Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations.

But it can be overwhelming to get started with data visualization. Does data visualization leave you feeling like the numbers are about to topple over on you??

If so, this step-by-step data visualization guide is for you!

I’ll walk you through the data visualization design process so you know what to do first, second, and third as you transform your spreadsheets into data stories.

Step 1: Understand Your Audience

Wait! Don’t start making graphs on your computer! First, we have to do some planning. A little bit of up-front planning will save you hours of blood, sweat, and tears in the long run.

First, we need to consider our audience and context. Who, exactly, is going to be using the data to make decisions?

Here are some discussion-starter questions to talk about with your colleagues.

Who is Your Audience?

A chart designed for a group of foundation program officers will not be appropriate for a group of high school principals, and vice versa.

List all your audience types on a piece of paper, or a whiteboard, or in a spreadsheet, or even on the back of a napkin. Share the list with your colleagues and make sure you’re on the same page.

Have you reached consensus about who you’re targeting with your data?

What’s Your Audience’s Numeracy Level?

Do they enjoy or fear data? Unless you’re designing charts for a group of economists or statisticians, you can usually leave out details like the effect size, power analysis, and margin of error. Laypeople are often more interested in practical significance (the “so what?” and implications of findings) than in statistical significance.

What’s Your Audience’s Data Visualization Familiarity Level?

If they’re brand new to dataviz, stick with the traditional charts like pie charts, bar charts, and line charts—otherwise they’ll spend more timing ooh-ing and aah-ing over the chart’s novelty than paying attention to the information contained in the chart.

How Much Time Does Your Audience Have?

Little time or interest: Simple static chart.

Lots of time and interest: Interactive charts.

What Types of Decisions Does Your Audience Make?

What information do they need? What information do they already have? What information are they expecting? How will your chart(s) add value for them?

If you can’t think of how your chart will add value for the readers, don’t make one. Every chart needs a purpose and so what?

How Much Precision is Necessary?

As the data visualization designer, you have the freedom (and responsibility) to select how much precision is necessary. Your selection should be well thought-out and intentional. Your decision plays out in two ways: the chart type you select, and how you label the data points.

When selecting chart types, remember that some charts are better than others in displaying precision. For example, charts that rely on angles and area to show differences, like pie charts, are for communicating general patterns. Charts that rely on length to show differences, like bar charts, are for communicating specific details.

How Many Decimal Places Are Necessary?

A related decision is how exact your data labels will be. Will you include decimal places? How many?

In most scenarios, you can safely round your decimal places to the nearest whole number. Your audience is rarely using the tenths, hundredths, or thousandths place to make decisions.

Are My Viewers Expecting a Story?

Think about whether your audience is expecting a traditional or storytelling graph.

You’ll learn about the distinctions in this video:

Step 2: Choose the Right Chart

It takes a while to understand all the different chart types and to pick the best one for your desired takeaway message. There are tons of great graphs to choose from!

Consult a Chart Chooser

My interactive Chart Chooser includes dozens of chart types, resources, tutorials, and templates.

New to Dataviz? Start with Classic Chart Types

If you’re not sure which chart to use, stick with classics like the bar chart to compare categories and the line chart to visualize how things change over time.

These charts will be “right” most of the time, so they’re a safe bet.

Use Pie Charts Sparingly

Contrary to popular belief, pie charts are not evil and don’t have to be avoided altogether. I have seven guidelines for using pie charts and donuts. In this pie chart makeover, I show you how to transform a 3D pie chart with way too many slices into a storytelling bar chart with icons:

Getting Comfortable with Dataviz? Branch Out and Try Other Chart Types

Once you’ve mastered the classic chart types, you can play around with less-familiar chart types like bubble charts, bullet charts, dot plots, heat maps, scatter plots, slope graphs, social network maps, tree maps, waterfall charts, and more.

Surround Yourself with Positive Inspiration

Surround yourself with great graphs so you can expand your worldview of what’s possible with data visualization. I suggest following top-notch data journalism teams like @PostGraphics, @NYTgraphics, and @WSJgraphics.

You can even create a physical or digital library of great graphs. For example, you might print full-page, full-color charts and tape them near your desk. Surrounding myself with a variety of chart types, all of which have been used in different reports and for different groups of people, helps me create brand new charts easily. All I do is glance up at my gallery, and then I quickly figure out which chart is best for my new situation.

Dive Into Your Dataset with Exploratory Data Visualization Techniques

I also use exploratory computer strategies, like Microsoft Excel’s spark lines, data bars, and conditional formatting, to help me narrow down the focus of my charts.

Spark Lines

Here’s a tutorial that shows you how to get started with spark lines:

Data Bars

And here’s a tutorial that shows you how to get started with data bars:

Conditional Formatting

You can set up rules in your spreadsheet that automatically change the color of certain cells based on their values. I regularly use heat tables to scan my dataset for patterns. You can follow my step-by-step tutorial to make heat tables for your data.

Sketch Rough Drafts on Paper

Step back from your software program. This is especially crucial if you’re using Excel or R (versus Tableau) where you usually need a solid idea of your chart’s design before implementing that design on the computer.

I sketch, draw, and doodle plenty of drafts before I create anything on the computer.

Here’s how it works: First, sketch plenty of rough drafts on paper. Give yourself permission to doodle as many drafts as you need. Share drafts with colleagues early and often. Gather as much feedback as you can. Next, create one or two of those promising drafts on the computer. Finally, edit, edit, edit! Put your easiest-to-follow chart in your final presentation or report. You might sketch five or more drafts. Only the single best chart will survive the editing process.

Step 3: Select a Software Program

Once you’ve got a rough mental idea of what your visualization might look like, sit down and build the first draft of your visualization on the computer.

There are dozens of software programs available for building data visualizations. Some are free. Others are low-cost. And others are quite costly, at least for smaller organizations.

I’m software-agnostic at my core, meaning that I don’t care which program you use. You can create great — or terrible — graphs in any software program.

That being said, 99% of my data visualization consulting is done in Microsoft products: Excel, Word, and PowerPoint. Those are the common denominator for the companies that hire me. I’d never create a dashboard in a specialty software program… if you don’t also have access to it and know how to use it. It would be useless!

Here’s an example of an interactive dashboard made in good ol’ Excel. You can learn how to make these, and many other types, inside my Dashboard Design online course.

Step 4: Declutter

After you’ve got the first draft of your data visualization created on the computer, it’s time to refine your visualization and make your message shine. No computer program is perfect. You’ll have to roll up your sleeves and make intentional edits no matter which software program you’re using. The very first edit I make is to declutter my visualization. Software programs come with way too many borders, lines, and unnecessary ink. Examine each and every speck of ink on the chart. Does it have a specific purpose? If you can’t articulate a reason for that ink, you don’t need it.

Apply the Squint Test

In these before scatter plot on the left, the cluttered appearance distracts us from the data. All these extra lines make the charts look overly scientific—and outdated. In the after version on the right, I removed the background shading and borders. I kept the x and y axes and some of the grid lines, but I intentionally changed the black ink to gray ink.

How do you know when you’re done decluttering? Apply the Squint Test. Here’s how it works: Squint your eyes so that you’re peering at the chart through your eyelashes. Everything should look a little blurry. Can you see the overall shape of the data? For example, you should be able to tell if a line chart is jutting upwards or downwards over time. If not, try removing more clutter.

Outline Shapes in White

You’ve got the gist of decluttering. Now, let’s fine-tune!

Sometimes reducing clutter means outlining shapes in white, rather than black, so that they match the chart’s background color.

Step 5: Clarify Your Message with Color

There are three goals for color:

1. Branding (Using your company’s colors, which saves time and helps you look professional)
2. Accessibility (Making sure your colors pass official guidelines so they’re legible for people with disabilities, like ADA/508 compliance in the United States)
3. accessibility (Using colors to make the graph feel intuitive)

Brand Your Visuals with Custom Colors

I’m begging you! Do not use the default colors from Excel, Tableau, or Google Charts. Nothing screams novice! or 2002! more than default color schemes. If you’re designing charts for a report, handout, or presentation for a client, use their color scheme. Consultants, this means the report will look like it came from the client. It will not have your firm’s look and feel.

In this example, Johanna Morariu and I were designing a slidedoc for the Working Families Success Network. We began by investigating the Working Families Success Network’s logo, website, and publications. Their logo has a distinctive blue, orange, and pink and their publications use dark gray text rather than black. Throughout their website they use color blocks with white text and white outlines. Next, we adapted that layout and color scheme for our slidedoc. The images on the right are separate slides (pages) of the report.

You can locate custom color codes in style guides, with a free eyedropper tool, or even with Microsoft Paint. Then, enter your custom color codes in Microsoft Excel or in Tableau.

Make Sure Your Colors Are Legible for People with Color Vision Deficiencies

Here’s how:

1. First, by proactive and avoid using red-green color combos.
2. Second, make sure you directly label your data.

Although we’re used to seeing legends, we rarely need them. Legends can lead to unnecessary zig-zagging around the screen or page, and legends can also be difficult to interpret if your graph is printed in grayscale.

Instead of using legends, directly label the data. Direct labels mean that you add labels as close as possible to the data. For example, in a line graph, you would delete the separate legend and place the category labels off to the right of each line. For bonus points, color-code the text in the labels to match the line.

This is what direct labels look like:

Then, you can upload your draft to www.color-blindness.com’s Color Vision Deficiency Simulator to get a preview of what it’ll look like for people with protanopia and deuteranopia.

Emphasize the Takeaway Message with the Action Color

When you want to tell a story with data, you can guide your viewer’s attention to your desired takeaway finding by creating a dark/light contrast. This example comes from one of my graduate school projects a decade ago, so I used the exact shade of green from my university’s logo. Then, I used dark green to draw my audience’s attention to a couple key parts of the slide. This slide comes from the fourth section or chapter of the presentation, the Limitations section, so that tab was highlighted in dark green so that it contrasted with the other tabs, which are in gray. The topic of this particular slide was Brevity of open-ended survey responses, so that text is in green so that it stands out against the rest of the text. And the box-and-whisker plot itself also uses dark green.

Step 6: Clarify Your Message with Text

It’s hard to get wording just right, so I usually save my titles, subtitles, and annotations for the end.

Brand Visuals with Custom Fonts

Rather than using Microsoft’s plain ol’ Calibri, make sure your visualization’s fonts match the project’s branding.

Write the Takeaway Finding in the Graph’s Title

Need to tell a story with data? Rather than using a generic title (“Figure 1” or “Number of youth served”), state the takeaway message in the title.

I first learned about this technique through Cole Nussbaumer’s Storytelling with Data workshop back in 2012—but geez, was it tough to apply! This is one of the hardest practices for social scientists to learn because we’re so comfortable with APA formatting and its generic figure titles.

Think Twitter-like and aim for six- to eight-word titles. Look to newspaper articles for inspiration; journalists know how to include the “so what?” in their title. You may or may not read the full newspaper story for additional details. Same thing with charts: your audience may or may not read your full chart, so your title must give them the gist of your findings.

Add Context with Annotations

Annotations are call-out boxes that provide important contextual details. In PowerPoint, Word, or Excel, you can easily create annotations by inserting a text box. No fancy software required!

Here’s a great example from Mother Jones. A generic title would’ve been “Number of children living in poverty” or “Relationship between poverty and geographic location.” This 6-word title, “In Climbing Income Ladder, Location Matters,” ensures that readers grasp the chart’s message instantly. A 2-line caption adds more details underneath the title, and a few cities are annotated. The tweet’s text also reinforces this message.

This is how likely poor kids are to grow up and move out of poverty based on where they live http://t.co/5A5VIZkLBN pic.twitter.com/7BBZQJ9bdg — Mother Jones (@MotherJones) January 31, 2014

Establish a Text Hierarchy

Size your fonts according to their importance. A text hierarchy tells your viewers which information is most important (headings) and which information is least important (the regular ol’ paragraphs). In this example, I transformed a university’s annual report simply by adding an intentional text hierarchy. I call this makeover a two-hour turnaround because these are changes that anyone can make in two hours or less. Before, all the font was the same size, so the headings didn’t stand out. The report looked like a sea of words. After, we made the headings stand out by with larger fonts and by overlaying the text on top of a photograph. We also used a different color for each section to break up the sea of words into manageable chunks.

Lower the Reading Level

The vast majority of reports, handouts, infographics, dashboards, and slideshows that I review with clients are written at a reading grade level that’s so high that reading the documents feels like homework. In this example, we assessed our draft’s reading grade level with a free tool called readable.io. Then, we re-worded the title so that it was a closer match for our intended audience.

Finally, go share your chart!

You’ll need to edit it slightly depending on the medium — a chart for a presentation should look different than a chart for a dashboard. You can learn about presentation-specific, dashboard-specific, and report-specific techniques.

Learn More

Sign up for my free online course called Soar Beyond the Dusty Shelf Report. There are several quick lessons that help you get started with data storytelling.

Or, contact me about online courses, private workshops, and conference keynotes.

The post The Data Visualization Design Process: A Step-by-Step Guide for Beginners appeared first on Depict Data Studio.

Here’s my definition of data storytelling, in case you missed the previous blog post. 

Great!  

Let’s remove the guesswork from our graphs. 

The next step is to figure out which message we’ll highlight. 

We can’t visualization everything—that dilutes the power of our graph. 

What Makes a Useful Data Story? 5 Questions to Ask

Here are five thought-starter questions to help you uncover useful nuggets in your data.  

1. What’s Going Well? What’s Not Going Well? 

2. Did We Reach Our Goals? Why or Why Not? 

3. What’s Surprising? What Unfolded as Expected? 

4. Which Information Needs to be Shared with Others? Who Needs to See This Information? 

5. What Increased Over Time? Decreased? Stayed the Same? 

If you analyze data for a living, then I bet you’re already asking yourself these questions. You were probably trained to approach data this way in grad school. Or, it’s become intuitive after years of on-the-job practice. 

Dabblers in data, this one’s for you.  

Newcomers to data, this one’s for you.  

What’s Going Well? And What’s Not?

Everyone loves a success story.  

Look through your dataset.  

Find the good news and highlight that finding with dark colors and takeaway text.  

I often start with positive findings so that my audience can celebrate a small victory right away. 

But, facilitating an honest conversation with data visualization requires balance.  

After presenting positive news, I present the less-than-positive news.  

For example, the next graph in my report might use a darker color to draw attention to something that isn’t going well. 

Did We Reach Our Goals? Why or Why Not? 

I consult to dozens of grantmakers and grantees each year—Federal, state, and local government agencies, foundations, and nonprofit organizations.  

In the grantmaking world, it’s common for funders to ask their grantees to explain whether they are meeting their targets.  

For example, one goal of a parenting program for teenage mothers and fathers might be to avoid repeat pregnancies. The health centers and high schools that are running the program might have to report whether there was, in fact, a decrease compared to a control group.  

Graphing these goals is an obvious choice. 

What’s Surprising? What Unfolded as Expected? 

Take off your data nerd hat. 

Put on your human hat.  

Step outside the math for a bit.  

Trust your gut instinct.  

I look for numbers that are surprising and unexpected.  

What’s surprising to you, personally?  

Surprising new facts make for interesting reports.  

Nobody wants to read the same old stories over and over and over.

Which Information Needs to be Shared with Others? Who? 

This thought-starter question keeps your data actionable.  

Examine your numbers.  

Who, in particular, needs to see these numbers?  

Think about all the different people who are involved in your project.  

Are there certain takeaway findings that your boss should probably know about? Or the boss’ boss? Or someone outside the organization?  

Who might act differently or make a different decision based on this new information?

What Increased Over Time? Decreased? Stayed the Same? 

Most projects have numbers available at multiple points in time.  

Examine how your numbers are changing over time, if at all.  

Sometimes a number will increase over time. Other times, a number will decrease over time.  

And other times, you might not notice any difference whatsoever. Flat lines can be useful, too! 

Data Storytelling Example: Highlighting a Flat Line in a Workforce Development Project 

I changed around the details, but this example is based loosely on a past project.  

Let’s pretend that you’re leading a career coaching program for adults who recently immigrated to the country. I consulted on a project like this a couple years ago.  

The purpose of the career coaching program was to get those adults into higher-paying jobs.  

A few times a year, the career counselors collected data on the participants. For example, they asked the participants how much they were being paid. The career counselors might even verify their wages by looking at pay slops or tax forms.  

The person responsible for compiling all this data should see whether wages are improving, declining, or staying steady.  

Imagine that you uncovered that wages for most program participants were staying steady—despite hundreds of thousands of tax dollars being poured into this program. 

That flat line has to be shared and talked about! Something needs to be adjusted ASAP. 

Your things-stayed-steady-over-time graph might look like this. 

We applied several data storytelling techniques. I bet you recognized them right away: 

1. We’ve got color contrast (all 30 participants’ individual lines are grayed out, and the average is highlighted in a darker brand color). 

2. We’ve got a takeaway title (“Wages Did Not Increase”). 

3. We’ve got numeric labels on a handful of key data points (the $18.27 average wage at the beginning, and the $18.30 average wage at the end). 

4. We’ve got (light) narrative annotations (“Average hourly wages: $18.30”) 

Looking for Useful Stories throughout the Analytical Process 

When do you look for possible data stories? 

Not the day before your project’s due!!!!!!!!!!!!!!!!!! 

Revisit these questions at each stage of your project’s analytical process. 

Look for Useful Stories in the Raw Data 

I start with my spreadsheets of raw data.  

I ask myself, “What’s going well? Did we reach our goals? What increased over time? What’s surprising? Which information needs to be shared with others?”  

I keep a running list of interesting nuggets in a notebook. 

Look for Useful Stories as You’re Compiling Tables for Your Appendices 

Later, I compile my analyses in tables. The tables often go in the appendix of a technical report.  

This means that I write the last pages of my report first.  

As I’m designing the tables, I ask myself those five questions again, and I add to my running list. 

Look for Useful Stories as You’re Designing Your Full Reports or Slideshows 

Next, I write my report (or create my slideshow, or whatever the finished product will be).  

I look through the tabulated data as I’m designing the report: Which numbers deserve to go into the body of the report?  

Look for Useful Stories as You’re Designing Summaries (One-Pagers, Infographics, Briefs, etc.) 

Finally, when my full report/slidedeck is complete, I pull out graphs that are so interesting that they deserve to go in a summary.  

I’m using the word summary loosely here.

A summary could be a one-page handout, an infographic, a shorter brief, etc. 

Yes, this is the place for those stories to shine.

Yes, you should’ve found stories along to way to include in your summaries. Hopefully!!!

This stage gives you one more chance to think carefully about useful gems in your dataset.

Don’t wait until the end of a project to think about the “so what?”  

This should be an ongoing, intentional process.  

When we think deeply about the data, our audiences will benefit from the added clarity. 

Your Turn 

What’s your process for uncovering interesting stories in your data? 

Do you have more thought-starter questions to add to the list? 

The post What Makes a Useful Data Story? 5 Questions to Ask  appeared first on Depict Data Studio.

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.

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For the past few months, I’ve been developing dashboards to support students’ understanding of local ecology and equip them to use that local understanding as a baseline to explore the rest of the world.

Imagine, for example, being a 10-year-old in St. Louis.

Your neighborhood has plenty of trees since you’re at the western edge of the forests typical of the eastern US. This can only happen because the temperature is warm enough and there is enough precipitation to support tree growth.

Heading west from here the ecology shifts pretty quickly to grasslands, with the grass getting shorter as you approach the Rocky Mountains. A quick look at the data shows decreasing levels of precipitation as you head west.

For a more extreme contrast, Yuma, Arizona is much hotter, but the area gets about 10% of the precipitation St. Louis does. How is this heat and lack of precipitation reflected in the plants and animals in southern Arizona?

All of this learning is wrapped under the heading “What’s It Like Where You Live?” – a program I used as a 4th grade teacher 25 years ago, developed by the Missouri Botanical Garden and now undergoing a major reworking.

As we flesh out the curriculum, we’ll be supporting kids’ local field work with dashboards synthesizing climate data and images of plants and animals typically found in different ecoregions.

First Forays

At a basic level, students can compare temperature and precipitation data for their local community with data for other cities around the world. Is it warmer or cooler, wetter or drier?

A simple table or a scatter plot serves the purpose quite well. The limit in this approach is the image students often develop when data from one city represents “the desert” or “the rainforest.”

Resolving this conundrum has opened the door to some exploratory work crafting dashboards which encompass both similarities and variation within an ecoregion. (Mostly I’ve just been geeking out with the data, but “exploratory research and development” sounds so much better!)

Refinements

Taking the data visualizations further has pushed me to walk a fine line between interesting visualizations and the developmental capacities pre-teen students bring to the task.

Most kids have limited experience with data tables and graphs, and what work they have done is pretty specific (such as graphing pizza preferences among class members).

Graphs showing means (or even means of means) risks becoming too abstract without the right supports.

After exploring a few options, I settled on a representation which captured both the spread of data typical of cities in a given ecoregion and the mean value of these cities.

Major thanks are due here to Ann Emery for streamlining the look and feel of this version. Her focused, uncluttered design aesthetic is a perfect match for this work.

I’ve tested this out with a few kids with good results, but COVID restrictions have kept me from seeing how a broader pool of students make sense of this display. I’m hopeful that restrictions will be lifted in the new school year so we can move forward with some pilot testing.

Going Further

To be sure students remain connected to their local base, I needed an anchor which is ideally movable so that students in other areas can use the materials.  For this, I’m indebted to Jon Schwabish of the Urban Institute and PolicyViz.

While participating in a workshop he led, a couple of techniques we were using came together. By combining a single point scatter plot and error bars, a reference line can be inserted to mark local conditions.

If this strategy proves useful in our pilot testing, I expect that we will be able to support localization so that students anywhere could enter their own data and have VLOOKUP or a similar procedure to change my St. Louis reference line to one appropriate for any student’s home city.

The work so far has been an enjoyable way to explore data and apply the many things I’ve learned in Ann’s workshops and elsewhere. I’m looking forward to seeing how students use the data when we begin pilot testing. 

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Before

Here’s the “before” graph from a recent project. The categories and numbers are different, but you get the idea. Most stacked bars look like this—there’s too much going on for the graph to be useful.

After

At a bare minimum, we need to declutter the default graph. You need to:

• remove the border;
• remove the vertical grid lines;
• declutter the horizontal axis (0 to 100 percent is plenty of detail—no need to label each of the 10 percent increments);
• move the legend to the top (since the legend is critical for understanding the graph);
• re-color the words in the legend to match the colors in the graph;
• declutter the title (short and sweet for slideshows, please);
• add numeric labels;
• outline the rectangular shapes in white (so the colors don’t bleed together); and
• reduce the gap width.

Check out the click-by-click breakdown:

Storyboard for a Live Presentation

The decluttered version is easier to read… but it’s still too dense for a slideshow. Here’s how we can “declutter” the graph so that our audience can follow along without wanting to bang their heads against a wall.

First, show the “completed” slide and provide a brief overview. I would say, “Let’s talk about our market share compared to our 4 competitors. We’re looking at five different products, A, B, C, D, and E.” You’d only spend a few seconds on this slide.

Second, dive deeper in the details. Focus attention on the ABC Org with dark colors. I simply changed the other segments of the stacked bar chart to an 80% transparency. I also selected lighter colors for the legend. You would say, “Here’s how we’re doing. Check out Product C–that’s where we hold 80% of the market share.”

Third, focus attention on Competitor 1 with dark colors. You would say, “Here’s how Competitor 1 is going. They hold 30% of the market share in Product A, but they don’t offer Product C at all.”

Fourth, focus attention on Competitor 2. You know the drill! You would say, “Competitor 2 holds a smaller piece of the market—between 7 and 13 percent for these five products.”

Finally, conclude your storyboarding with the “finished” slide again. Invite your audience to ask questions and open the floor to discussion. You would say, “Here’s that same overview again. I won’t go through Competitor 3 and Competitor 4 in detail, but you can see how they’re doing, too. Next, let’s talk about what these numbers mean for our future work…”

Storyboarding–guiding your viewers through one piece at a time with dark colors–guarantees that your audience will be looking at the data, and not scrolling through their phones. How are people supposed to make decisions based on your data if they’re not even paying attention to you? Break up dense visualizations into multiple slides. Your audience will thank you.

Bonus! Download the Materials

Want to explore how I edited the graph? Download the spreadsheet.

Bonus! Watch a Sample Class

I’m teaming up with 13 guest experts to bring you Great Graphs, an online course about getting your data out of spreadsheets and into real-world conversations through better data visualization, reports, slideshows, and dashboards.

Want to see what the storyboarded slides would look like and sound like? Watch a sample class:

Great Graphs begins October 1. The course only opens once a year and we’re only able to take 100 students. Reserve your spot today before they’re gone!

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Before

This attendee worked at a large university library and was responsible for writing an annual report about the library’s operations and accomplishments. The report was full of tables, graphs, and photographs.

This is a screenshot from the beginning of the report that offered background information about the library, like how many visitors came to the library each hour and each day. Library headcounts inform decisions about staffing and about future library hours. For example, if they find that library attendance peaks in the morning, then the university might decide to open the library earlier to accommodate their visitors’ preferences.

After: Reenvisioning the Individual Graphs

The first graph was about average hourly headcounts—how many students, staff, and other visitors were present in the library at any given hour in the day.

In this makeover, we:

• Decluttered the graph by removing the border, tick marks, and horizontal x-axis line;
• Decluttered the vertical y-axis by removing all of the labels except for the smallest value (0 visitors) and largest value (50 visitors);
• Decluttered the horizontal x-axis by only labeling a handful of key hours;
• Nudged the columns closer together (here’s a tutorial on shrinking the gap width) so that viewers could see the smoothed-out shape of the graph rather than the individual columns;
• Applied a mix of darker and lighter colors; and
• Wrote an annotation above the graph to highlight the key finding. This takeaway—that the library’s peak hours are between 10am and 7pm—was hiding in the paragraph above the graph. I wanted to make the report skimmable.

Here’s the second graph, which is about average daily headcounts. I decluttered the graph and then brought their key message into center stage with dark colors and an annotation.

After: Re-envisioning the Page as a Whole

Next, we had to think creatively about the page as a whole. How would we arrange the graphs on the page? How big or small should we make each graph? How would we re-write the existing paragraphs?

While editing the page as a whole, we:

• Adjusted the report’s text hierarchy by making the Heading 1 and Heading 2 text large, bold, and dark.
• Re-wrote the introductory paragraph and moved some of those sentences closer to their respective graphs. The sentences about daily headcounts belong next to the graph about daily headcounts.
• Wrote graph titles. I usually advocate for storytelling titles that explicitly state the graph’s desired takeaway message. But in this makeover, I decided that annotations—Peak Days; Monday through Thursday—would be just as powerful. I didn’t want the graph’s storytelling title to be redundant with the graph’s annotation, so I combined a generic title with a storytelling annotation.
• Added icons because icons can make our graphs more memorable; and
• Paid careful attention to alignment. The words are left-aligned. The graphs are aligned with each other, too. You could draw a single vertical line from the top graph’s y-axis down to the bottom graph’s y-axis. It took a few minutes to get the spacing just right, but alignment is always worth the extra time because it makes the finished product look more professional and purposeful.

Here’s the full before/after data visualization makeover:

Bonus

Would you like to explore the Excel file, Word document, and PowerPoint slides that I used to create this makeover? Purchase the materials and use them as inspiration for your own projects.

Purchase the Templates ($5)

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