Episode 7: Why and How You Should Improve Your Audience’s Graphicacy

/ Data Communication, Podcast

Why and How You Should Improve Your Audience’s Graphicacy

In this episode I’m going to explain why you should take opportunities to improve your audience’s graphicacy and some suggestions on how you can start doing so. 

I recognize that some people believe that you should only use and show charts in your data visualizations that your target audience is familiar with and to avoid using uncommon or novel charts.

But this can *sometimes* be harmful for a couple of reasons.

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What You’ll Learn in this episode

  • Why you should improve your audience’s graphical literacy
  • How you can start doing so
  • Ways you can improve your own graphicacy

Action Items

  • After making data visualizations for a particular audience and before you show them the data viz, assess which of the charts they may already be familiar with and which may be new to them or they may need help decoding.
  • Enhance the data viz you think your audience will have trouble understanding with labels, annotations, legends or other features that will guide your readers to understand what’s going on in your data viz and how to read it
  • If you haven’t already, check out Hans Rosling’s famous TED talk here: https://www.youtube.com/watch?v=y1kg5k6_fcA

Get in Touch with Hana

Let me know what you think of the episode, you can message at hana@trending-analytics.com or on Instagram @hanalytx.

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If you enjoyed this episode, check out my previous episode about why to focus on communication skills.

Episode Transcript

Graphicacy AKA graphical literacy is the ability of the readers of your graph or data viz to understand and comprehend the graph they are looking at. In this episode, I'm going to explain why you should take opportunities to improve your audience's graphicacy and some suggestions on how you can start doing so.

Now I recognize that some people believe that you should only use and show charts in your data visualizations that your target audience is familiar with and to avoid using uncommon or novel charts. But this can sometimes be harmful for a couple of reasons. One, if the best way to show your message is through a particular chart that your audience happens to not be familiar with.

Then you may end up compromising on your main message being conveyed accurately. If you choose a less optimal chart, that's more familiar to your eyes. Second, your audience g raphicacy will not improve if they are not exposed and taught how to interpret a new chart type they haven't seen before. 

It will not only help you, but your audience and other data visitors they may work with. If your audience has graphicacy improves. So the next time you're communicating data through charts. I want you to think about what message or take away you think is critical for your target audience, and then figure out which chart type will best show this message.

Then assess if your chart will be something your audience is familiar with or will have difficulty in processing now before deciding to replace it with a different one. See, if you can help your audience interpret this new chart type. And there are a few ways you can do this one, you can have accompanying text, like in the caption of a chart, explaining how to interpret this chart.

And also what's going on. Like, what is the main message that you want this chart to convey? Second, you can also annotate the chart directly pointing to elements on the data viz where you want your audience's attention to focus back on. So they're not overwhelmed by a new chart, so this can help bring their attention back to the main message that you want them to take away after they're done digesting the chart.

Third habit legend on the chart. That explains how to interpret some bowls or other encodings that they may not be familiar with. And finally, if possible, demo the chart to them in real time, like at a meeting or presentation when you're showing this chart for the first time. So break it down and walk them through each part one by one.

Now, You don't have to do all of these. You can do one or two or all of them. If you think whatever you think is best, basically for your particular situation.

 There are a few ways you can do so, and this is listed in no particular order, and you can implement these based on your particular situation. You don't have to do all of these. These are just some suggestions that I have. One is you can have accompanying text, like in the caption of our chart to explain how to interpret this chart.

And also what's going on. Like, you know, what's the main message or takeaway that your audience should leave. Two, you can annotate the chart directly like using arrows and texts, pointing to elements on the chart where you want your audience's attention to focus back on. So they are not overwhelmed by a new chart.

So once they're done processing this new chart type and figuring out how to interpret this, you can use these annotations to help bring back your audience's attention to the main message. Three, you can have a legend on the chart that explains how to interpret symbols or other encodings that you think they may not be familiar with.

And finally, if possible, I strongly recommend this is to demo the chart to them in real time, like at a meeting or presentation when you're showing this chart for the very first time to them. So break it down and walk them through each part one by one. One of my favorite examples of this is with a famous Ted talk Hans Rosling did, when his talk came out and I saw it. I was not in the world of data at that time. And I wasn't familiar with all the different charts. There are, especially the uncommon ones. The highlight of his talk is this animated bubble chart that not a lot of people were familiar with at that time.

And I'm talking about the general public, not to data professionals. It's possible. Some scientists and data professionals were familiar with this chart. If you don't know which talk I'm referring to, or need a refresher, he essentially created a scatter plot where on the X axis, he had the log GDP per capita for countries and the Y axis represented life expectancy at birth and each dot represented a country and the size of its dot represented the population size of that country.

So that's what gave it the bubble experience, because you can imagine countries with huge populations. The bubble was a lot bigger. The bubbles were also color-coded to represent the economic region that they belong to. And then to take it further, he animated this chart to show a transition of all of these variables from 1960, until the latest available data he had at that time.

So you basically see these bubbles moving along the graph as time progresses. In my opinion, as someone who watched this talk back when I was unfamiliar with the world of data and had never seen a chart like this before, I think  his commentary and narration of the chart was what helped me understand how to interpret this.

And his narration is said to be like a sports commentator. It's very animated and he is very excited that it's almost contagious and he just makes it fun to watch, you know, just to look at the chart and see what happens as time progresses. So if you haven't seen this talk before, I'll link it in the show notes, I highly recommend watching it.

But back to my point, now, many people know about bubble charts, thanks to this very famous talk. And in fact, it inspired many data visualizers to use this bubble chart for their own work and recreate it further, exposing others or the general public to this previously uncommon chart type. 

This should encourage you to not shy away from using uncommon charts for your audience. If you believe this chart most effectively conveys your message. In Hans Rosling's case, he took a pretty standard chart, like a scatterplot and played around with different ways of encoding and animating to display their dimensions on this typically straightforward chart type.

Now, this is considered a chart type of its own, a bubble chart. 

 Now. I want to leave you with some action items to implement what you learned in this episode today.

After making data visualizations for a particular audience. And before you show them these data visits, assess which of the charts your audience may already be familiar with and which may be new to them, or they may need help.

Then enhance the data viz. You think your audience will have trouble understanding labels, annotations, legends, or other features that will guide your readers to understand what's going on in your data viz and how to interpret it. Finally look for ways that you can improve your own graphical literacy, whether it's through books or you can even peruse data visualization, galleries, and portfolios to expose yourself to novel and uncommon chart types.