Jun 222016
 

Overview

My obsession with finding the best way to visualize data will often infiltrate my dreams. In my slumbers I find myself dragging Tableau pills in an ongoing pursuit to come up with the ideal dashboard that shines light on whatever data set has invaded my psyche.

But is the pursuit of the perfect dashboard folly?

Probably, as I’ll explain in a minute, but I don’t want to suggest anyone not at least try for the clearest, most insightful and most enlightening way to display information.

Is this way is the best way?

This pursuit of the ideal chart preoccupies a lot of people in the data visualization community. Consider this open discussion between Stephen Few and Cole Nussbaumer Knafflic that transpired earlier this year.

As you will read, Few weighs in on Knaflic’s book Storytelling with Data and her use of 100% stacked bar charts.  He cited this particular example.

Figure 1 -- Knafflic's 100% stacked bar

Figure 1 — Knafflic’s 100% stacked bar

Few argued that there was a better approach and that would be to have a line chart with a separate line for each goal state.

Figure 2 -- Few's line chart

Figure 2 — Few’s line chart

Having written about visualizing sentiment and proclivities, I chimed in suggesting that a divergent stacked bar chart would be better (see Figure 3.) I think this presents a clearer and more flexible approach, especially if you have more than three categories to compare as the 100% stacked bar chart and line chart can become difficult to read.

Figure 3 -- My divergent stacked bar chart

Figure 3 — My divergent stacked bar chart

The ongoing public discussion was engaging and congenial but I’ve seen similar cases where one or more of the parties advocating a solution become so certain that his / her approach is without a shadow of a doubt the only right way to present the data that tempers flare high. Indeed, I’ve seen instances where some well-respected authors have declared a type of “Sharia Law” of data visualization and have banned so-called heretics and dilettantes from leaving comments on blogs and even following on Twitter!

My take? While I prefer the divergent stacked bar, the real question is whether the intended audience can see and understand the data. In this case, if management cannot tell from any of the three charts that there was a problem that started in Q3 2014 and continued for each quarter, then that company has some serious issues.

In other words, if the people that need to “get” it can in fact make comparisons, see what is important, and make good decisions on their new-found understanding of the data  — all without having to work unnecessarily hard to decode the chart — then you have succeeded.

I’m not saying don’t strive to be as efficient , clear, and engaging as possible, it’s just that the goal shouldn’t be to make the perfect chart; it should be to inform and enlighten.

And in this case I think all three approaches will more than suffice.  So stop arguing.

Understanding and educating your audience

Earlier this year I got a big kick out of something that Alberto Cairo retweeted:

Figure 4 -- Avoid Xenographphobia: The fear of unusual graphics / foreign chart types.

Figure 4 — Avoid Xenographphobia: The fear of unusual graphics / foreign chart types.

Xenographphobia! What a wonderful neologism meaning “fear of unusual graphics.”

So, why do I bring this up? While it’s critical to know your audience and not overwhelm them with unnecessary complexity, you should not be afraid to educate them as well. I’ve heard far too often people proclaim “oh, our executive team will never understand that chart.”

Really? Is the chart so complex or the executive so close-minded that they won’t invest a little bit of time getting up to speed with an approach that may be new, but very worthwhile?

I remember the first time I saw a bullet chart (a Stephen Few creation) and thought “what is this nonsense?”  It turns out it wasn’t, and isn’t, nonsense.  It took all of 60 seconds for somebody to explain how the chart worked and I immediately saw how valuable it was.

Figure 5 -- A bullet chart, explained.

Figure 5 — A bullet chart, explained.

I had a similar reaction when I first heard about jump plots from Tom VanBuskirk and Chris DeMartini. My thoughts at the time were “oooh… curvy lines.  I love curvy lines! But I suspect this is a case where the chart is too much decoration and not enough information. I bet there are better, simpler ways to present the data.”

Figure 6 -- Jump plot example. Yes, these are very decorative, but they are also wickedly informative.

Figure 6 — Jump plot example. Yes, these are very decorative, but they are also wickedly informative.

Then I spent some time looking into the use cases and came to the conclusion that for those particular situations jump plots and jump lines worked really well.

That said, there are some novel charts that I don’t think I will ever endorse, with the pie gauge being at the top of my list.

Figure 7 -- The pie gauge, aka, a donut chart within a donut chart, aka, stacked donut chart. I won't go into the use case here but a bullet chart is a much better choice.

Figure 7 — The pie gauge, aka, a donut chart within a donut chart, aka, stacked donut chart. I won’t go into the use case here but a bullet chart is a much better choice.

So, what should we do?

I’ve argued that you should always try to make it as easy as possible for people to understand the data but you should not go crazy trying to make the “perfect dashboard.”

I also argue that that while you should understand the skillset and mindset of your audience, you should not be afraid to educate them on new chart types, especially if it’s a “learn once, use over and over” type of situation.

But what about aesthetics, engagement, and interactivity? What roles do these play?  Is there a set of guidelines or framework we should follow in crafting visualizations?

Alberto Cairo, in his book The Truthful Art, suggests such a framework based on five key qualities.

I plan to write about these qualities (and the book) soon.

Jan 202015
 

 “With great power comes great responsibility”

— Voltaire

— Benjamin Parker (Uncle Ben from Spiderman)

Overview

Recently both Ryan Sleeper and Andy Kriebel blogged about donut charts in Tableau.

Figure 1 -- Donut chart courtesy of Andy Kriebel

Figure 1 — Donut chart courtesy of Andy Kriebel

While both of them cautioned about where, when, and how best to use them, I fear many people will ignore the warnings and dig into this sugary, analytically-impoverished chart type and start creating stuff like this.

Figure 2 -- Really bad donut chart.  In fact, it’s just a pie chart with a whole in the middle.

Figure 2 — Really bad donut chart. In fact, it’s just a pie chart with a hole in the middle.

Yuk.

And what fuels my fear?  Ryan and Andy do great work, and they write great blogs.  They rightfully have a lot of influence in the Tableau community.

But with great power — and influence —  comes great responsibility and I suspect that some people will see Ryan and Andy’s work, ignore their recommendations, and apply the following bit of “logic”:

Ryan Sleeper is a Tableau Iron Viz champion and really cool — and he makes donut charts.

Andy Kriebel is a Tableau Zen master and really cool — and he, too, makes donut charts.

I want to make cool vizzes and be really cool; therefore, I should make donut charts.

[Insert face palm here]

Interviewer: So, what do you have against donut charts?  Don’t you think they look cool?

Me: My problem is that donut charts don’t tell you very much.

Interviewer: Yes, but they look cool!

Me [yelling]: You know what else looks look cool?  Pictures from the Hubble telescope.  Vintage electric basses.  Three-dimensional pie charts! Should I festoon my dashboards with these images, just because they look cool?

Interviewer: Fine, explain to me why this chart types doesn’t work, but I’d like to see an alternative that isn’t BOR-ING!

Me:  Okay, allow me to do the following:

  • Explain why donut charts don’t tell you much (or not as much as a bar chart)
  • Present a better alternative
  • Show how to have your cake (not your donut) and eat it, too

Why donut charts don’t tell you much

Consider the chart in Figure 1, above.

I always recommend that people ask the following questions when coming up with a visualization:

  • Do I need different colors?
  • Do I need a legend?
  • Do I need measure labels?

Let’s see what happens when we remove the measure labels:

Figure 3 -- donut chart without measure labels.

Figure 3 — donut chart without measure labels.

The chart does pass some of the “can I figure this out test”.  For example, it’s easy for me to see that West is around one quarter of the way to goal and that East is a little more than half way.  Where the chart fails is with comparison among regions.  For example, can you tell how much closer North is to its goal than West?  This comparison is particularly hard to determine as it’s very difficult to gauge how much longer one arc is than another arc.

A better alternative

I think a bar chart with a goal line is easier to grok.  It tells me more and takes up less screen real estate, too.

Figure 4 -- Bar chart with goal line.

Figure 4 — Bar chart with goal line.

There’s an added advantage in that I can easily see both the progress towards a goal and that the goal is $100,000.

Better yet, suppose the goals were different for each region?  Right now they each have a shared goal of $100,000 but suppose the goal for North is $125,000 and the goal for East is $75,000?  With the donut chart, how will you show the actual goal and the progress towards the goal at the same time?

Why is it easy to compare progress across regions using the bar chart?  I’ve discussed this in length here, but the bottom line is that humans are much better at judging the length of bars than they are judging the area of circles or the lengths of arcs.

But does the chart pass the “no measure labels” test?  Have a look.

Figure 5 -- Bar chart without measure labels.

Figure 5 — Bar chart without measure labels.

While I prefer having labels, it’s pretty easy for me to the following:

  • North is more than twice as long as West
  • East is a little more than half way
  • South is more than a third of the way to goal
  • East is about twice as long as West

In other words,  I can draw conclusions more easily from this chart than the donut chart.

Another Example

Consider the chart below that shows the percentage of confirmed judicial nominees that are women, broken down by president.

Figure 6 – Donut Chart showing Female Judicial Nominees (source: Alliance for Justice)

Figure 6 – Donut Chart showing Female Judicial Nominees (source: Alliance for Justice)

There are some good stories in here but they are buried.  Compare this with a bar chart that contrasts the different presidents and underscores the differences between Republicans and Democrats.

Figure 7 -- Bar Chart showing Female Judicial Nominees (source: Alliance for Justice)

Figure 7 — Bar Chart showing Female Judicial Nominees (source: Alliance for Justice)

I think this is a lot clearer.

But it is, well, boring.

Have your cake and eat it, too

I admit that most of my practice has me building stuff that looks more like it would appear in The Economist than in USA Today, but I do understand that you may need to create something that is eye catching.

And I agree that the donut chart is eye catching, but I hate to sacrifice information for the sake of decoration.

Is there a way to get both?

I think there is.  Let’s work on the first example where we were examining progress towards a goal broken down by region.

Want some sugar?  Try a lollipop chart

Figure 8 -- Lollipop chart

Figure 8 — Lollipop chart

Creating a lollipop chart is easy in Tableau. You create a dual axis chart where both measures are identical but you have a different chart type (in this case a bar chart combined with a circle chart).

Figure 9 – Tableau settings for a lollipop chart

Figure 9 – Tableau settings for a lollipop chart

Try some fun shapes

We can also take the lollipop chart and dress it up with a custom shape, like the one shown below.

Figure 10 -- Combination bar and shape chart

Figure 10 — Combination bar and shape chart

While I prefer the lollipops to the runner, I have no problem with the chart shown above because I don’t have to work hard to see both the distance from the goal and to compare among regions.  That is, I did not fight Tableau’s suggested default chart type but instead took it and dressed it up a bit.

Conclusion

NoDonutEven if you are tasked with having to create visualizations for mass public consumption I urge your to use caution before creating a donut chart. I understand that you may need something that is more visually arresting than a simple bar chart, but take that as a challenge: find a way to make something that looks cool but does not sacrifice one bit of analytical clarity.

And if you do create a donut chart, please look carefully at what Ryan and Andy did (and did not do) in fashioning theirs.