That was the overarching theme of my Lightning Talk at the DATAcated Expo, which was held virtually in October 2021.

You’re not going to keep the ugly, outdated defaults. Great!

But what should you do instead?

And how do you modify a graph so that it’s just right for your audience?

Surely a group of scientists will need something different from a group of policymakers.

Some audiences adore data. Others don’t.

Some audiences have plenty of time. Others don’t.

In this blog post, you’ll learn about:

• the differences between default, traditional, and storytelling graphs;
• which techniques can help you tell a story with data (e.g., dark colors); and
• when to use each type of graph.

Watch the DATAcated Expo Lighting Talk

Missed the live event?

Watch the Lightning Talk here.

This is a 17-minute video. If you’re short on time, just watch a 10-minute segment — minutes 2 through 12 of the video.

Here’s a summary of what’s inside.

Defining the Term “Data Storytelling”

This is a tricky term with lots of definitions.

Some people love this term.

Others hate it.

In the recording, you’ll see me ask the attendees to share what “data storytelling” means to them.

You might define data storytelling as:

• “What does data really mean, and what do you want it to tell.” — an Expo attendee
• “Translating data for non-data centric users.” — an Expo attendee

And data storytelling is NOT:

• Fiction
• Making things up
• Biasing our audience
• Fudging the numbers

Data Storytelling in a Bar Chart

In the Lightning Talk, I showed attendees three versions of the same graph: default, traditional, and storytelling.

We’ll look at each of these side by side, so that you can see how they’re similar and how they’re different.

At the end, I’ll ask you to comment and share which style you think each of your audiences need.

The Default Bar Chart

We never, ever keep the default settings.

The Traditional Bar Chart

Instead, at a bare minimum, we need to design a traditional graph.

We would:

• Enlarge the font
• Enlarge the bars (by decreasing the gap width)
• Remove the border
• Add labels (optional—if we think our audiences would want specificity)
• Adjust the scale
• Use brand colors
• Use brand fonts

It’s up to the viewers to read the chart and figure out the “so what?” for themselves.

The Storytelling Bar Chart

Sometimes, our audiences prefer storytelling graphs.

You already spent 60 seconds cleaning up the default settings.

In another 60 seconds of editing, we would:

• Sort the bars (e.g., greatest to least)
• Gray everything out
• Highlight one takeaway finding with a dark color
• Add the takeaway finding to the graph title
• Bold a few key words to make the title even more skimmable

Data Storytelling in a Slope Chart

You can apply these principles to any and all chart types.

Here’s what the three different styles look like in a slope chart.

(A slope chart is just a fancy name for a line chart that has exactly two points in time.)

The Default Slope Chart

Defaults are for 2005.

We know better.

C’mon, Excel. And Tableau. And PowerBI. And and and.

The Traditional Slope Chart

At a bare minimum, we need to:

• Enlarge the fonts
• Adjust the scale
• Remove the border
• Add brand colors
• Add brand fonts
• Remove the legend and directly label the data

(Direct labels have three key advantages: They’re faster to read; they’re better for people who are colorblind; and they print better in grayscale.)

The Storytelling Slope Chart

Take the edited graph you just made, and keep going!

In a storytelling slope chart, we would:

• Gray everything out
• Highlight one thing at a time
• Re-write the title and put the takeaway message in the title
• Bonus points: Bold a few key words to make it even more skimmable

Which finding will you highlight in a darker color?

You might highlight:

• The Success Story (Project A)
• The Debbie Downer Story (Project C)

Be careful with red; in Western cultures, red means caution! warning! But colors are culturally-specific; in Eastern cultures, red doesn’t necessarily mean anything bad.

Data Storytelling in a Scatter Plot

We didn’t have time to discuss scatter plots at the DATAcated Expo, but I’d still like to share this example with you.

Here’s how data storytelling would be applied to a scatter plot.

Never keep the default settings!!!!!!!!!!

Traditional graphs are all one color and they have topical titles.

Storytelling graphs have a dark-light contrast and takeaway titles. For bonus points, you could label a few key points.

Data Storytelling in a Map

Finally, here’s how data storytelling would be applied to a choropleth map.

Never keep the default settings!!!!!!!!!!

In traditional maps, none of the colors stand out, and they have topical titles.

In storytelling maps, we’d add an intentional dark-light contrast and takeaway title. For bonus points, you could label a few key points.

When Should You Use Data Storytelling?

Comment below: When would you use each style?

Which of your audiences prefer traditional graphs?

Which of your audiences prefer storytelling graphs?

In the video, you’ll also hear the conference attendees share their perspectives, and you’ll hear from me, too.

There’s a misconception that accessibility takes all day, that’s it’s costly, or that it’s complicated. Those are all false.

Accessibility is woven into all my trainings, but since this is a topic I get asked about a lot, I decided to make a new talk that’s focused just on accessibility for dataviz.

In Spring 2021 I gave a talk at the Good Tech Fest conference about dataviz accessibility quick wins.

The talk was a “Choose Your Own Adventure” style where the audience chose what we discussed from a list of options. They chose:

• direct labels,
• lower the reading level, and
• lower the numeracy level.

You can watch the recording or read the highlights. Enjoy!

—–

Watch the Conversation

Here’s the main takeaway message: remove legends and directly label instead.

You probably know what a legend is, but direct labeling? What is that?

Let’s look at an example of a regular (inaccessible) graph.

Why Traditional Legends Don’t Work

When I saw this graph a few years ago, I actually liked most aspects of it.

I really liked the title in particular, and how it was phrased as a question, which gets the audience to engage. Two thumbs up to the title, “What happened to women in computer science?”

Legends Take Too Long to Read

But then I kept reading a little bit and I was like, “Wait a second… Time out.”

In full color I could mostly tell which section of the legend corresponded with which line. The turquoise lines were tricky because it’s hard figure out which is dark, which is medium, and which is lightest. Your eyes zig–zag back and forth trying to differentiate between the three. It’s really time-consuming.

Legends Don’t Work for Grayscale Printing

So it works in color, kind of, but what about grayscale printing?

Some people will view our graphs on-screen. Others will print them.

And if they’re printing the graphs, we should plan for grayscale printing. Colored ink is so expensive.

It doesn’t work at all.

Legends Don’t Work for People with Color Vision Deficiencies

What about color blindness?

If somebody has a color vision deficiency and can’t differentiate between red and green, the lines would all look yellow.

Traditional legends don’t work; they’re a thing of the past.

So what to do instead?

Directly Label the Graphs

We’re going to directly label our graphs.

What does that mean?

Direct labeling means you put the labels as close as physically possible to the data.

In this line chart, you’d just add the labels off to the side of the line.

Direct labels are:

• Faster for everyone to read (less eye zig-zagging)
• Grayscale-friendly
• Colorblind-friendly

A win-win-win!

Bonus points if you color-code the text to match the line it is labeling. (Red text for a red line, turquoise text for a turquoise line, and so on.)

How to Label Pie Charts

We’ve looked at line charts.

So, how do we label a pie chart?

Friendly reminder: Pie charts aren’t evil. They can be used as long as you follow the rule of two: you’re only allowed two slices in your pie. Maaaaybe three. The dark slice will be what you want the viewers to really look at, versus everything else in gray. Simple, right?

But we still need to directly label them, and it’s as easy as putting the labels as close as physically possible to their slices.

For example, if you have short labels, you can place the labels on top of the pie slices.

Now it’s speedier for people to read, it’s legible in grayscale, and it’s even legible for people with color vision deficiencies.

A question I get a lot is, “But if I have really long labels?” I know most of us aren’t comparing A to B.

If you have long labels, you can put your labels outside of the pie charts.

Bonus points again if you color-code the labels to the corresponding slices.

How to Label Donut Charts

Here’s another scenario for you with donuts. You’ve seen these, right? They’re just a pie chart with a hole punched in the middle.

They have the same rules as pie charts: two slices (max), with one dark slice versus everything else.

But, it’s really hard to fit any labels on top of donut segments. So how do you label these?

You have three options:

1. Outside of the donut segments
2. Inside the donut itself
3. Beside the donut

How to Label Bar Charts

Have you ever seen this, where Excel gives a legend that reads something like ‘Series1’?

This is confusing for viewers. To fix it, all you need to do is delete the legend.

How to Label Clustered Bar Charts

If your bars are long enough, you can place the labels on top of the bars, like this.

No need to label every single bar. Teach the viewers how to read the chart by labeling the top bars. Then, let them read the rest on their own.

During the Good Test Fest talk, an audience member asked how I added those labels.

You can:

• Add text boxes on top of the bars (beware: clunky and time-consuming)
• Use fancier automation techniques (e.g., concatenating the words and numbers together, a technique from this course)

How to Label Clustered Column Charts

I’m not a fan of putting the labels on the columns. The labels would need to be rotated vertically, which takes longer to read than horizontal labels.  

I typically use horizontal clustered bar charts to allow for horizontal labels, which are the fastest to read.

Download the eBook

Want to learn more about accessible data visualization?

In this ebook, you’ll learn 10 quick wins for designing accessible data visualizations. These small edits can have a big impact for our coworkers, board members, and funders who have color vision deficiencies, hearing loss, or learning disabilities–and for all of us who are pressed for time.

For your complimentary copy, use code: goodtechfest

Take a simple line graph, for example.

How will you label your line graph?

With vertical axis labels and light gray grid lines? With labels directly above or on top of the data points? A mix of both?

Here are four styles to consider.

Option A: Label the vertical axis

The first option is to simply label your vertical y-axis: 0, 25, 50, 75, 100, and so on.

The trick is strike a balance between labeling too frequently and not frequently enough. In this fictional scenario, I used increments of 25. The increments you choose will likely depend on your unique dataset.

Then, lighten (mute) the grid lines. Thin gray lines > thick black lines. We need our viewers to focus on the star of the show — the burgundy and orange lines — and not get sidetracked by the backup dancers — the supplemental information like grid lines and tick marks.

I use this style when I want viewers to focus on the general, big-picture view. Is the line generally going up or going down? Where are the peaks and valleys over time?

The viewers won’t see the exact values. In other words, my spreadsheet will tell me that Organization A had a 130 in 2009. But my viewers can only estimate that value.

The viewers’ takeaway message might be, “Organization A’s values are always above Organization B’s values. Both organizations have higher numbers in 2015 compared to 2009. Organization A started around 125 and went up to the 175-200 range, and Organization B started in the 25-50 range, got as high as the 100-125 range, but then went back down to the 75-100 range. And what the heck happened to Organization B between 2014 and 2015?”

Sometimes I add markers (those little circles on top of the lines).

I include markers when I want my viewers to remember that each point represents a different point in time. Rather than the smoothed-out appearance in the line above, this style subtly emphasizes that there gains and losses over time. Make sure your markers are relatively small; otherwise, the graph can look outdated and clunky.

Option B: Label all of the data points directly

A second option is to remove the axis and label the data points directly.

Direct labeling means placing the labels as close to the data as possible. In this case, the numeric labels go right above, or on top of, the data points. We’re aiming for physical proximity.

You might choose to place the labels directly above the lines. However, this style tends to get a bit cluttered, especially when there are more than two lines per graph, or if you have lots of points in time to display.

To avoid some clutter, I often center the numeric labels directly on top of each data point:

Or, you might center the numeric labels directly on top of circular markers.

Meh.

The circles need to be pretty large to fit two-digit and three-digit labels. And if my labels included percentage signs, then the circles would need to be even larger.

This style gets clunky fast. It reminds me of something I would draw in elementary school. Feel free to disagree… I don’t have research to back this up. It’s just my personal aesthetic preference.

Option C: Label both the vertical axis and data points

No, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no.

This style is overkill. Information overload. Super cluttered.

Axis labels help viewers estimate the numbers. Data labels help viewers see the exact numbers. Do your viewers need estimates or exact numbers? Put on your thinking cap and choose one or the other.

Label the axes, or the data points, but not both.

This chart would score poorly on the Labels Are Used Sparingly section of the Data Visualization Checklist.

Option D: Label just a couple points along the line

Finally, a fourth option is to only label a few points along the line.
You might label the beginning and end points. Or, you might label a specific year or two. For example, you might be telling a story about what happened in 2012 specifically. If so, you could label the 2012 point only.

This style helps you avoid information overload and is often preferred among laypeople viewers who want the big-picture, birds-eye-view of information. If your viewers are researchers or data scientists who love seeing alllll the raw data, I wouldn’t recommend this style.

You might forego the vertical axis labels:

Or, you might include the vertical axis labels:

Which styles do you use most often? Which styles do you prefer?

The topic caught my attention but the separate legend about the line graph made me cringe.

This graph is challenging to read in color (which turquoise category goes with which line?) and would be impossible to read when printed or photocopied in grayscale.

These data labels and separate legend score a big fat zero on the Data Are Labeled Directly section of the Data Visualization Checklist.

The solution is simple. First, remove the legend.

Then, insert those labels beside their corresponding lines. The goal is to get the labels as close as possible to the actual line so that your viewers aren’t zig-zagging their eyes back and forth between the lines and the legend.

To insert labels next to the lines, you can:

1. Format the data labels so that the label contains the category name. In Microsoft Excel, right-click on the data point on the far right side of the line and select Add Data Label. Then, right-click on that same data point again and select Format Data Label. In the Label Contains section, place a check mark in either the Series Name or Category Name box.
2. Insert text boxes next to the lines. There’s no magic behind text boxes; insert the as you normally would just like when you’re using Word or PowerPoint. Text boxes take a few seconds longer but give you greater flexibility than traditional data labels in terms of placement.

Finally, for bonus points, color-code the labels so that they match their lines. Use turquoise for medical school, law school, and the physical sciences, and use red for computer sciences.

Direct labeling! A small edit for you and a huge advantage for your viewers.