declutter this graph!

The importance of decluttering when communicating visually with data is something that I talk about regularly (you can watch a video from me on this topic, and I've also blogged about it many times—here's an early example). It's mostly common sense stuff when we stop and think about it: get rid of the visual elements in your graphs that are unnecessary. Doing so can have a profound impact. Yet too often, we don't take the time to do this.

To illustrate the benefit, let's examine the following visual. I found it on viz.wtf, so we know it's a model for what not to do when visualizing data. There is a ton of clutter and other issues here. Consider for a moment: what clutter would you eliminate?

Declutter GIF 1.png

Scroll down to see the progression I went through. Each of these changes on its own is relatively minor, but sum them up and it's a pretty big difference between the original and the decluttered version. We can take it a step further by identifying the so what? and using words and color to make the point clear. 

Declutter GIF 7.png
Declutter GIF 8.png

The graph still isn't perfect. It bothers me that the time intervals on the x-axis aren't consistent. You may see other things you'd approach differently as well. But check out the improvement we can make by identifying and eliminating clutter and making some other changes to reduce cognitive burden. Don't let unnecessary elements distract from your data or your message. If interested, you can download the Excel file.

What's your favorite type of clutter to remove? Leave a comment!

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so what?

"What is the point?" This is a question that comes up often in my workshops when we are looking at graphs and discussing how they can be improved. Other flavors of this same basic question take the form: "What is the message?", "What is the story here?" or the concise, "So what?"

Too often, when we communicate with data, we don't make our point clear. We leave our audience guessing. Your audience should never have to guess what message you want them to know. The onus is on the person communicating the information (you!) to make that clear.

I've been thinking a lot about story lately (in preparation for my recent Tapestry presentation and also for an upcoming project). The word "story" has become a buzzword. Everyone wants to "tell a story with data." But very often, when we use this phrase, we don't really mean story. We mean what I mentioned above—the point, the key takeaway, the so what? 

I've started to draw a distinction when I talk about story into two types: story-with-a-lower-case-'s' and Story-with-a-capital-'S.' The latter is Story in the real sense of the word. A Story has key critical components—there is a structure, a shape—it has a plot, a rising action, a point of climax where tensions reach their highest, a falling action, and a resolution. (Related note, Jon Schwabish is currently running a series of blog posts on story—the capital S kind—that starts off with his thoughts on the question what is Story?). But I veer slightly off track.

Today, my focus is on story-with-a-lower-case-'s,' which, in my view, is the minimum level of "story" when you are communicating for explanatory purposes with data. It's not really story at all, but rather the point—the so what? For every graph you show, for every slide you show: make the point clear to your audience. This can be through your spoken words in a live meeting or presentation, or physically written down on the page if the document is meant to stand on its own. Don't assume two people looking at the same graph or slide will interpret it the same. Which means if there is a key takeaway—which there absolutely should be if you're at the point of communicating the information—you need to make that point clearly to your audience. Put it into words!

Speaking of words, in slideware-land (PowerPoint, Keynote, and similar), the title bar on each slide is precious real estate (this is similar to a section heading in a written report). This is the first thing your audience encounters when they see your slide. Too often we underutilize this space with a descriptive title. Think about instead using this precious real estate for an active title. Put your key takeaway—the so what?—there. It makes sense when we stop and think about it: use your title strategically. (If you need more evidence than common sense, check out Michelle Borkin's Tapestry talk where she demonstrates the importance of effective titles and also covers some other interesting learnings for communicating with data from her studies at Northeastern University.)

Let's check out the importance of having a clear so what? and the impact effective titling can have through an example. The following is a visual I discussed in a conference presentation recently:

I originally came across this graphic when combing through recent posts on viz.wtf (an entertaining potpourri of what not to do when visualizing data). I know, I know—it's not really data visualization at all, just a visual made to look infographic-y with some numbers in it. Being clear on the so what? can help us better understand how to best visualize this data.

I did a little digging, and it turns out that the graphic above was originally part of an article in The Daily Texan:

Note the title: "DWI rates increase in months following departure of Uber and Lyft." It turns out there already was an effective title making the so what? clear.

Now that we know what point we're trying to make, we can visualize the data to make this point more effectively. As a related note, these numbers don't actually appear to be DWI rates, as described in the original graphic. A little research reveals this data is likely the number of DWI arrests. I'll make that clear in the title and also spell out what DWI is the first time it's used, in case anyone in my audience is unfamiliar. If we want to show an increase over time, a line graph could do that effectively: 

The line graph allows us to clearly see the trend: decreasing DWIs January through April and increasing April forward. But remember that part about making my so what? clear via words? In the above, my audience is left to interpret the data themselves and draw their own conclusions. If I'm the one presenting the data, I should assist in that process. In the following, I've added a subtitle making the takeaway clear. Also, there was an important event—the departure of Uber and Lyft—which I've annotated on the graph directly for context.

I put the main point into words via the subtitle. I did my audience the added bonus of tying these words visually to the relevant data points through consistent use of color. This means that after my audience reads the words, they know exactly where to look in the graph for evidence of the point that is being made. If I were showing this graph on a slide, I could have the takeaway "DWIs increase in the months following Uber and Lyft departures" as the slide title (and remove it from the subtitle, leaving just the main title on the graph). 

Bottom line: make your so what? clear via words on every graph and every slide. Don't leave your audience guessing, or leave your important takeaway being known to chance!

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an updated post on pies

Today's post is about one of my favorite dessert graphs: the pie chart. Those who follow my work know that I am not a fan. In fact, I've written posts with titles like "pie charts are evil" and given presentations called "death to pie charts." It can be fun to be a little provocative sometimes. Though one might argue this is taking it too far.

A wise person once said to me, "rather than ban pies altogether, teach people how to use them appropriately." This is sage advice. The challenge for me is that the appropriate use cases are few and personally, in pretty much every one of these cases, I'd opt for another approach.

Still, I do not want to spread misinformation. Pie charts are not inherently evil. Like pretty much any tool, they can be used well and they can be used not-so-well. Since my pie-bashing posts and presentations, new research has been conducted (by afore-quoted Robert Kosara and Drew Skau) that debunks some previously held common beliefs about pies. So I thought it prudent to write an updated post on the pie chart. While not exactly glowing, this will likely be at least a little more balanced compared to what you may have seen from me on this topic in the past.

The appropriate use case for the pie chart

Pies do a better job than probably any other visual out there at expressing the part-to-whole relationship. When you see a pie, you immediately have an understanding that it depicts a "whole" and can be sliced into pieces of that whole. It's also very easy for us to pick out a very large slice or a very small slice.

The limitation of the pie is that it's harder to say much more specific than that. When segments are close in size, it can be difficult to determine which is bigger or by how much. When that is an important goal, the pie chart breaks down. Ok, that didn't take so long—I'm already talking about what not to do with a pie chart. Let's shift next to more on that.

What not to do with pies

Pies seem to lend themselves more than other graph types to unnecessary—and often downright harmful—dressing up and embellishment. No other graph type is depicted in 3D or exploded as frequently as the pie.

A sampling of results from Google image search for "pie chart."

A sampling of results from Google image search for "pie chart."

But the argument that it isn't fair to ban pies based on a bad example of a pie is a logical one. Due to their frequent misuse and in the spirit of teaching people how to use them correctly, here are some pointers on what not to do with pie charts:

  • Don't use 3D effects or explode your pie. At best these add unnecessary clutter; worse, they can make it difficult or impossible to accurately understand the relative values in the pie. Here's a simple example.
  • If the pie is depicting percents, it must sum to 100%. If it sums to anything other than 100%, something is wrong. If not percents, then the pie must sum to some meaningful whole.
  • Don't have a ton of slices. There isn't a hard and fast rule here, but be reasonable. A pie showing a ton of tiny categories will be impossible to read (even if legible, hard to say much useful from, like this). Consider whether it might make sense to combine small slices into an "Other" category.
  • Don't use a pie if the primary goal is to compare the size of the slices. The lack of alignment to a common baseline and area encoding of data makes this difficult. A bar chart will usually be a better option if comparing a quantity across categories is the primary goal.
  • Don't use multiple pies and ask your audience to compare across them. This piece of advice may be controversial. But if the slices are different across the pies (which I'd expect they are if you have something interesting to say with them), the pieces shift around; this plus the spatial separation and lack of alignment to a common baseline make comparing slices across pies difficult. Perhaps you could get away with it if you're emphasizing a single slice across multiple pies, but if you want to do more than that, pies won't be a good approach. 

In conclusion

I will continue not to use pies. Does that mean you should follow suit? Not necessarily. What I advise in my workshops is, when you find yourself reaching for a pie, pause and ask yourself why. If you can answer that question, you've probably put enough thought into it to use the pie chart. Though when I step back and think about that advice—really, that's something we should do anytime we make any kind of graph. Think about what you want to enable your audience to do with the data you are graphing and whether the type of graph you choose is allowing for that in a straightforward manner. If you do that, you'll be well positioned to get your point across. And that's sort of the whole point, isn't it?

For more on this topic (and some varied perspectives on pies), check out the following posts:

The above isn't a comprehensive list—if you know of others worth mentioning, please do so in a comment.

I'll also note that one limitation of the pie study is that it included pies depicting only two segments. I hope to see further research expand on this and also look at pies having three or more slices.

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