My first chance came in the form of a front-end evaluation project for a children’s museum planning a new exhibition on dinosaurs.

Measuring What Kids Already Know about Dinosaurs

The museum wanted to understand what children and families already knew about dinosaurs – including whether they knew what other types of animals and plants existed at the same time.

I designed a fun card-sort activity, where parent-child pairs were asked to work together to sort 19 cards with images of different plants and animals into two piles:

• one pile for those they thought lived at the same time as dinosaurs, and
• one pile for those they thought didn’t live with dinosaurs.

Here’s a sample of a few of the cards we gave to families:

Draft 1

For my first stab at a graph showing the results, I applied several of the best practices I learned about in Great Graphs:

• I sorted my data from largest to smallest.
• I applied color meaningfully – using the client’s brand orange to show the animals that did exist at the time of dinosaurs and gray to show those that didn’t.
• I eliminated the unnecessary visual clutter from the Excel default graph and made some simple modifications (for example, increasing the width of the bars and the text font size).
• I even added annotations highlighting interesting findings.

Here’s what my first version looked like:

Draft 2

I thought I was off to a pretty good start, but I wasn’t sure if my graph was clearly explaining that some of the answers were correct and some were incorrect, so I decided to bring my graph to Office Hours with Ann to see what else I could do.

Ann offered me three simple ideas that took this graph from good to great.

1. Group the bars to better show which responses were correct or incorrect.

Rather than order all the bars from largest to smallest, Ann suggested that I group all the correct answers together (ordered from largest to smallest) and similarly group all the incorrect answers together.

2. Add space between the groups to create a visual distinction.

Although the same effect could be achieved by creating two separate graphs, Ann showed me how to add a gap between two sets of bars in a single graph by simply inserting one (or more) blank rows in the source table. (Note from Ann: Learn more about adding blank rows in this tutorial, and view another example of intentional gaps here.)

To make the difference between the two groups even more obvious, we also added subtitles to indicate correct and incorrect responses.

3. Add icons for visual interest and whimsy.

This graph is for a children’s museum project about dinosaurs. This is the type of graph that is just calling for a touch a playfulness.

We found an adorable dinosaur icon in the free icons that are included with all Microsoft Office products.

We added an orange dinosaur icon to highlight the correct answers and a grey one with a slash through it to highlight the incorrect answers.

Here’s the final version of the graph that I included in my report:

A big improvement made in three simple steps and less than 30 minutes.

There’s a reason the course is called Great Graphs.

Connect with Maia Werner-Avidon

On LinkedIn: https://www.linkedin.com/in/maia-werner-avidon/

Learn more about Maia’s work at www.mwainsights.com.

Beyond Graphs 1: I made a Great Graph after just a Few Course Modules

Soon after the course started, I brought Ann data about who connected with me on LinkedIn after I was listed as one of Nick Martin’s 9 Amazing Humans to Follow. Nick has a HUGE network and I got over 69 connection requests in the first day. And requests continued for more than a week!

So I made a graph to go with a post on LinkedIn, applying all the learnings from the first few course modules.

Beyond Graphs 2: 2 Things I Learned in 10 Minutes of Help in 1 Office Hour Session

I examined the few data variables on the LinkedIn connection requests. My impression was validated. Only 2 of 136 requests had a personalized message (despite LinkedIn experts emphasizing the need to personalize connection messages).

I tried different ways to display this finding (waffle chart, pie chart, and this one). Luckily, Office Hours were the next day. (Office Hours are a CAN’T MISS opportunity for immediate feedback!)

Ann took one look and exclaimed, “Ooh, let’s try the WeePeople font!” (Well, maybe not exactly like that!)

She then quickly used WeePeople to show the data.

Learning 1: More relevant and representative visuals with icons showing diverse silhouettes

(Hooray – No more using just the standard male icon.)

And then Ann taught us all how to make a gif which was even more effective at telling the “only 2 of 136 people” story.

Learning 2: Using a gif can give readers a quick result from your data

And, for those who follow LinkedIn stats to see how their posts engage, the post with the bar graph got 4,765 impressions and the 2^nd post (the next day) with the gif got 8,778 impressions!

Beyond Graphs 3: Now I’m Applying a Mental Checklist to Graphs and Charts

No – not only to the few graphs I’m making.

The course taught me and heightened my awareness to look at all the visual elements in the many graphics we see each day. There was so much learning from the course modules. And then many great opportunities in Office Hours to learn from what others were working on.

Here are things I find I am automatically looking for in these graphics:

• Color choice(s)
• Is it a Traditional or Storytelling title?
• The clarity of the data message
• Is the graph or chart the best for that message and the data being used?
• What elements could be removed for better clarity?

And a Beyond Graphs Bonus: Consistency and Efficiency

I consider myself a digital pioneer. But I didn’t know what I didn’t know, even being a longtime Word, PowerPoint, and Excel user.

I jumped into the course, and my efficiency increased in the first week! The course started – not with graphs – with ensuring basics including branding by setting my color and font defaults.

And then, a couple weeks later, I set up branding for a 3-part seminar series I did for Waey, the Association for Community Health in Saudi Arabia.

And I now have the consistency across Excel, Word, and PowerPoint and across my different PCs. What a difference!

This is just the tip of the iceberg of everything I am doing differently after Great Graphs – Excel!

Connect with Sue Griffey

LinkedIn: https://www.linkedin.com/in/suegriffey/

Twitter: @SueMentors

Youtube: https://www.youtube.com/channel/UC-rjWX4ZmTdo0S3ssKbut_A

SueMentors Resources: https://suegriffey.fyi.to/suementors-resources-for-your-professional-presence

A no-cost short course: Build and Update Your Professional Presence in 4 Steps at this page: https://www.linkedin.com/company/4-steps-to-build-update-your-professional-presence

I was working full-time and finishing graduate school at night.

My stress came out as insomnia.

I’d get tired of laying in bed… and go make YouTube videos. 😊

For me, being up in the middle of the night + making YouTube videos = intertwined.

I was up in the middle of the night again to speak at the Present to Succeed Conference (it’s mostly a European conference – different time zones).

I woke up at 3, presented at 4, and decided to make a YouTube video for you at 5.

I was up anyway, and I wanted to share some highlights from the conference session with you. Enjoy!

Watch a 16-Minute Segment

In the conference session, we learned about avoiding Death by PowerPoint by storyboarding.

Instead of presenting a single graph all at once, we’d explain the graph one piece at a time.

How to Edit the Existing Graph

In the video, you’ll learn about:

• adding target lines (if/when that applies to your project);
• grouping data with space (top vs. bottom categories);
• grouping data with color (blue vs. gray categories);
• adding words to explain our categories; and
• adding icons to increase memorability.

How to Storyboard the Graph

In the video, you’ll see me turn on my presentation voice and give a mini presentation.

I talk through the graph one piece at a time.

Behind the Scenes in My PowerPoint

In the video, you’ll see how I:

• make the finished graph;
• copy and paste that slide; and
• delete or hide one thing.

I’ve got all sorts of not-so-magical magic tricks: deleting icons and text boxes; adding white rectangles to cover words; changing the color of some bars to make them transparent; and deleting some of the numeric labels.

When It’s Worth Storyboarding Your Dense Graph

You don’t have to break up every graph across multiple slides.

I use storyboarding:

1. at the beginning of a presentation (to start with a bang), and
2. to explain dense, complex visualizations one piece at a time.

Bonus

Download my PowerPoint slides and explore them on your own.

Your Turn

If or when you apply this technique, get in touch! I’m cheering for you.

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

—–

I was ecstatic to register for the Great Graphs: Excel How-To’s course and transform my graphs using Excel instead of online data visualization software. I already had Excel, my data was there, and it did not make sense to use another platform to make the graphs I needed.

I had tried other options before, and it was always clunky because instead of focusing on the data and the message I was trying to show, I was inevitably focusing on the different software programs and how they would interact.

Tips, Tricks and Community

Through the Great Graphs: Excel How-To’s course, I learned more tips and tricks than I ever expected. But the most impactful, yet unexpected, outcome was joining a community and making connections with others that were also along their own journey of improving their graphs as a way of sharing messages and insights through data.

Throughout the course, I was exposed to new ways of thinking about graphs, Excel, and how data visualization can be used in the workplace to guide more data-informed decisions.

Now, I look at data visualization differently.

When I see data visualization in the newspaper or on social media, I think about what it was trying to show and whether it was successful. This critical eye helped me be a better peer and colleague when seeking to get the message across from my own data and in collaborating with others.

As we started the course, I got to know my peers also in the course.

When I was working on producing graphs for a blog based on my graduate work in evaluation surrounding the perceived effects of medication access from COVID-19, we reviewed and workshopped them together with live feedback in an office hour session.

Because of the varied backgrounds of those in the course, I was thinking about my work in a completely new and improved way. I started experimenting with graph types I had not used before, with some having greater success than others.

Before: Audience Left with Questions

My original graph applied many of the lessons we had covered – I used my color palette, I had storytelling titles, but there was room for improvement.

There was much left up for the audience to discern when reviewing the graph – was the 85% or 70% most important? Why were different values mentioned in the heading?

Live Editing During Office Hours

During the office hours, we mocked up what the graphs could look like and ultimately edited them to produce higher quality, easier to understand visuals. We took the original column charts and turned them into 100% stacked bar charts.

After: Clear Focus

Focusing on the major effects (both positive and negative) also provided a stronger narrative instead of hoping the audience would understand the message.

 Highlighting the ends of the bars gave not only visual focus, but a clear visual.

The final product was influenced by the feedback I received throughout the course. Getting feedback from the group changed the style of the graphs, colors, way I utilized text, and I was even encouraged to turn the graphs into a GIF for better online engagement.

Shared Learning is the Best Learning

As much as I looked forward to continuing the lessons, I was excited for the live office hour sessions when I would get to hear from someone else in the field and learn how they might use a graph style as well as how they did it.

Questions and discussions sparked thinking and problem solving about my data visualization in new ways, past the technical work in Excel.

As the course continued, my skills improved, and so did my connections with my peers. We connected on LinkedIn and offline to get to know more about each other’s work – similarities and differences abounded.

I found a great benefit in this as there was so much knowledge to be shared, wherever we were along our data visualization or professional journey. As a new evaluator, learning what others found to be successful also helped me in thinking about what I could apply to my own work.

The supportive community that was built goes beyond the technical skills we learned throughout the course, as I know I have a wider network to connect with for questions, feedback, and advice long after the course ended.

Connect with Maddison

LinkedIn: @maddison-staszkiewicz

Website: www.maddisonstasz.com