introducing the SWD podcast

 
ColePodcast.jpg
 

I'm very excited to officially launch the storytelling with data podcast! This first episode focuses on feedback in data visualization. I discuss the value of both giving and receiving data visualization feedback and potential problem areas to avoid. Hear The Economist's response to the recent hurricane data visualization challenge as well as answers to reader questions on the topics of when to use graphs, considerations with dashboards, and data viz 101 book recommendations.

Big thanks to Timo Elliston, friend and awesome NYC composer/musician, for the amazing original music, and to hubby Randy for encouraging all of this in the first place, equipping our office with recording gear, and for always being my biggest supporter.

I hope your enjoyment of the session is as great as the fun we had making it happen. If you like what you hear, please be sure to rate the SWD podcast on your favorite podcast platform!

Links mentioned during the podcast:

 

Feedback? email feedback@storytellingwithdata.com
Blog post: SWD makeover challenge on The Economist’s hurricane graph
Article: “Design & Redesign in Data Visualization” by Fernanda Viegas & Martin Wattenberg
Blog post: my guiding principles
Article: The subtle art that differentiates good designers from great designers by UX Planet
Blog post: a tale about opportunity
Book: The Big Book of Dashboards by Steve Wexler, Jeff Shaffer & Andy Cotgreave
Book: The WSJ Guide to Information Graphics by Dona Wong
Book: Show Me the Numbers by Stephen Few
Book: The Visual Display of Quantitative Information by Edward Tufte
Questions? email askcole@storytellingwithdata.com

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making a case for stacked bars

When I review common types of visuals used in a business setting during my workshops, one of the graphs we discuss is the stacked bar chart. I typically say something like the following:

“While we’re on the topic of bars, let’s talk about another common bar chart: the stacked bar graph. Stacked bars work well when you want to compare totals across different categories, and then within a given category, you want some understanding of the subcomponent pieces. Notice though, that they work less well if you want to compare those subcomponent pieces across categories. This is because as soon as you get past the first series, you no longer have a consistent baseline to use to compare. This is a harder comparison for our eyes to make, so something to keep in mind when reaching for stacked bars.”

I’ve noticed lately, though, that when a client shares a stacked bar as a makeover candidate, it usually gets remade into something else. Which has me pondering… Is there really a good use case for the (non-100%) stacked bar?

The most common scenario in which I see stacked bars used is to show total over time and also the change in composition. For example, I was just looking at a workshop example like this that depicted revenue over time: the overall height of the bars represented total revenue and then each bar was subdivided into source of revenue, in an attempt to show how the source of revenue was shifting over time as overall revenue changed. Here’s a quick sketch of what it looked like:

Stacked bar sketch - before.png
 

To me, this was a clear case where, by attempting to answer too many questions with a single visual, we don’t answer any one of them as well as we could if we broke it up into multiple visuals. In this case, my recommended view had a line graph showing total revenue over time, and a 100% stacked bar chart to show the relative shift in composition (revenue source). This is roughly what the visuals looked like (the real version was paired with additional explanatory text and emphasis that I've omitted for simplicity):

Stacked bar sketch - after.png

There is a challenge that frequently arises with the stacked bar: if anything interesting is happening further up the stack, it becomes challenging to see it because it’s stacked on top of other things that are also changing. This means potentially important components of the data or what we can learn from it can get lost or missed. In the above scenario, for example, there was an interesting shift happening in source of revenue over time that was hard to see in the original graph.

I’ve been racking my brain for good examples of stacked bars to figure out whether I should change how I discuss their use. I’ll ask for your help on this front momentarily.

I do have a couple of examples that are top of mind. There is a horizontal stacked bar that I highlight in Chapter 6 of my book as a model visualization:

In the above example, the most important thing is the length of the overall bars. It’s interesting to know what the subcomponent pieces are as a proportion of the given bar, but there isn’t a strong need to be able to compare those subcomponent pieces across bars. I think this works. Though I should mention that I’ve also received feedback (a one-off comment, so no idea how representative it is) that this particular visual is confusing.

As I write this, it occurs to me that I did also use a stacked bar in my prior blog post. This was a case where I wanted the audience to focus on the stacked piece (there were only two data series stacked on each other in this example), but the big picture opportunity the stacked portion illustrated across the various bars was more important than specific comparisons between the bars.

With that prelude, I'll turn the conversation over to you—have you seen examples of stacked bars that are effective? These can be vertical or horizontal (I think it's coincidence that the two I highlight above that work are horizontal and the one that didn't as well is vertical, but perhaps that's not the case?). Also, I limit my question here to the non-100% versions, as I do think there are more common use cases for 100% stacked bars, since you get additional flexibility with multiple baselines to align by and compare across (top and bottom-most data series in vertical 100% stacked, or left and right-most in horizontal 100% stacked). But I’m struggling to come up with many great use cases for simple stacked bars.

Please share your thoughts and examples by emailing them to stackedbars@storytellingwithdata.com by Wednesday, 11/22. Is there an example from your work where you’ve used a stacked bar effectively that you can share? (Please don’t share anything confidential—anonymize as needed.) Have you seen good examples in the media or elsewhere? Are there use cases you can imagine where a stacked bar would work well? What considerations should we keep in mind when using stacked bars? It will work best if you can share a visual, even if it's a simple sketch like I’ve included above.

I will pull together what I receive into a follow-up post. Stay tuned on that front and in the meantime I look forward to hearing from you RE: stacked bars!

a tale about opportunity

One statement that I make often and emphasize repeatedly in my work is that when it comes to explanatory analysis, we should never simply show data; rather, we should make data a pivotal point in an overarching narrative or story. Today, I’ll take you through an example that illustrates this transition from showing data to using data to answer a question in a way that leads to new insight.

Let’s assume you work for the pharmaceutical company, Gleam. At Gleam, you focus on Product X (common abbreviation: PX), a medication for Aglebazoba (this is a real example, but I’ve anonymized it and had some fun with the names to preserve confidentiality—these names sound like a foreign language because that’s how pharmaceutical naming sounds to me!). You’ve been tasked with providing an update on Product X’s penetration in the marketplace.

After considering this for a bit and discussing with some colleagues, you decide there are two important things to consider. First, the disease doesn’t affect everyone equally. Rather, diagnoses tend to be classified by severity into Mild, Moderate, and Severe. So you decide that categorizing the data in this way will make sense. Second, when thinking about how to measure penetration, you decide that the population of those diagnosed with the disease is the most straightforward way to quantify the potential market currently. Given these considerations and the data you have on hand, you create the following visual.

Opportunity1.png
 

This graph looks pretty good. The design is clean, everything is titled and labeled. Severity increases as we move up the graph, which makes sense. N counts were included to tell me how many people each bar represents. Color has been used sparingly to focus the audience's attention, with words at the top to tell them why they should focus there. Let's consider the takeaway highlighted here: a greater proportion of Moderate patients are taking PX compared to the total diagnosed with Moderate severity Aglebazoba. That's interesting. But does it answer the question we set out to?

In the above, we're graphing the % of total across two categories: (1) total patients diagnosed and (2) total patients taking Product X. But what if rather than severity as a % of total, we make severity the primary category and within that look at those taking the drug out of total diagnosed? I'll do this in the following step, and will also switch from graphing percents to graphing the absolute numbers (we'll incorporate the percents back in momentarily). 

Opportunity2.png
 

In the above view, the overall length of the bars represents the total number of patients diagnosed with Aglebazoba. The blue portion represents those taking Product X. If percents are important, we could add labels on the blue bars. I'll do that in the next view. Note now that this isn't % of total taking Product X, but rather the % taking Product X out of the total diagnosed with the given level of severity.

Opportunity3.png
 

So 35% of those diagnosed as Severe are taking PX, 61% of those with Moderate severity are taking PX, and 23% of those with Mild severity take the drug. Note that we can see the same thing here that was highlighted in the original graph: a higher proportion of those with Moderate severity are taking Product X compared to the other severity levels. But with this view, I can also see something new: opportunity. The blue portions of the bar represent those currently taking PX. Which means the grey portions of the bar represent those who aren't currently taking Product X... but potentially could be. Let's show this as empty space to be filled in:

Opportunity4.png
 

Now I can see the opportunity. But let's emphasize that even more, via darker, thicker lines:

Opportunity5.png
 

When I look at the above, the labels in the blue portion of the bars seem to be competing for attention with the opportunity in white. That's an easy fix: let's label the white portion instead.

Opportunity6.png
 

I recognize I may be bothering some people when I graph absolute numbers and label with percents. If you fall within that camp, we could address by taking the percents out of the graph...

Opportunity7.png
 

...but then tie the percents back in when we put all of the words around the visual to help make sure it makes sense to my audience and that they focus on the takeaway that I want them to. I see this as a tale about opportunity. Let's use words to make that point clear to my audience:

Opportunity8.png
 

After you've created a graph in response to a question, consider that question again. Too often, I find that we stick with the first way we aggregate the data and first view of it that we land on. It's easy to provide data that is relevant to a question without actually answering the question. If we step back and think about what sort of tale we can use the data to tell—is it a success, a failure, a call to action, or, as we've seen here, a tale about opportunity—it may reveal new ways to aggregate or visualize the data that will help you help your audience understand something new.

If interested, you can download the Excel file with the above visuals.

10/31 update: A couple people have commented that the tendency is to want to tie the blue percents in the text to the blue portions of the bars in the final iteration above, which is confusing. This is a great point (that's the Gestalt Principle of similarity, by the way, that makes us want to connect similar elements, like things that are colored the same). I've made an update to outline the opportunity in black and use black for those percents instead, as a way to visually make a distinction between the blue (people taking PX) and black (opportunity: those who aren't but could be taking PX) and tying the black portion visually to the percents in text through similar use of color. See below for the updated version. I think this resolves that prior confusion—let me know what you think!

Related thought: this is a great example of why it can be useful to seek input from others on our visual designs. When we get familiar with our data, we know intuitively how we want others to look at it, but this isn't necessarily how they will. Soliciting a fresh perspective is a great way to see our data through someone else's eyes and learn from this how to potentially further improve or refine our approach. Thanks for the feedback!

Opportunity9.png
 

learning through questions

 
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If you are a parent or spend time with young children I’m sure you can relate when I say, "Wow, kids ask a ton of questions, like, a TON of questions!" The remarkable thing is that they do so, all the time, everywhere, throughout the day and for some reason especially at bedtime (though I’m starting to become wise to their crafty delay tactics). Between my two boys, they often take a tag team approach—one asks the initial question, then the other chimes in with a follow-on query. Take for example, a recent dialogue during lunch:

AVERY: Why did the squirrels eat all the apples on our tree?
ME: Well, squirrels have to eat, just like you. They find their food outside, in places like our apple tree.
AVERY: But why doesn't the squirrel's mommy make them peanut butter and jelly sandwiches so we can have our apples?
DORIAN: I like peanut butter and jelly sandwiches. Do you like them too, Mommy?
ME: Yes, Dorian. Avery—squirrels can't really make sandwiches, that's why they look for nuts and fruit. Since our apple tree is there, they found the apples. Maybe a mommy squirrel was finding food for her baby squirrel.
DORIAN: Where do baby squirrels come from?
ME: Where's Daddy, boys?

If I step back, I can see that the seemingly never ending series of “Why? But why? How come?” is actually a very important part of kids' learning, development and retention. One can practically hear the gears turning in their heads as they process things from multiple angles.

Shifting to my work with storytelling with data—I notice that you have a lot of questions as well. Your queries come to me through many different channels—during workshops, after speaking engagements, via email, TwitterLinkedIn, Facebook & Instagram, in comments on my blog and YouTube channel. I enjoy engaging on these questions because I know this helps with the learning process and ultimately helps you be more effective and confident telling your stories with data. I also know that if someone takes the time to ask a question, there’s a good chance someone else was pondering the same thing.

My limited bandwidth makes it challenging to answer every single inquiry (and I'm sure I've missed some over time), so I’m excited to launch a new forum for answering a number of your questions each month. I'll be doing so through a novel medium for me—a podcast. I love podcasts because you can listen (and learn!) almost anywhere—on your morning run, during your daily commute, or while lounging at home. The SWD team will scour the various channels I mentioned for posted inquiries, but you can jump ahead of those lines by simply emailing your question to us at: askcole@storytellingwithdata.com. We’re recording our first ask cole podcast now to be aired soon, so submit the questions that are top of mind and will help you learn and make progress with your work.

...and for now, if we could just hold off on the squirrel chatter, that’d be great!

Looking forward to hearing from you!

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novel vs. the boring old bar chart

Often, to kick off a workshop we’ll do a quick round of introductions, where I ask participants to tell me something they are hoping to learn over the course of the day. It is not uncommon for someone to respond with something like, “I want to learn some new exciting ways to show data so I’m not just using boring old bar charts all the time.” I jot down a note, silently challenging myself to convince them otherwise over the course of the following hours.

This happened just last week, where a participant voiced a wish to learn novel ways to show data. I can understand this desire. But it’s not the graph that makes the data interesting. Rather, it's the story you build around it—the way you make it something your audience cares about, something that resonates with them—that’s what makes data interesting.

We circled back to this novel-ways-of-showing-data idea later in the workshop when looking at some of the team’s specific examples. I want to share with you the makeovers and discussion; it was another important reminder to me that simple often beats sexy. A “boring old bar chart” can get the job done—and even end up being people's preference.

The team was looking at some market research data, wanting to compare their company to their main competitor across a few dimensions. They originally visualized this with a connected scatterplot. It didn’t work well. People found they were having conversations about how to correctly read the graph for too long before they were getting to the point where they could really look at the data and see what they might say with it. I won’t go through the work of fully recreating it here, but to give you a sense, I’ll do a quick sketch (all data has been changed and scenario generalized to preserve confidentiality):

Connected scatter 1.png
 

There were two main points to make with this data:

1.    The company was doing better than the competitor across all areas except ATTRIBUTE 1.

2.    The company was beating their target across all areas except ATTRIBUTE 5.

This seems pretty straightforward, right? You can get to these takeaways through the above visual, but there are improvements we can make to it and other potential views that could also work. I originally thought we should look at two alternatives: (1) a dot plot and (2) a slopegraph. It was actually Elizabeth on my team who added a third option—the “boring old bar chart”—into the mix (I'm glad she did!). Let’s take a look at each of these.

First, the dot plot. This was mainly an attempt to improve their original visual with a similar view and some slight modifications. I don't use these super often, but have found there are some good use cases. I thought this would be an appropriate scenario for it. The base visual looked like this:

Sexy vs boring 1.png
 

Rather than connecting the dots downward, as in the original connected scatterplot, which makes the primary comparison how OUR COMPANY is doing across the various attributes, the horizontal lines here draw the eyes from left to right. This makes the primary comparison OUR COMPANY vs. the COMPETITOR, which seemed to be the main point here. 

Next, let's apply the brand colors:

Sexy vs boring 2.png
 

Now that I've incorporated color, I can vary intensity to emphasize certain points. For example, we could first draw attention to ATTRIBUTE 1, where OUR COMPANY scores lower than the COMPETITOR:

Sexy vs boring 3.png
 

I could also add in a marker and text designating the target and use the same strategy to draw attention to ATTRIBUTE 5, where we score below TARGET:

Sexy vs boring 4.png
 

I actually thought the dot plot worked well. But I wanted to show some alternatives. Slopegraphs can sometimes be a good way to visualize group comparisons, like we have here. By putting the COMPETITOR at the left and OUR COMPANY at the right, the relative slopes of the lines give a sense of how we're doing across the various attributes compared to the competition. Where the line slopes upwards, OUR COMPANY is outperforming the COMPETITOR and vice versa.

Sexy vs boring 5.png
 

I could emphasize just the ATTRIBUTE 1 line to draw attention to the one area where we score lower than the COMPETITOR. Note that with the slopegraph, since I already have the clear spatial separation between COMPETITOR at the left and OUR COMPANY at the right, I don't need to introduce color as a means of telling the groups apart (vs. in the dot plot, where OUR COMPANY was left of COMPETITOR for ATTRIBUTE 1, but right for all the others, so we need some other way to distinguish one from the other). Here, I can instead use color for emphasis, or simply keep everything grey and use intensity to draw attention to where I want my audience to look.

Sexy vs boring 6.png
 

Similar to the dot plot, I can also add a symbol and text showing where the TARGET is and drawing attention to ATTRIBUTE 5, where we fall below it.

Sexy vs boring 7.png
 

Dot plots and slopegraphs aren't anything crazy. But they aren't as popular or well-known as traditional lines bars, and pies, which means they sometimes carry that novel appeal that those wanting something fresh desire. 

Next, let's look at the data in a bar chart:

Sexy vs boring 8.png
 

As with the slopegraph, position distinguishes for us OUR COMPANY vs. the COMPETITION (the former is always first, the latter second) so we don't necessarily have to introduce color here. Instead, we could use intensity—pushing some elements back by lowering intensity and drawing others forward via higher intensity—to focus our audience's attention. We might focus first on ATTRIBUTE 1:

Sexy vs boring 9.png
 

As in the other views, we can incorporate the TARGET into the visual and draw attention to ATTRIBUTE 5, where we missed it. Let's try a different view of the TARGET this time, using light background shading to illustrate the region where we are above target and emphasizing ATTRIBUTE 5, where we fall short:

Sexy vs boring 10.png
 

There's no "right" answer in terms of how one should display this data. Any of these visuals could work. The different views let us more or less easily see different things. Let's look at the base versions (without any emphasis) side by side:

Sexy vs boring all.png

I showed a similar side-by-side after we discussed each of the options during the workshop and asked people to vote which they liked best. Remember, this was the audience who said they were seeking novel approaches. Which did they choose? I'm sure you've guessed it—the "boring old bar chart."

To take the next step and put words around it so my audience knows why they are looking at this data and why they should care, we could do something like the following:

Sexy vs boring 11.png
 

Meta-lesson: novelty may not be the best goal. Bars don't have to be boring when you've used them to help make the data accessible and made it clear to your audience why they should care.

What do you think? Which view of the data do you like best? Why? Leave a comment with your thoughts!

If interested, you can also download the Excel file with the above graphs.