Tuesday, March 31, 2015

the great pie debate

You can't title a talk "Death to Pie Charts" and not expect to spur some debate on the topic. Sometimes being a little provocative can help generate interest and keep people's attention. It seemed to work last night at a talk I gave at the University of San Francisco as part of their Data Visualization Speaker Series.


We had an awesome turnout and I covered a condensed overview of the key lessons I teach in my workshops: understand the context, choose an appropriate visual display, identify and eliminate clutter, draw attention where you want your audience to focus, and tell a story. As part of the lesson on common visual displays, I noted one graph you won't see from me: the pie chart. We looked at an example to illustrate some of the challenges reading pie charts and discussed some alternative ways to visualize the data. Then we went on to cover the remaining lessons, followed by some lively Q&A.

The debate started with a simple question that went something like this: I've recently become interested in data visualization and I've been reading a lot about the field. Specifically on the use of pie charts, I've read some things that denounce them and others that say they have a place. Are you aware of any research comparing the takeaways that people get from pie charts compared to bar charts, for example?

My response went something like the following. This is a difficult space to study. Many of the studies that come out demonstrating one thing are opposed via counter-studies that show the opposite. My personal dislike of pie charts is more anecdotal - when I see them used in a business setting, inevitably they fail. 

I didn't talk about this last night, but upon further reflection, as I think back through the many pie charts I've encountered over time (hundreds, at least), I can think of only two cases where I tolerated them:
  1. At Google when we first started sharing diversity stats on the workforce internally - the team wanted to show the general breakdown of men vs. women (for example) but didn't want to communicate the specific numbers. In this case, the fact that our eyes don't do a great job of accurately measuring two-dimensional space worked in their favor. So in a way, they were taking advantage of one of the pie chart's biggest disadvantages.
  2. More recently, I encountered this data visualization highlighted in Best American Infographics 2013 - ten years of art history. Each pie represents an individual painting with the five most prominent colors shown proportionally. You can see the shift in color usage over time. Art via pies. I actually really like this!
Personally, I don't use pie charts because when I pause and think about what I want to show, I've always found a way that seemed to get the information across better than the pie chart.

That said, intelligent people will disagree with me and point out use cases for the pie. I welcome this diversity of perspective! Last night, after giving my viewpoint, I opened the question up to the audience. Santiago Ortiz (Moebio Labs) was in attendance and offered some great perspective. I'll paraphrase the viewpoint he shared: There are studies, and usually bar charts win in terms of people remembering the numbers. But it's difficult to research the Gestalt feeling of a "percent of whole" where pie charts are actually effective. So is the story about the specific numbers, or the relative amounts, as a percent of the whole? If it's the latter, then pie charts can work. (I'll note also that this is a similar point to one raised by Robert Kosara as part of his highly valued feedback on my forthcoming book).

Still, I'm standing firm. I won't use pies.

Does that mean you shouldn't use pies? Not necessarily.

First and foremost, always think about what you want your audience to be able to do with the data you are showing. Choose a visual that will make this easy. I often recommend the following. If 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 make it work. I should point out that this is something you should do for any type of visual you are using. Making yourself articulate why the chosen visual works for your needs is one way to help ensure that it actually does.

We didn't solve the great pie debate last night and we won't solve it here. People stand on different sides of the fence and I actually think that is ok. When it comes to data visualization, rarely is there an absolute right or wrong. You should constantly be applying your critical thinking skills. Don't do something blindly because of a statement you read or hear. Think about your audience, what point you are trying to make, and how you can do that in an effective way. If unsure, create your visual and seek feedback.

Big thanks to the event organizers and sponsors for last night's event: Scott, Sha, Alark, Sophie, Chris, all of the student volunteers, and everyone else who helped. Thanks also to those who participated in Q&A and everyone who showed up to the talk. I had a great time and I hope you did, too!

Monday, March 23, 2015

the biggest bang for your buck

After trekking through some surprise springtime snow, I had a great public workshop in Chicago this afternoon (want to join in the fun? see here for upcoming sessions, including workshops in London, Dallas, and San Francisco). Discussion and Q&A are some of my favorite components of the workshops, because we can tackle specific challenges that folks are facing. There are always great questions and today was no exception. There was one super practical question that stuck with me that I thought I'd share more widely here.

You've likely heard of the 80-20 rule. Basically, in business it's the idea that you can put in 20% of the effort and get 80% of the result (and avoid the remaining 80% of work that only yields an additional 20% of result). The question was: "how can we apply the 80-20 rule to what we've learned today?" In other words, out of all of the meaty content we've covered, where should you start when it comes to having the greatest impact? Or, as I'll paraphrase it - where should you focus your energy to get the biggest bang for your data visualization buck?

My answer? There are two easy things you can start doing today to have greater impact when it comes to communicating with data:

First: always tell a story. Think about what you want your audience to get out of every graph you show and STATE IT IN WORDS. Doing this simple step goes an amazingly long way when it comes to helping make the data you show make sense to your audience. When you put the takeaway into words, your audience knows what they are meant to look for in the visual. We spend the hands-on portion of the workshop looking at a number of real-world example graphs. All made by well-intending people. And the question that comes up again and again and again is: what point are they trying to make? Don't make your audience work to figure this out - state it for them!

Second: use color sparingly and strategically. Rethink how you use color - don't use it to make your graph colorful. When used sparingly, color is your single biggest tool for drawing your audience's attention to where you want them to pay it. I often start by making every single component of my visual light grey, pushing it all to the background - the data, the axes, the titles. This forces me to think about where I want to draw attention and use color intentionally and with purpose to emphasize those pieces of the visual.

Pair these two things - state your story in words and use color strategically to highlight where you want your audience to look - and you'll have gone a long way down the path of communicating effectively with data. Bonus: you don't even need crazy technical skills to do either of these things.

Thanks, Bill, for the thought-provoking question!

Thursday, February 26, 2015

annotate with text

In keeping with my prior post, I'm sharing another "most-photographed slide" from my recent workshops.


My voiceover typically sounds something like this:

When it comes to storytelling with data, one very important component of stories is words. There are some words that absolutely have to be there: every graph needs a title and every axis needs a title. This is true no matter how clear you think it is from context. The only exception that comes to mind is if your x-axis is January, February, March, etc., you probably don't need to title it "months of the year." You probably should make it clear what year it is. Any other axis needs a title. Label directly so your audience doesn't question what they are looking at.

Don't assume that two different people looking at the same data visualization will walk away with the same conclusion. Which means, if there is a conclusion you want your audience to reach, you should state it in words. Use what we know about preattentive attributes to make those words stand out: make them big, leverage color and/or bold, and put them in high priority places on the page like the top.

Speaking of which, that title bar - stories have words: annotate with text - is precious real estate. It is the first thing your audience encounters when they see your screen or your page, so make it count. Use this space for active titles, not descriptive titles. If there is a key takeaway for your audience, put it there so they don't miss it. It will also help set up the content that is to follow on the rest of the page.

When you are communicating with data, there are some words that usually need to be there: data source, as of date, and perhaps notes on assumptions or your methodology. These are necessary words but they don't need to cry out for attention. Use what we know about preattentive attributes to emphasize the important parts of your visual and also to de-emphasize less critical pieces. Footnotes can be small, the text can be grey, and they can be in lower priority places on the page like the bottom.

Use words to title, label, and explain; they help make your data visualizations accessible!

To see this and other storytelling with data lessons firsthand, attend one of my upcoming public workshops.