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You’re not going to keep the ugly, outdated defaults.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

The post Are Viewers Expecting a Story? Lightning Talk from the DATAcated Expo appeared first on Depict Data Studio.

Amanda is the Data Visualization Capability Lead at Excella; a co-organizer for Dataviz DC; and the Operations Director for the Data Visualization Society (DVS).

She’s also one of the most knowledgeable people around when it comes to visualizing COVID-19 data.

Listen to Our Conversation Here

What’s Inside: Amanda’s Career Path

“Ten years ago, when I finished graduate school, I couldn’t have guessed that this would be my full-time job and I could wear as many hats as I do in the data viz world. Now I think it’s really exciting because there are a lot of paths into data viz,” Amanda told me.

Excella

Amanda is currently the Data Visualization Capability Lead at Excella, a technology consulting firm. She works with organizations from the CDC to Fortune 500 companies.

“It’s been a really transformational learning experience for me to see not just different ways data and tech get used, but the different ways projects get managed. I’ve learned a lot more about Agile processes and software development and thinking about how some of those same practices actually apply when we’re building different analytical applications like dashboards,” Amanda said.

Previously, she worked in global public health.

DataViz DC

Amanda is also involved with DataViz DC. They focus on bringing people together from various disciplines, from graphic designers to software developers. They host monthly meet-ups, which have included hands-on workshops, guest speakers, and career panels. It’s a great way to connect in the DC area- there are over 8,000 members!

Data Visualization Society

Amanda is the Operations Director for the Data Visualization Society.

They are focused on bringing people together across the world and serve as a global, professional organization for dataviz professionals at any level. They have over 13,000 members from more than 130 countries around the world. They communicate through Slack, email and Fireside Chats with panels of dataviz experts.

Amanda told me that the Data Visualization Society is “a great space for actually bringing together different disciplines. Instead of focusing on one tool or tech stat, we instead said, ‘How do we bring together the people that are individually engaged in Tablueau groups? Power VI groups? R groups? Graphic designers? How do we bring all those people together and create a space for people early in their career or looking to change careers and do data visualization as their full-time job and create a space for them to grow and learn and share best practices?’”

She continued, “While we use different tools and technologies in different data viz disciplines and roles, I think there are so many cross cutting best practices that once you learn and master them in one tool, it’s really easy to think about how they are used and applied in other spaces. DVS tries to create that central space to bring people together and really advance the data viz discipline as a practice profession.”

Visualizing COVID-19 Data Responsibly

Next, I asked Amanda to share tips for visualizing COVID-19 data responsibly.

She wrote “Ten Considerations Before You Create Another Chart About COVID-19” on the Data Visualization Society’s blog in March 2020, and I also wanted to know whether her guidance had evolved or shifted since writing the article.

Amanda said, “There are different points in which we make decisions about how and what we visualize, and then how we publish and share. What are we creating and doing more for our own exploration and understanding? And what are doing so that we can share it with the public to help others make sense of information?”

Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices.

COVID-19 Data Quality Issues

Amanda said, “Consider the fact that even though the dataset are very accessible right now, does not mean it is high quality data.”

“There are so many issues and challenges with the different ways that COVID-19 cases are counted in different states or countries. Are we including only cases that have been lab confirmed with a swab test that came back positive? Or are we also including probable cases or diagnostically confirmed cases?

How those cases are counted are very different in some states and countries. It’s really hard to make these apples to apples comparisons, as easy as it might seem since the data is so accessible.”

Understanding COVID-19 Data Collection

Amanda said, “If you’re going to dabble in COVID-19 data in some way, try to really understand how the data gets collected so that you have a firm understanding of what that process looks like and why there might be issues with the accuracy, timeliness or completeness of those datasets.”

She continued, “Make sure you understand how the data got collected. Just because it’s there as a nice shiny, analyzable table doesn’t mean it’s not something you should try to understand the underbelly of.

In your choices around how you analyze and design certain visualizations, be mindful that the data that we have is really incomplete. The case data is really a function of how many tests are being done.  As a result, where we have more certainty on the death counts, deaths are also a function of cases.

So we have to think about the fact that we have a lot of cases not represented in that data. We’re seeing that come out more and more in some of the retroactive reporting being done in countries that are farther along in their epidemic curves.

Remember that there’s a lot of uncertainty in the data and it’s not uncertainty that we can represent visually well. We can’t always quantify that uncertainty.

Make sure that you’re considering the ways in which your visualizations could be misinterpreted or misused.”

Be Careful with Comparisons and Reference Points

Amanda said, “One of the common comparisons I’ve seen is comparing COVID-19 to the flu.

We’ve seen that in the media and early on even public health folks were trying to make sense of this disease by comparing it to the flu.

When we look at how we collect data on the flu in the US, we have routine, structured reporting systems for that data with better quality data. We have a disease that comes in seasonally and we understand what that seasonality looks like. We don’t know that about COVID-19.

So comparing cases in March for COVID-19 and the flu [doesn’t make sense]. We’re in a very different point of that epidemic curve for COVID-19. It really isn’t an apples to apples comparison. We’d need a whole year of COVID-19 data to start to make that comparison.

So be cautious in how you start to try to create those reference points which can help us enable understanding, but also can mislead.”

Remembering the People Inside the COVID-19 Datasets

Amanda said, “Remember that every single case and every single death represents a person. As we visualize and think about health-related data, thinking about that fact that each of those cases and each of those deaths represents a person and their story, makes it really important to be thoughtful and mindful about how we’re presenting that information.”

COVID-19 Data Visualization Color Choices

Amanda said, “Those red, big bubble maps can be hard to interpret, but the color choice also creates such a visceral, angry, sad response. I hope we can be thoughtful in the ways our visualizations can create emotional responses especially when visualizing such sensitive data.”

Connect with Amanda Makulec

• Data Visualization Society: DataVisualizationSociety.com/Join
• Slack Workspace: DataVizSociety.Slack.com
• Twitter: @ABMakulec

The post Visualizing COVID-19 Data Responsibly: An Interview with Amanda Makulec appeared first on Depict Data Studio.