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You’re not going to keep the ugly, outdated defaults.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

Redundancies steal precious time from our days. Redundancies force us to read two visuals instead of one—the table and the graph—when all we need is a single well-designed graph.

Before

Here’s a before slide that I encountered in a client workshop. I tried to figure out how the two graphs and two tables were related, if at all. I started to crunch some numbers in a calculator and couldn’t find any clear connections. Eventually, it clicked! They wanted to display both the numbers and percentages, but they couldn’t figure out how to do that in a single visual, so they created both a graph and a table. Aha! That’s an easy fix!

After

We needed to combine the percentages from the graph with the raw numbers from the table. We:

• Decided to focus on the percentages, so those are bold and large. The numbers are there, too, but they’re smaller.
• Transformed the column charts into stacked columns to further emphasize the percentages (how the darker 32% is part of the whole blue section).
• Decluttered the entire slide by removing the redundant table (duh) and by removing the borders, grid lines, and footnotes. We also removed the arrows that had shown 5.4% and 3.1% increases. We considered adding the arrows again but decided that the after version was so easy to follow that viewers could do the simple mental math for themselves.
• Edited the colors. Before, the visuals were color-coded by year (blue for 2012 and green for 2016). After, the visuals are color-coded by product (blue for Product A and green for Product C). Immediately, you know that the slide is comparing two things, the blue thing and the green thing.
• Applied a text hierarchy. A text hierarchy tells your viewer where to look. The important text should be large, dark, and bold. Before, there were too many different font sizes, so our viewers didn’t know what was important. After, product names and percentages stand out because they’re significantly larger, darker, and bolder than everything else.

Here’s what our finished slide looked like:

Do your slides have redundant tables and graphs? Examine each one. Decide what to focus on. We opted to focus on the percentages. Then, design a single graph or table to highlight that single piece of information.

Bonus! Download the Materials

Want to peek behind the scenes and see how I made the graphs? Download the spreadsheet and slides.

Download the Materials

The post How to Consolidate Redundant Tables and Graphs appeared first on Depict Data Studio.

Option A: Axis stretches just past the actual maximum value (30% in this example)

The biggest number is 26%, and the axis goes from 0% to 30%, just past the biggest number in the bunch. Your stakeholders will think the 26% looks enormous because it stretches all the way across the screen. Wow, look at our numbers! Great news! Of course, we’ll have to work on Category D, but we can certainly improve that number! Especially when the other categories are looking so good! Talk to your teammates: is this the message we’re going for?

Option B: Axis stretches all the way to the potential maximum value (100% in this example)

What if the numbers have the potential to stretch all the way to 100%? (The percentage of attendees who said they’d recommend your conference to a colleague, the percentage of students who graduated on time, and so on.) If you’re trying to hit 100%, now the 26% isn’t looking so hot. Argh. We thought everything was going so well! We’ve got so much to improve upon. Where do we even start?! Ask your teammates: is this the right time to risk overwhelming the stakeholders with so much bad news?
My advice: Good facilitation skills are an ingredient of good data visualization. I often begin with the first graph to avoid scaring stakeholders away from action. As we get to know each other  — and they get to know their numbers — I slowly introduce the second style.
When choosing minimum and maximum axis values, what other factors do you consider?

Download the template.

The post The Graph’s Scale: Actual Maximum Value or Potential Maximum Value? appeared first on Depict Data Studio.

Notice anything funny about the height of these bars?

The shortest bar, for 2012, represents 26,000 youth. But the tallest bar, for 2010, represents 27,000 youth. 26,000 and 27,000 are pretty similar numbers, yet they look reeaaaallllly different. The 26,000 seemed way too short compared to the 27,000.

This graph’s vertical y-axis doesn’t start at zero, so the differences between bar heights are exaggerated.

It’s okay to have a non-zero y-axis. But, it must be labeled.

In the after version, I added labels for 25,600, 26,000, 26,400, and so on.

We don’t want to mislead our viewers. We have to be clear that we’ve intentionally truncated that y-axis so that we could zoom in on that segment between 26,000 and 27,000.

Or, another option is to adjust your vertical y-axis so that it starts at zero.

Then, you wouldn’t need to have any axis labels off to the left of your graph.

The tradeoff is that, now, the bars are all roughly the same height… which might be okay, depending on what you want to emphasize. Maybe you’re trying to show that a consistent number of youth were enrolled in the study sample each year. In that case, starting the y-axis at zero would help you out.

Let’s look at those two “after” versions once more.

Option A, on the top right: If you’re going to start your y-axis at something other than zero, then you need to add axis labels.

Option B, on the bottom right: You can start your axis at zero and forego having any labels.

How have you handled truncated y-axes in your projects?

The post Be Careful with the Y-Axis… appeared first on Depict Data Studio.

Not too long ago, a friend was trying to decide whether to continue renting his current apartment or to purchase a similarly-sized condominium.

• Option A: Continue renting current apartment. Approximately $2,000/month, which includes utilities and amenities. Walkable neighborhood with grocery stores, restaurants, and biking trails. Near public transportation options like a metro stop and multiple bus stops.
• Option B: Purchase a condominium. Approximately $1,700/month, thanks to those darn homeowners association dues. Farther from public transportation, which means extra commuting time and potentially extra commuting costs. Comes with the thrill of purchasing a home and the potential for making money when selling it later on.

During our initial conversations, his decision seemed to be mostly financial—will he save money on a monthly basis by buying a condo? Will he save money over the next 5, 10, or 15 years by buying a condo?

Location is also part of the decision. Rush hour’s no joke in DC, so he wants to stay near public transportation to keep his commuting time and commuting costs low.

Finally, flexibility is part of the decision. He’s a computer programmer and his start-up environment can be unstable. Last year, when one of his company’s apps didn’t sell as well as they expected, the company downsized and he was laid off. He found another job almost overnight but knows it’s possible that his new company could downsize at some point. He’s pretty sure he wants to stay in DC, but has also considered moving to San Francisco.

I couldn’t make the decision for him, but I could help him crunch the numbers.

We made this spreadsheet together over a beer. He wanted to compare costs of renting vs. buying over a 10-year period. A few of our acquaintances have had trouble selling their condos so he knew that, if he chose to buy a condo, it would be a long-term living situation. And of course, we factored in rental costs like gym fees and parking fees and homeownership costs like association dues and home repairs.

My friend stares at computer programming languages all day long but he was in hell with this spreadsheet. Data visualization to the rescue! We mapped out his options in this simple line chart…

…and then formatted the chart.

Notable edits:

• We removed the grid lines;
• We removed the legend and directly labeled the end points of each line (i.e., we inserted a text box so that “Continue renting apartment” was right next to its corresponding line);
• We used the muted color + action color strategy to draw attention to Option B (how buying a condo is the cheaper option);
• We added a so what? title;
• We added a 2-line description underneath the title; and
• We drew attention to that magical 4-year milestone where my friend would break even financially—where the cost of buying a condo becomes a cheaper option than renting.

And which option did my friend choose? After our conversation, he designed Option C: moving to a slightly smaller and slightly cheaper apartment in his same great neighborhood. He was unpleasantly surprised to see how expensive his apartment really was, but he was also hesitant to buy a condo, and hadn’t previously considered swapping one rental for another. The money he’s now saving on rent will go towards his eventual move to San Francisco. Once he compared the costs, he realized that the flexibility slice of his decision making pie was pretty important.

How are you using data visualization for personal or professional decisions? Share your tips below!

The post How Data Visualization Supports Decision Making appeared first on Depict Data Studio.