I’ve been building portfolios of my work for a while.

Over the past year or so I’ve been building an online portfolio of speaking and writing engagements.

Before that, I collected paper copies of reports I worked on:



The great sobering thing about portfolios is that you can bask in the glory of your wonderful work bow your head in shame.

Most of these charts from my prior work were ineffective. Of course they were. Data visualization is a new skill for most researchers and evaluators.

One of these charts was so horrific that I just had to share it with you:


What’s so bad? you ask. I see these all the time, you say. Can’t they just tilt their head to read the words, you argue.

Each of these mistakes–from the before I knew better period of my career, never to be revisited again–kept viewers from understanding the information:

  • Generic and centered title (Should have a “so what?” and be left-justified.)
  • No subtitle or annotations (So the viewer can’t skim the information.)
  • Border (Should be removed.)
  • Tick marks on vertical and horizontal axes (Unnecessary.)
  • Full grid lines (Should be lightened or removed altogether.)
  • Legend (Should have direct labeling instead of the legend to avoid those back-and-forth eye movements.)
  • Speaking of direct labeling… why did I label the axes and the bars? Overkill.
  • Diagonal text (Should swap this vertical bar chart for a horizontal bar chart, leaving more room for the labels.)
  • Default color scheme (The client’s logo definitely didn’t contain Excel’s default blues.)
  • An action color is there, kind of. Not really. It’s used incorrectly. Our brains are drawn to darker colors. Why was I drawing attention to the “possible items correct” section with the dark blue?
  • Most importantly, why did I display the raw scores vs. the percentages? Can you imagine how much mental energy it must’ve taken my viewers to figure out what 2.9 vs. 2.6 vs. 6 means?

Introducing the Data Visualization Checklist

Now I know better. I’ve studied, practiced, gotten trained, and trained others. I want you to learn from my mistakes and go forth and make charts that people actually understand. That’s why Stephanie Evergreen and I published the Data Visualization Checklist earlier this month.

Guess how my old chart scored on the Data Visualization Checklist? 39%. Which means I’m certain that nobody would take the time to understand my poorly-titled, redundantly-labeled, cluttered, confusing chart.

Preview the checklist below. Download it. Avoid the agony. Your viewers will thank you. Maybe they’ll even hire you again.


The first phase in learning about data visualization is usually critiquing charts. What, exactly, is wrong with your work or someone else’s? How can you learn from those mistakes? The second phase is articulating specific ideas for how you’d make charts better (Data Visualization Checklist to the rescue!). The third phase is actually remaking your charts. Some people stay in the first phase forever. My goal is to move more people into the third phase: actually improving your work.

So I’ll lead by example. Here’s how I’d re-do this chart today. I designed five different remakes.

Remake #1: A horizontal clustered bar chart

First and foremost, I changed the chart type from a vertical bar chart to a horizontal bar chart. Horizontal bar charts are great when the data labels are pretty long, like in this example. Your goal is to avoid diagonal text at all costs. It’s harder to read, so viewers get distracted, bored, or generally turned off and stop trying to decode your messy chart. I’m not a fan of vertical text either. Not sure how to transform your vertical bar chart into a horizontal bar chart? I’ve got a tutorial.

Next, I transformed the raw scores (“2.6”) into percentages (“43%”).

Finally, I addressed all the Low Hanging Fruit formatting issues. For example, I added a 10-word title and a 1-sentence caption; I deleted the border, grid lines, and tick marks; I placed the percentages on the bars so I could delete the axis; and I placed the years directly on the bars so I could delete the legend.

Why the black and white? Just for fun, I want to show you that you can still emphasize patterns without using bright, showy colors. In real life, would I match the action color to my client’s RGB codes? Absolutely.

What do you think of remake #1?


Remake #2: A stacked bar chart

I’ve experimented plenty with real-life clients to see whether they prefer regular bar charts or stacked bar charts. Here’s the typical response:

  • Me: Here’s the first chart. [Regular bar chart.] What’s the message here?
  • Client: Oh wow! Our participants are doing so well! 46%! That’s high! … Right? Or is that number low? Out of what? Out of 100%, right? Hmm…
  • Me: You’re on the right track. Here’s the second chart. [Stacked bar chart.] What’s the message in this one?
  • Client: Oh darn, we’ve got a long way to go. 46 out of 100%?! I need to speak with our program director about this. In fact, our whole team better see this. We need to figure out what we’re doing wrong before it’s too late!

A dozen conversations later, and the result is still the same: Nearly all my clients gain deeper insights about the findings through stacked bar charts instead of regular bar charts. What’s the response in your projects?


Remake #3: A side-by-side bar chart

The first two remakes are better than the original. That being said… clustered bar charts are my least favorite chart in the history of the world. They’re so cluttered. And worse, the comparisons are lost. With so many bars smushed together, it’s nearly impossible to see at-a-glance patterns between the two series of data.

In this example, I created a side-by-side bar chart so viewers could more easily see 1) the 2009 pattern on its own, 2) the 2010 pattern on its own, and 3) the difference between the two. Still in the bar chart family, but different patterns pop out, don’t you think?

I also used the action color (dark gray) to emphasize the Social and Ethical scores, and I added an annotation (the call-out box on the chart) to make my viewer’s comprehension even easier.

Want to make your own side-by-side bar chart? I’ve got a tutorial.


Remake #4: A dot plot

Dot plots are often the superior chart. I use them to compare two points in time (like this example); two distinct groups (Program A and Program B); or, when I triangulate data, two distinct viewpoints (students’ perspectives vs. teachers’ perspectives).

But they’re not always superior. This dot plot doesn’t work. It’s too cluttered, isn’t it? Its more confusing than helpful. It’s because students improved on some areas, declined on other areas, and didn’t change at all on other areas. The annotation isn’t helping, either; instead of adding clarity, it adds clutter.

For the rare viewer who’s willing to spend 60+ seconds interpreting the chart, it’s great because it shows more nuanced patterns than the other charts. But my guess is that the majority of viewers will lose interest after a few seconds because they can’t immediately grasp what it means.


Remake #5: A slope chart

A slope chart is basically a line chart for two or three points in time. This chart type is also effective at showing rankings (i.e., it’s easy to see how the skills are ordered on the left-hand side). Want to make your own slope chart? Download Cole Nussbaumer’s template.

This chart is the winner. It’s easiest to understand at-a-glance. It ranks each of the skills areas. It shows differences over time. There’s a lot of cool stuff going on for the viewer to explore. It’s supplemented with a non-intimidating title, subtitle, and annotation. Yet, the information takes up very little ink and space. The score on our Data Visualization Checklist–98% of the possible points–reflects the strengths of this remake over the others.



Which remake would suit your audience best? Have you re-done any of your charts using our Data Visualization Checklist? If so, please contact me. I’d love to see how you’re improving your work.


P.S. Want to learn more about titles, subtitles, and annotations? Go check out Stephanie Evergreen’s post (http://stephanieevergreen.com/how-to-rock-the-text-in-your-data-visualization/) plus other Data Visualization Checklist resources on her blog (stephanieevergreen.com/tag/data-visualization-checklist).

P.P.S. Are you in DC next week? I’m leading an evaluation workshop on June 2 and a data visualization workshop on June 6. No diagonal text, I promise.