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A couple years ago, I was invited to be the keynote speaker for the Continuous Quality Improvement Conference in Illinois. (And a couple weeks ago, I keynoted their conference in California, too. What a great group!)

While planning for the session, I asked conference attendees to submit examples from their reports, dashboards, and slideshows that I could makeover as part of the talk.

Later, during the live keynote, I shared a few data visualization principles. Then, as a group, we practiced applying those principles to their real projects.

Here’s one of my favorite submissions:

This conference attendee worked at an organization that placed children into foster care homes. Each year, the organization surveyed their foster care parents to gather their feedback about the experience.

Virtually every organization conducts satisfaction surveys of one kind or another, so even if you don’t work for a foster agency, keep reading!

I’m going to show you the before version followed by five makeovers.

The first makeover didn’t work. The second makeover didn’t work. The third makeover didn’t work. The fourth makeover didn’t work. Just as I was about to give up, I found a winning design with my fifth attempt!

Let’s make fun of my first few attempts together. Then, we can celebrate the fifth attempt together.

I’m going to provide a behind-the-scenes peek into my thought process so that you can apply my thinking to your own projects.

What’s Already Working Well: Length and Context

A couple things were already working well in the before version.

First, I was pleasantly surprised to see that they fit all 22 survey questions and the responses on a single page. They wanted an at-a-glance handout, not a full report. All too often, I witness organizations drone on and on about simple survey results. It’s just a survey. Keep simple things simple, please.

Second, I was pleasantly surprised to see two years’ worth of data included: fiscal year 2016 and fiscal year 2017. All too often, I see annual survey results that only provide the current year’s data. Without historical context, we don’t know whether the current year’s data is any better or worse than previous years. Providing patterns over time is always a good thing.

What’s Not Working: Clutter, Order, Clustered Bars, and Analysis Approach

We’ll declutter the one-pager, obviously. We need to remove the gray background shading. How are the foster agency’s leaders supposed to make decisions based on this data if they can’t even see it?

We’ll also re-order the survey questions. Right now, questions are listed in the order they were asked on the survey: 1, 2, 3, and so on. Presenting survey results in the same order as the survey is rarely the best approach. Instead, we’ll group the individual questions into categories.

Next, I wanted to find an alternative to the clustered bars. Clustered bars are my least favorite chart of all time—even more so than 3D exploding pie charts! Clustered bars aren’t inherently evil. They’re just overused.

Finally, the analysis approach was a bit off. The agency asked foster parents whether they were completely satisfied, very satisfied, satisfied, not very satisfied, or not at all satisfied. They coded a completely satisfied as a 5, a very satisfied as a 4, and so on. Then, they calculated the average score. For example, Staff are courteous and respectful got an average score of 4.5 in 2017.

Although this numeric coding approach is common, it’s not correct. Variables can be nominal (favorite ice cream flavors), ordinal (this satisfaction scale), interval (the scale’s points are equidistant), or ratio (the scale has a true zero, e.g., a height of 0 feet means zero height). If this is the first time you’re learning about nominal, ordinal, interval, and ratio scales, check out this article to learn more.

You can only calculate averages on interval or ratio scales, but the survey has an ordinal scale. In other words, the agency should’ve displayed how many foster parents selected completely satisfied, very satisfied, and so on for each of the questions instead of calculating an average score.

I didn’t have access to the raw dataset while designing this makeover, so I’m going to have to display the average scores here. It’s not the end of the world. But I did cringe during the makeover process. And I’m definitely cringing again during the blogging process.

Makeover 1: Slope Graphs Didn’t Slope…

I experimented with a few makeovers before I settled on a winning design. Here’s the first makeover. Wait! Before you start frowning and rolling your eyes, hear me out. Let’s look at what’s working, and then I’ll be honest with you about what’s not working.

I kept all of the makeovers to a single page, which was a fun challenge. I kept two years’ worth of data. I dramatically decluttered the page, removing the background shading, vertical lines, and horizontal lines from the original. And I grouped the survey questions into categories and then color-coded by category (Staff in turquoise, Clients in purple, Caseworkers in red, and so on). You can learn more about color-coding by category here.

I love most aspects of this redesign.

The major shortcoming of this data visualization makeover is the visualization itself—darn!

We have a few options for comparing two points in time, like these two fiscal years. The most obvious choice is a line chart, which was born with the sole purpose of displaying patterns over time. A slope chart is just the line chart’s cousin; it displays exactly two points in time.

The problem is that the slopes didn’t slope. The visuals were too short to show much of a difference.

Most designs require compromise. I decided that it was more important to limit the makeover to a single page than to increase the height of each slope chart (and, therefore, spill the survey results onto a second page).

Makeover 2: Columns Were Too Short…

Here’s my second attempt.

In this iteration, I visualized the data with column charts.

You can already see the problem, right? The columns were too short to see any differences.

If I didn’t tell you that these were supposed to be column charts, then you might’ve assumed they were just funny-looking squares.

I’m not upset that this makeover didn’t work. I wasn’t rooting for the clustered column approach anyway!

Onwards. I’ve still got a few more ideas up my sleeve…

Makeover 3: The Heat Table Was Too Colorful…

Heat tables are helpful when you’re working with limited space, like my self-imposed rule of limiting myself to a single page, just for fun, ha! The colors live on top of the numbers, not beside them, so they take up less space.

I can generally see that the FY17 column is darker than the FY16 column (ratings were higher in FY17 than in FY16). But I have to work to see the darker colors because I’m distracted by the rainbow in front of me.

If I wasn’t stubbornly devoted to a one-page design, then I could’ve added an empty row between the categories. For example, you’d see the turquoise section for staff. Then, there would be a centimeter of white space. Then, you’d see the purple section for clients. But again, every design is a compromise. I was committed to a one-page design. I couldn’t turn back now!

Makeover 4: Check Boxes Provided Overly Positive News…

As I was critiquing the heat tables, I finally realized that the FY17 results were better than the FY16 results. I hadn’t actually noticed that pattern while looking at the original, at my slopes, or at my columns! Spotting this pattern was a game-changing aha moment.

In this redesign, I opted to focus on big-picture results: that foster parents scored the agency higher in FY17 than in FY16 on every survey item except one. The filled-in squares and empty-squares are easy to scan at a glance. I’ve used square icons in dozens of real-life projects, and I’ve talked about them a few times on the blog before, too. You can read this post to learn how I created them. The filled-in square is a lowercase g in the Webdings font and the empty square is a lowercase c in the Webdings font.

The downside was that the check boxes provided overly positive news.

Can you have too much good news? I think so. Pretend that you’re a leader at the foster care agency. You see this handout at a meeting. You’ve improved from one year to the next on nearly everything! This is great news! There’s nothing to fix! Everything’s working! Except for that one survey question, but that’s just one thing, so who cares! No need to try any harder next year! You’re already doing everything perfectly… or are you?

I didn’t want to encourage complacency. I wanted to provide actionable ideas for improvement.

Makeover 5, the Winning Makeover: Deviation Bars

Finally! The winning makeover! It only took four failed attempts to get this one right…

I loved the simplicity of the check boxes. But I was afraid that they only provided good news. I needed to strike a balance: Keep the makeover simple while providing details about where the agency could do even better.

I created deviation bars to show the size of the difference from one fiscal year to the next. I intentionally re-ordered the survey questions yet again. Within each category, the survey questions are ordered by the magnitude of their improvements.

At a glance, you can still see that all but one survey question improved. But now, you can also see how much or little improvement took place.

It’s good to provide leaders with good news, but it’s better to provide leaders with balanced news.

Now, they can still celebrate all the areas where they improved. Then, it’s time to roll up their sleeves and get to work on improving even more.W

Bonus: Download the Materials

Want to see how I created these five makeovers? They all live inside Excel!

That’s right, I typed the survey questions into Excel, created all of the visuals within Excel, and then PDF’d my screen so that I could share the handouts with leaders.

Purchase the files to learn more and to adapt the templates for your own work.

Purchase the Templates Once-a-Year Enrollment for My Data Analysis Course is Now Open!

Registration for Simple Spreadsheets: From Spreadsheet Stress to Superstardom is now open!!!

This online course is all about data analysis time-savers. Spreadsheet skills are the foundation of good data visualization. You need to be able to pour through huge datasets and find the gems that are worthy of visualizing. You need to manipulate your tables, rows, and columns to get formatted juuuuuust right. And most importantly, you need to be able to analyze data quickly and without mistakes.

I’ve spent the past 10 years teaching myself all of these tricks. I took classes. I checked out books from the library. I searched through hundreds of (really bad) videos. I learned the hard way so you don’t have to. Simple Spreadsheets is a shortcut. I’ll teach you what it took me a decade to learn on my own.

Want to enroll? Register today before seats sell out. There are only 50 spots! Last year, the course sold out in just 2 weeks.

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It’s 3.14—Happy Pi(e Chart) Day!

If you’ve read this blog before, or heard me speak, then you know that designing data visualization makeovers is one of my favorite activities of all time.

I love redesigning pie charts, in particular.

The vast majority of data visualization trainings just advise people to stop using pie charts… without teaching people what to do instead.

Every year, I work with dozens of organizations, and every single organization still has pie charts sprinkled throughout their reports, slideshows, dashboards, and infographics.

Hearing that you’re supposed to avoid 3D exploding pie charts with a hundred tiny slices is beginner-level stuff. I need to train you on useful alternatives–that’s the advanced-level stuff.

I created this before/after pie chart makeover a million years ago but forced myself to wait until March 14 to share it. Phew! It’s been a long wait.

Before: Two Pie Charts

I recently worked with a grantmaking organization. They awarded grants to support various research projects. For anonymity, I’ve changed the names of the research projects to A, B, C, etc.

The grantmaking organization simply wanted to look for patterns in their funding over time.

Their “before” version looked like this:

Two Slices Only

A major guideline for pie charts is that they’re easiest to read with only two slices.

At the very least, we’d need to collapse the seven slices into just two slices. For example, you may choose to focus on research topic A with a dark-light contrast.

Dark-light contrast is helpful, but it’s not enough. We have to keep editing.

Don’t Make Viewers Zig-Zag Their Eyes Across Different Graphs

Another guideline—for all charts, not just pies—is that you don’t want to make viewers make comparisons across multiple charts.

If you want them to make comparisons, then put those things next to each other, not several inches apart from each other.

Here’s how my eyes have to zig-zag back and forth to read the original:

The collapsed, two-slice version still requires zig-zagging eye movements:

Let’s make the comparisons faster and easier.

The grantmaking team and I put our heads together. We came up with a few alternatives.

After: Stacked Columns

Being researchy types, the first alternative we came up with was stacked columns. Stacked columns are just rectangular versions of pie charts.

There’s nothing inherently wrong with stacked columns. But, yikes! This alternative felt way too busy. There are seven segments in each column, which is too many…

… unless…

…. you use dark-light contrast to focus your viewer’s attention one just one segment at a time.

After: Slope Graphs

Since we’re comparing patterns over time, how about a slope graph?

A slope graph is a fancy name for a line graph that has exactly two points in time.

Spaghetti Slope

Here’s what we tried:

Yikes! We had good intentions, but we accidentally created a spaghetti slope graph.

When this happens, don’t fret. It’s not your fault. It just means your particular dataset had percentages that criss-crossed and overlapped too much.

Spaghetti Slope with Highlighting

There are a couple ways to detangle spaghetti graphs.

One option is to guide viewers’ eyes to just one thing at a time with dark-light contrast:

Which line(s) will you highlight?

Use your best professional judgment.

Think about your unique audience. You might highlight something that increased, like B. Or you might highlight something that decreased, like D. Or, you might highlight something that remained steady over time—perhaps that thing was supposed to go up or down, but didn’t, and you’ve got an interesting story to discuss.

Small Multiples Slope

Another way to fix a spaghetti graph is with a small multiples layout.

Small multiples means multiple small charts.

You could produce seven mini charts, one for each of the seven research topics.

But better yet, let’s group them into categories that will give our viewers more insights into the patterns—the fact that some research topics received more funding while other topics received less funding.

I color-coded by category (one color for increases, another color for decreases).

Finally, I added icons to boost memorability.

This one’s my favorite. Swoon.

After: Dot Plot with Arrows

I intentionally sorted the categories into those that increased and those that decreased. It’s kind of like a small multiples dot plot.

I also included arrows, instead of just regular ol’ circles or dots, to reinforce the direction of the changes.

I color-coded by category (increases in one color, decreases in another color).

Finally, I added icons to boost memorability.

Join the Conversation

Team, you know the drill! Comment and let me know which alternative is your favorite and why. I’m personally drawn to the small multiples slope graph.

Bonus! Download the Materials

Purchase the spreadsheet that contains these graphs.

Purchase the Materials

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I always begin my presentations by making fun of myself and my past projects.

I show one of my dreadful graphs, reports, or slideshows from ten years ago, before I knew better, and then show participants how I’d edit that graph today to make it more effective.

I want to set the tone immediately that everyone makes mistakes and it’s no big deal. Laugh at yourself and move on. Even the supposed guru in front of you has used 3D distortions, red-green color palettes that are illegible for people with color vision deficiencies, and tiny Times New Roman text in barely-understandable technical gibberish.

I have five different examples that I cycle through depending on the audience: I might show a past peer-reviewed journal article disaster for an academic audience, or an evaluation disaster for an evaluation audience, or a foundation disaster for a foundation audience, and so on.

Regardless of the specific example I choose to share with each audience, the message is the same: No shame, no blame. Laugh at your past data visualization mistakes, and then move on and just focus on making improvements to the projects you’re currently working on.

I’m not the only data visualization designer with skeletons in my closet. We’ve alllll made mistakes. In this post, I pulled some of my friends together and asked them to share their past work, too. They were great sports and had fun laughing at themselves along the way, too.

And more importantly–look how far they’ve come!!! What a difference a few years of practice (or ten) can make in your own learning journey.

Kylie Hutchinson
Kylie says, “This was fun looking back at my files… Both are from 2009.”
Then
“The first is a table, one of 10 in a report. I can’t believe I didn’t make a chart instead. I didn’t even order the magnitude of responses, sigh.”
“The second is, of course, a bad pie chart…  I also have a very vague memory of having trouble getting the slices to orient the way I wanted them to and I think I eventually said, forget it.”
Now
Kylie is so humble that she initially only shared her “before” examples with me. I knew her work had evolved significantly, so I asked for her “after” visualizations. You’ll be happy to see that Kylie traded in her pie charts for slope graphs that use dark/light contrast and storytelling titles!
Want to learn more from Kylie? She literally wrote THE book on failures, along with another guide on program sustainability that I’ve heard is great. Not an affiliate, just a fan.
Echo Rivera

Each says, “That time I thought SmartArt was the best design option…”

Then

“This is my masters thesis defense from 2010. I think this was the first time I tried to make my own slide template, which involved adding some speech bubbles to a square and pasting it on all the slides. And, as you can see, I was a huge fan of SmartArt back then. It also has an enormous amount of text on the slides and, generally, does not look all that visually appealing.”

Now

“Luckily, I didn’t stop there! I continued to experiment and learn how to design highly visual presentations. This is a section of a workshop I created in January 2019—it’s one of my most recent presentations. As you can see, it’s much more visual and has significantly less text on each slide. It also has built-in moments for audience engagement and is, generally, a lot of fun to deliver and a blast for my audience. I now love designing presentations so much that I have a website and courses to help others make the same transformation in their slides.”

“For more of my old slides, and to check out my website, please see the original article, Blasts From the Pasts: My Old Slides (LOL).”

Sheila B. Robinson

Sheila says, “I found a good (meaning bad, of course) slide for you and I promise, this is the original and it’s from 2009!”

Then

“Unfortunately, I did no doctoring to make it worse for this purpose. Funny, this is probably one of my last really bad slides. By 2006 or 2007 or so, I knew to limit text (no complete sentences) and use larger fonts on each slide, but not much else. It was 2010 when I started really paying attention to design and learning at a much deeper level. Enjoy! :-)”

Jon Schwabish

Jon shared a 3D area chart disaster…

Then

Oh man, look what I found! One of my own graphs from a paper I published in 2007. #mydatavizsins pic.twitter.com/zUE6Pv4Bue

— Jon Schwabish (@jschwabish) July 30, 2018


Now

Jon’s blog is one of my favorite resources for data visualization best practices and advanced Excel how-tos. You can read more at https://policyviz.com/. For example, he just published an article about developing a tile grid map for Washington, D.C. zip codes at https://policyviz.com/2019/02/14/a-tile-grid-map-of-dc/.

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In July 2018, I spent a couple days teaching data visualization workshops in Wisconsin. I remember one attendee in particular: Sara DeLong.

Sara was serious about taking her skills to the next level. Over the past six months, Sara started her own data visualization blog, and Sara and her team transformed their lengthy technical reports into visual summaries that local health departments and community-based organizations crave.

I talk about the 30:3:1 approach to reporting in Soar Beyond the Dusty Shelf Report. The gist is that you write a technical report plus shorter summaries, like a three-pager or a one-pager.

Sara and her team of public health researchers took the 30:3:1 approach to the next level–they even developed a media campaign to get HIV data out of dusty spreadsheets and into the community. You’ll love the photos of the bus and the bus stop in her article.

I hope you enjoy learning these practical tips about effective data communication from Sara DeLong. –Ann

Do you have a long report that took a lot of time and effort, but you’re not really sure how anyone is going to use it? Annual progress reports, data publications, strategic planning documents… many of us spend a lot of time writing these documents, and they’re necessary to capture a lot of detailed information.

Here is an example of how I turned a long report into accessible, graphic summaries, and eventually into a media campaign.

The 130-Page Report

The Wisconsin Integrated HIV Prevention & Care Plan 2017-2021 is 130 pages long. I took this 130-page plan and paired it down to a 10-page overview document and a one-page snapshot.

10-Page Overview

The 10-page overview is primarily used by local health departments and community-based organizations to identify strategies and data that will support their grant writing.

Readers can pull out the key points of the report more quickly with enough detail to support their own strategic plans and align their proposed activities with statewide plans.

Here is a page out of the 10-page overview:

One-Page Snapshot

The one-page snapshot is intended for the general public, specifically anyone who would like a quick overview of how Wisconsin plans to address the HIV epidemic in our state.

These one-page summaries are helpful for media releases and make great handouts for public presentations on the HIV Integrated Plan.

Five Steps for Transforming a Report into Digestible Summaries

Shedding over 100 pages of content isn’t as simple as hitting the delete key, but with a little planning, you can create tailored one-page and ten-page summaries for your target audiences. Here’s how.

Step 1. Identify the Main Points with Input from Colleagues

My colleagues worked really hard to put together the 130-page strategic plan. One of the biggest challenges of creating the one-page snapshot and the ten-page overview was getting the team to agree on the most important information to highlight for various audiences. Start by identifying your audience and the content that is most important to them. This can take several meetings and revisions, but this step is crucial before you start designing.

Step 2. Create a Style Guide that Specifies the Document’s Fonts and Colors

I created the snapshot and overview documents above, and a colleague created an accompanying slide deck. Designing a brief style guide made it easier to collaborate with my colleagues. Style guides can vary in detail, but creating one with just fonts and a color scheme can make a big difference in the consistency and efficient production of your final deliverables.

I intentionally chose colors that would pop off the page to make the materials as visually appealing and engaging as possible. I also used icons throughout the summary documents. This content can be dense, but icons help break it up for the viewer. Ann Emery demonstrates the power of icons in her blog post, How to Visualize Qualitative Data.

Step 3. Design, Edit, Seek Feedback, Repeat

Since a large group worked on the 130-page document, my team members had different ideas about how the most important information should be represented. Asking for feedback throughout the development of these materials was central to the success and usefulness of the final documents. These feedback sessions included in-person meetings and edits passed via email. By the end, everyone on the team had provided their input and felt a greater sense of ownership of the final products.

Step 4. Pick a Realistic Timeline

These materials were created and approved over the course of two months. The Wisconsin Integrated HIV Care & Prevention Plan has been in use for about two years now, and the summary documents are used regularly when presenting to the public or working with community partners. Even internal staff tend to refer back to these overview documents instead of the 130-page report.

Keep in mind that it’s never too late to put a summary document in play! Ideally, all the final products would be released as a package at the same time, but in our case that wasn’t feasible. These summary materials were created two months after the full report was published.  In future iterations, we plan to create supplemental materials like these when a large report is in the final stages of production.

Step 5. Pick the Right Tools

I created these materials using Microsoft Publisher, which functions a lot like Adobe InDesign but is something all of our staff have on their computers. You can use whatever software you prefer, even Microsoft Word.

The Impact of the 10-Page and One-Page Overview Documents

We shared the 10-page overview and one-page snapshot with our community advisory group. They provided great feedback and then requested even more accessible materials covering these HIV-related topics.

So our team went back to the drawing board.

As a result of the community advisory group’s feedback, we contracted with a media firm to develop the HIV in Real Life media campaign based in Milwaukee, Wisconsin. T

his media campaign features residents of Milwaukee who have been impacted by HIV and addresses HIV-related stigma through stories, images, and video.

This was a different kind of information design that looks nothing like the 130-page plan.

There is no mention of the large plan on the HIV in Real Life website, even though its overarching principles are present throughout the campaign.

In the end, the overview documents were a critical stepping stone from the large report to the final media campaign.

The overview documents enabled conversations about how to make the key messages from Wisconsin’s HIV Integrated Plan more accessible to the general public.

­­

Media Campaign as Information Design

In this media campaign we focused on the stories of people impacted by HIV to highlight data and facts about HIV.

Ronnie tells the story about how the unconditional love of his family is a big reason he is living with HIV and thriving.

Jaymes shares that he learned the hard way how to be a friend and an advocate for the people in his life living with HIV.

Tianna talks movingly about her unwavering support for her best friend, Corey.

These stories counter the fear and stigma commonly associated with HIV. They begin to change the narrative that has been around since the 1980s.

HIV in Real Life - HIV Has Changed - YouTube

When my team started exploring the possibility of a media campaign, we posed the same questions we ask when approaching any data visualization project.

  • Who is our audience?
  • What are the main points?
  • What is the best method to convey those main points?

We knew we didn’t have all the answers. So we took the general concept of a media campaign to our partners. We attended community events, board meetings, coffee dates, and had many phone conversations.

We got feedback on those three questions, but we also talked to experts who had run previous, local media campaigns to learn from their past successes and challenges.

All those conversations taught us one thing: we couldn’t create this HIV media campaign in a bubble. So we formed an advisory board of community stakeholders to guide its development.

Color

In data visualization, we try to title our graphs using the main findings from the data to do the work for the viewer.

We also focus on using action colors to highlight specific information in our charts. Those same data visualization principles were applied in this media campaign.

Color was the easy part.

We knew we wanted to use bright, vibrant colors to challenge the fear and stigma commonly associated with HIV.

When the campaign launched, someone came up to me and said, “If someone drives by a billboard and just sees the word HIV and those colors this will be a success, because it is so different than people’s misconceptions of HIV.”

Headlines

Creating the headlines was the hard part.

This project started with a 130-page strategic plan, and we had to narrow down the message of our campaign to just a couple of words that would fit on a bus, billboard, or online banner ad. This took a lot of trial and error and actually ended up pushing back our launch date because it was so important to get the headlines just right.

The community advisory board that provided feedback throughout campaign development was instrumental in identifying the final headlines for the campaign. In the end, this campaign was particularly meaningful because it was collaborative and community driven from day one.

Learn More

Take a look at the complete one-page snapshot and ten-page overview documents to see how I transformed my long report into graphic summaries to create an impact.

Not everyone has time or resources to develop a city-wide media campaign, but as data visualization practitioners, advocates and experts we should continue to look for ways to transform long documents into more accessible products that maximize their value for a variety of audiences.

About the Author

Sara DeLong is passionate about information design and data visualization. Working in public health in both nonprofit and government settings has taught her that the most effective communications materials are collaborative, community-driven, and bold with color. Sara enjoys the challenge of taking complex content and transforming it into visually engaging materials. She is really excited about growing her data visualization skills and continuing to prioritize accessible data communication. Sara currently resides in Madison, Wisconsin. To see more of her work subscribe to her blog.

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What’s in store for the future of data visualization? A decade ago, I celebrated making my very first 3d exploding pie chart for a report that I was writing. Nowadays, I can’t open my laptop without my jaw dropping on the floor as I scroll through site after site of data visualization masterpieces that magically incorporate animation and interactivity and scrollytelling and multimedia and the perfect blend of art and science. How times have changed! In this article, let’s take a look back at some of the best data visualization accomplishments from the past 12 months. We’ll also look forward and make predictions for the future of data visualization in the coming 12 months (and beyond).

Rather than just share little ol’ Ann’s reflections, I turned to YOU, the data visualization community. I created a quick poll on Google Sheets and I also asked you to reflect on both Twitter and LinkedIn. I also collected social media posts that touched on these themes of predictions for the future or resolutions for the upcoming year. This is by no means a serious research study. I just wanted to gather some of your ideas into one central place.

Here’s what you said about the best data visualizations of 2018 as well as your predictions for the future of data visualization in 2019 and beyond.

The Best Data Visualizations of 2018

First, I asked the community to name the coolest data visualization they saw in 2018.

Here’s what you said:

Michel Guillet of Capire Solutions mentioned Powerless: What it looks and sounds like when a gas driller overruns your land by Ken Ward Jr., The Charleston Gazette-Mail, and Al Shaw and Mayeta Clark, ProPublica. Michel writes, “The map projections in the story are stunning and really bring this story to life. Also, really easy for the reader to engage and appreciate it.”

Lance Salyers of 5×5 Advisory mentioned Frames of Mind by Alberto Lucas López of National Geographic which is a treemap that depicts 8,000 of Picasso’s works within 12 major themes. Lance wrote that Frames of Mind is “literally a work of art that is also deeply information dense.” The visualization was even painted on a 36 x 60 inch canvas!

RoBlitz mentioned Morph, which is “a free & open-source tool for creating designs, animations or interactive visualizations from data.”

Mike Pell, Envisioneer at The Microsoft Garage, gave a trophy to the “Weather Channel’s use of mixed reality in TV broadcast to show the dangers of rising floodwaters with Hurricane Florence in Sept 2018.” Want to peek behind the scenes of the design process? You can read all about How the Weather Channel Made That Insane Storm Surge Animation.

Weather Channel Hurricane Florence storm surge graphics (Erika Navarro) (augmented reality) - YouTube

The Kantar Information is Beautiful Awards “celebrate excellence and beauty in data visualizations, infographics, interactives and information art.” You might recognize Here’s How America Uses Its Land by Bloomberg LP, “a series of unique 8,000-pixel maps in a distinctive, scrolling web experience.”

Nathan Yau of FlowingData described his picks for the Best Data Visualization Projects of 2018. I was glad to see that the Women’s Pockets Are Inferior” project Jan Diehm and Amber Thomas made the list. Their diagrams comparing men’s and women’s pocket sizing was a personal favorite.

The Reuters’ graphics department reviewed their Best of 2018 visualizations. Their 14-member graphics team, which spans New York, London, and Singapore, described the favorite pieces they designed over the past year. I loved reading their personal reflections on why each piece was their favorite; for example, because the project “was a great showcase of pictures, video, graphics and text, and how all those things can work seamlessly together in one package.”

Marissa Michelotti wrote What a year–Explore the top vizzes & authors of 2018 in our #VizInReview to celebrate the Tableau Public community’s accomplishments. Marissa begins by highlighting some of Tableau’s new features that were released in 2018, like the Inspiration feature, which allows you to credit the authors or visualizations that inspired your work. In theory, Tableau’s Inspiration feature should cut down on visualization plagiarism, in addition to making it easier to give virtual high fives to each other. My favorite visualization from Marissa’s article is Andy Kriebel’s Visual Vocabulary, which is basically the Financial Times’ Visual Vocabulary resource brought to life inside of Tableau. You can see more visualizations on social media by searching for the #VizInReview hashtag.

The Wall Street Journal highlighted their best visual stories from 2018 in The Year in Graphics: 2018. My personal favorite was Trump Takes to Twitter Like Clockwork not because of the subject matter but because it depicts time series data (tweets by time of day) going around in a cycle. My brain is one big spreadsheet. I tend to only think about time as being linear from left to right. When I see time depicted as going around in a circle, it forces me to go outside my visual comfort zone a bit.

The New York Times collected some of their favorite multimedia stories in one place. The New York Times is my all-time favorite graphics team, so it’s hard to choose just one personal favorite from their article… but I’d like to give a special trophy to The Age That Women Have Babies: How a Gap Divides America by Quoctrung Bui and Claire Cain Miller. Recently, a Facebook “friend” was claiming that all Millenials are having babies in their early 20s. My acquaintance included a screenshot from the New York Times’ article in her Facebook rant–clipped ever so perfectly to include only the data to support her point and leave out all the rest of the context. Being the font and color nerd that I am, I recognized the New York Times’ green/pink maps immediately. I went straight to the article and (politely) pointed out how she had clipped the screenshot to support her point and that the data don’t suggest that Millenial women are having babies earlier and earlier–in fact, the opposite is true in many cases. She didn’t respond but I like to hope that she’s still stewing over being called out for trying to lie with data. Any article that arms you for internet arguments is a winner, amirite??

FiveThirtyEight shared 45 of their best and weirdest visualizations from 2018:

Pew Research Center shared their 18 most striking findings from 2018. While not a review of their best or favorite visualizations, the 18 findings are all depicted visually. The article contains a variety of small multiples bar charts, and line graphs with shading to show confidence intervals, and slope charts, and dot plots.

Finally, the Guardian highlighted their top 18 visualizations from 2018 in a Twitter thread:

18 for 2018: a thread..

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Deven Wisner is a frequent Depict Data Studio collaborator, a personal friend, and an all-around awesome data nerd. Check out his additional articles about getting started with Tableau, customizing fonts, and customizing colors.

—–

If you’re like me, you were probably inspired to revise some of your charts after reading Ann’s How to Visualize Age/Sex Patterns with Population Pyramids. Too often when I storyboard demographic information I default to separating out this demographic information (you know, for the sake of keeping things simple). Unfortunately, this is sometimes a missed opportunity to provide a more comprehensive picture. These things combined inspired me to recreate the Pyramid Chart in Tableau — Let’s get started!

The Data

The dataset we’ll be using is from the Arizona Office of Economic Opportunity, which you will see consists of age groups, sex (male/female), and population. If you receive disaggregated data, that wouldn’t be a problem — that’s Tableau’s specialty!


The Hack

We want to load our data source, review that our variables came in correctly, and add a new sheet. Next, we’ll drag two Population pills to Columns.

Now drag Age Group to Rows. You will want to click the arrow and uncheck Show Header.

To get our Age Groups in the middle, we need to hack Tableau a bit. Start by creating a Middle calculated field — all you’re doing is entering “0” and saving. Next, drag the Middle pill between your Populations. On the Marks Card, add Age Groups as a text item and change the type to text.

Okay, we’re making progress! Realizing our pyramid (or butterfly) is dabbing, we need to reverse the axis of our population on the left.

Note: You might need to sort Age Groups, as I did (see above where Under 5 years is after 85 years and over).

In order to highlight females on one side and males on the other, we need to create a couple calculated fields.

On the Marks Card, add Female as a text item to one population and Male to the other. Now we have the shape of something, but you probably noticed that the comparison is about as helpful as a table.

To change that, we’re going to get crafty with colors and create a calculated field for each. This will allow us to control the colors independent of one another while still maintaining the same x-axis (i.e. Population).

After creating your Female/Male Color calculated fields, you’ll want to drag them to their respective Color Marks Card. Next, edit the colors and be sure the “0” (i.e. the sex you don’t want to call out) is something very light.

Additional color tips: You’re better than basic — use something other than the default blue and orange Tableau gives you. You should also avoid using traditional blue and pink to represent male/female (this is 2018, after all!).

Our final steps will be cleaning up our visual. First, I want to get rid of unnecessary borders and lines across the entire sheet (they’re just a distraction). I would also hide my axes, as I have labels on my bars.

Speaking of the labels, I want to format (SUM)Female/(SUM)Male to be a nice round number and change my units to thousands (K). You should use your discretion when doing this but for the purpose of this chart, I know my readers aren’t actually distinguishing between 200,010 and 200,500.

Last but definitely not least, I want a descriptive title that pulls readers in. You’ll notice that I also leveraged colors in my title to differentiate between each side of the pyramid.

Bonus: You can go the extra mile and include some great information in your tooltips, too. Often, percentage of an entire population is a really important piece of information. The tooltip is a great way to provide that without adding distractions to the visualization.

Deven is a Senior Managing Consultant at Empact Solutions. He is trained in applied psychology, with concentrations in evaluation research, and industrial-organizational psychology. Deven dedicates the majority of his time to building the capacity for data-driven decision making within organizations. He also develops the appreciation, understanding, and use of data through compelling data visualizations using variety of tools. Through evaluation, Deven empowers decision makers to understand the inner-workings of their programs or new initiatives – allowing them to make the most informed decisions. His facilitation experience varies from delivering customized workshops to teaching the Psychology of Leadership & Organizational Behavior for the University of Arizona, South.

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I recently partnered with a school system to visualize data about the percentage of their students who quality for free or reduced-price meals. In the United States, Free and Reduced Meals (a.k.a. FARMs) is basically how we measure poverty in schools. The more students who are eligible for free lunch (based on their family’s income), the more students who are low-income.

Before

Here’s the before version. They wanted to compare one of the schools to the district as a whole. The nested design almost made sense because it almost easily allows us to compare the school and district. But the concentric circles throw off the proportions. Do you see the magenta segments? The outer ring is larger than the inner ring, so the outer five percent is incorrectly larger than the inner five percent. Oops!

After: Stacked Columns

Here’s my first attempt. At the very least, we need to transform our circles into rectangles.

For all of these makeovers, I selected two colors: one color for the district and another color for the school. I also made the District and School text stand out in a large font size. You would use your own branded colors and fonts, not mine.

My Former Researcher Brain loves stacked charts. Researchy types like me tend to default to this layout. I’m not sure why… just because we’re used to stacked charts?

There’s nothing inherently wrong with stacked charts. They just tend to be overused. Even though I design data visualizations for a living, I still can’t shake my Former Researcher tendencies, and I tend to draft reports with way too many bar charts. As I’m editing my own work, I look for places where I can swap out my overused bar chart for another chart type. I don’t include variety just for variety’s sake. I look for places where the chart’s messaging might be clearer through another chart type.

After: Waffles

Another option is a waffle chart, like these two waffles. Waffles contain 100 little squares. They’re like square pie charts.

This approach almost works. I like it, but I don’t love it. I’m not in love with the labels. I tried creating text boxes for each of the segments—e.g., 27% free meals or 5% reduced meals—but it was impossible to arrange the text boxes on top of the waffle without looking cluttered or busy. Then, I moved the text boxes to the top of the graph, where they are now… but the viewers have to zig-zag their eyes back and forth between the words and the squares. Good. But not great.

After: Icon Arrays

Or, go big-picture with icon arrays. I love this approach, and the school district did, too.

In this icon array, we intentionally collapsed the categories—we combined free and reduced into a single category—to focus on big patterns so that our audience wouldn’t get lost in the weeds.

We also intentionally transformed the percentages into whole numbers, which is a better approach for non-data people (i.e., regular people who don’t stare at spreadsheets all day). Whole numbers are a lower numeracy level than percentages, so more people will understand the information.

And we intentionally focused on small numbers—3 in 10 students instead of 30 in 100 students—so that the numbers feel more tangible.

Want to try icon arrays yourself? A heads up: You must be comfortable with rounding. Thirty-two percent of students in the district qualified for free or reduced meals, but I rounded that 32 down to 30. I don’t suggest coloring-in part of an icon. Partially-colored icons can get confusing, especially if you’re using those little people icons.

Icon arrays shine when you need an at-a-glance overview. For example, you might use icon arrays for slideshows or executive summaries. You can save the nuanced stacked columns or waffles for the body of the report.

This isn’t an exhaustive list of options. What additional designs can you come up with?

Bonus: Download the Materials

Want to explore how I made these visuals? Download the slides and use them however you’d like.
Download the Slides

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Deven Wisner is a frequent Depict Data Studio collaborator, a personal friend, and an all-around awesome data nerd. Check out his additional articles about getting started with Tableau, customizing fonts, and customizing colors.

—–

If you’re like me, you were probably inspired to revise some of your charts after reading Ann’s How to Visualize Age/Sex Patterns with Population Pyramids. Too often when I storyboard demographic information I default to separating out this demographic information (you know, for the sake of keeping things simple). Unfortunately, this is sometimes a missed opportunity to provide a more comprehensive picture. These things combined inspired me to recreate the Pyramid Chart in Tableau — Let’s get started!

The Data

The dataset we’ll be using is from the Arizona Office of Economic Opportunity, which you will see consists of age groups, sex (male/female), and population. If you receive disaggregated data, that wouldn’t be a problem — that’s Tableau’s specialty!


The Hack

We want to load our data source, review that our variables came in correctly, and add a new sheet. Next, we’ll drag two Population pills to Columns.

Now drag Age Group to Rows. You will want to click the arrow and uncheck Show Header.

To get our Age Groups in the middle, we need to hack Tableau a bit. Start by creating a Middle calculated field — all you’re doing is entering “0” and saving. Next, drag the Middle pill between your Populations. On the Marks Card, add Age Groups as a text item and change the type to text.

Okay, we’re making progress! Realizing our pyramid (or butterfly) is dabbing, we need to reverse the axis of our population on the left.

Note: You might need to sort Age Groups, as I did (see above where Under 5 years is after 85 years and over).

In order to highlight females on one side and males on the other, we need to create a couple calculated fields.

On the Marks Card, add Female as a text item to one population and Male to the other. Now we have the shape of something, but you probably noticed that the comparison is about as helpful as a table.

To change that, we’re going to get crafty with colors and create a calculated field for each. This will allow us to control the colors independent of one another while still maintaining the same x-axis (i.e. Population).

After creating your Female/Male Color calculated fields, you’ll want to drag them to their respective Color Marks Card. Next, edit the colors and be sure the “0” (i.e. the sex you don’t want to call out) is something very light.

Additional color tips: You’re better than basic — use something other than the default blue and orange Tableau gives you. You should also avoid using traditional blue and pink to represent male/female (this is 2018, after all!).

Our final steps will be cleaning up our visual. First, I want to get rid of unnecessary borders and lines across the entire sheet (they’re just a distraction). I would also hide my axes, as I have labels on my bars.

Speaking of the labels, I want to format (SUM)Female/(SUM)Male to be a nice round number and change my units to thousands (K). You should use your discretion when doing this but for the purpose of this chart, I know my readers aren’t actually distinguishing between 200,010 and 200,500.

Last but definitely not least, I want a descriptive title that pulls readers in. You’ll notice that I also leveraged colors in my title to differentiate between each side of the pyramid.

Bonus: You can go the extra mile and include some great information in your tooltips, too. Often, percentage of an entire population is a really important piece of information. The tooltip is a great way to provide that without adding distractions to the visualization.

Deven is a Senior Managing Consultant at Empact Solutions. He is trained in applied psychology, with concentrations in evaluation research, and industrial-organizational psychology. Deven dedicates the majority of his time to building the capacity for data-driven decision making within organizations. He also develops the appreciation, understanding, and use of data through compelling data visualizations using variety of tools. Through evaluation, Deven empowers decision makers to understand the inner-workings of their programs or new initiatives – allowing them to make the most informed decisions. His facilitation experience varies from delivering customized workshops to teaching the Psychology of Leadership & Organizational Behavior for the University of Arizona, South.

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