A few of the million+ decisions your software program can’t make for you:

10. Which chart is best for your data. Yes, I’m well aware of Excel’s Suggested Charts feature. No, don’t even think about it. There’s no substitute for your critical thinking skills.

9. How much ink is necessary. Will your viewers benefit from light gray grid lines, or should you remove them altogether? Ask 10 different people and you’ll get 10 different opinions. You’ll have to think yourself outta this one.

8. How many decimal places are needed. One argument is that decimal places add precision – if you’ve got decimal places, why not display them? Another argument is that our datasets are rarely complete enough, cleaned enough, or generally accurate enough to warrant giving the viewers lots of decimal places (and therefore, a false sense of precision).

7. Whether you’re going to stick with a default color scheme (please, NO) or customize your color palette to match your client’s brand identity.

(And who chose Excel 2013’s color palettes, anyway? Lime green, royal blue, and orange? But that’s an article for another day.)

6. The extent to which you’ll label your data. Do your viewers need to see every point on the line, or just the beginning and end points? Or just the highest line? Or just the lowest line?

5. Whether your chart needs to be rotated to avoid diagonal text. In this scenario the answer is yes! rotate that chart! but your software program won’t automatically do this for you. You’ll have to determine whether your data labels need some additional space, and if so, swap your vertical bar chart for a horizontal bar chart.

4. Whether your viewers need aggregated or disaggregated data. It doesn’t matter how much you paid for your software program. Your computer doesn’t know whether your viewers would benefit from a single visual that contains aggregated information or whether your viewers need to make comparisons across four different variables simultaneously through a small multiples layout. But yes! Your brain! It knows!

3. Whether your viewers will really understand that diverging stacked bar chart, or whether you should just stick to a regular old stacked bar chart.

2. Which patterns to emphasize and which patterns to hide, like whether viewers need to see all the peaks and valleys (line) or whether you need them to focus on just two points in time (slope).

1. And in case it isn’t obvious yet, your audience is pretty darn important. Your software program will never understand your audience’s numeracy level, data visualization level, interests, time limitations, or information needs as well as you.

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 my old 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.

What’s Wrong with my Previous Chart

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.)
• 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 (Should use the client’s brand colors).
• 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?

Before/After Makeovers

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.

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.

Makeover #1: A Horizontal Clustered Bar Chart

First and foremost, I changed the chart type from a vertical column 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?

Makeover #2: A Clustered 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?

Makeover #3: A Small Multiples 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.

Makeover #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.

Makeover #5: A Slope Graph

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). 

For me, 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.

Your Turn

Which remake would suit your audience best?