn by hand!) that help reinforce and illustrate concepts. Here’s an example 2-page spread:


The chapters are relatively short in length but dense in ideas and concepts, which provides good balance. The book is divided into six main sections: (1) Origin, (2) Metaphorical, (3) Mathemagical, (4) Sensational, (5) Informational, and (6) Onward. Also don’t miss the impressive and cleverly formatted bibliography and RJ’s essay on how the book came to be.

While I enjoyed it all, I especially appreciated the Mathemagical chapters: Create to Explore, Explore to Create, and Uncertain Honesty. I’m commonly asked questions about exploratory data analysis, and together these chapters pose a number of thought-provoking questions that can help direct those working with data through this process. I also really appreciated the Sensational chapters, which explore a number of other areas (e.g. Museum design), imploring the reader to draw their own parallels to data storytelling. Chapter 16 “Inspire Trust” was another standout section for me, with some great insightful discussion on people’s belief systems and the resulting difficulty of changing minds.

Info We Trust is definitely not a how-to book, and yet it is interlaced with practical advice. To give you a sense of language and style, here is one excerpt I highlighted, from Chapter 3, Embodied Encoding (pages 43-44):

There is a candy shop full of ways we get to communicate meaning visually. For example, the concept of importance is naturally associated with size. Big things are important. Why might this be? We start off small. When you are a child, big people like your parents are important. Bigger people, the ones who were already grown up, are much more powerful. Sometimes big adults are even scary. Even longer ago, big animals, you must remember, used to eat us.

Big things, whether parent or predator or palm tree, are also important because, to our eye, big things are closer. Ultimately, big things occupy a larger portion of our visual fields. There, big things vie for more of our attention. Important big things stretch, conceptually, into our language (e.g., “I wish you would stop focusing on small matters and see the giant issue we have”). Embodied metaphors transcend language because all people have similar embodied experiences. Big things are important in Zulu, Hawaiian, Turkish, Malay, and Russian. When we make pictures of important things, we do not have to abstract all the way to language metaphors. Just draw important things bigger on the page.

At one point, RJ discusses sparking curiosity in your audience. He says—and I’m paraphrasing—that good stories leave space for the audience to make connections. The book itself does this beautifully—not prescribing “do this” or “don’t do that,” but rather making observations and leaving the reader space to make connections and extrapolate to their own work.

I found myself experiencing both excitement and sadness as I neared the end: excitement, as I could tell it was building in a grand crescendo, sadness that it would soon be over. That sadness abated quickly, however, when I got the chance soon after ending my own experience with the book to talk with RJ about it. You can listen to our conversation:

There are a lot of fun and inspiring surprises throughout Info We Trust that I won’t spoil for you. Let me just end by saying that I highly recommend this book and I hope you enjoy it as much as I did.

Thank you, RJ, for creating Info We Trust and for sharing it with us all!

InfoWeTrust RJ in Office.jpg

#SWDchallenge: visualize THIS data!

There is no single “right” way to graph data. Any data can be visualized multiple ways, and variant views of the data allow us to see different things. This month’s challenge is to see this in practice: we challenged our readers to show us how they’d visualize the same dataset.

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