The closer you are to the red line, the more effective you are.
(We hinted at the possibility that the red line is not the same for everyone – that it’s tied to your capability as a data visualizer. A super-skilled information designer might have a red line that stretches almost to the top right corner. A newbie might be a straight line from top left to bottom right.)
In any case, you want to improve. You want to have visualizations that approach that top right corner. How do you do so?
Ask the Right Questions
Let’s assume you have some data to visualize. Are you aiming more towards clarity or engagement? Here are some questions you can ask yourself:
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Who is your target audience?
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What is the main thing you’re trying to say?
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What do you want your audience to do about it?
Try to be as specific as possible as you explore these questions. It might even help to build them into a use case or user story. The process is almost identical to the steps you’d take if you were designing software.
Things that are more decision-oriented will lean toward clarity. If you’re trying to delight people, then it’s more about engagement.
For example, if you’re building a dashboard for an air traffic control center, then you want to be five-star clarity–your audience is already super-engaged. If you want to get a million new Facebook friends, then you need five-star engagement–clarity is secondary.
Visualize the Signal
Separating the signal from the noise is the essence of clarity. And clarity is where you start. Don’t be tempted to imagine the dancing bologna until you’ve locked onto the signal. The signal will tell you what chart type to use, what layout will work, and even how the user can best interact.
Many projects go awry right here. You jump ahead to a cool concept and only later discover that the data doesn’t support it. You do this because design is the fun creative stuff and analyzing data isn’t. Unfortunately, great data visualizers are first and foremost great signal finders. They’re data analysts at heart. This is why there are so few of them.
I would go so far as to say data visualization is a misnomer. It should be called signal visualization. Exploring the data to find the signal isn’t something you should leave to others. It’s fundamental to your job.
Think back to the suicide graphic in the previous article. Do you think the creators of the visual stumbled into that particular format by accident? I highly doubt it.
I imagine they explored a much larger data set that probably included race, region, socio-economic status, and a host of other variables. Through careful exploration, they discovered that age cohort was a strong signal – perhaps the strongest. They then crafted a visual that would bring that particular signal to the forefront. This effort is what makes the visual so effective.
I can’t emphasize this enough: signal must come first, even if your aim is primarily engagement.
Improve the Engagement
Engagement is a creative process, but it can still be systematic. Here are four things you can do:
1. Colour to set the tone
Colour’s impact is instant. You can use it to evoke different moods, to spark an emotional response, or to draw attention to a specific element. Think of it like a soundtrack in a movie. It sets the tone.
Start by choosing a palette that fits your purpose. Red is bold, dangerous, or angry. Orange is playful. Etc. Just don’t overdo it. Each colour you use attracts attention, so the more colours you use, the more your audience’s attention will be split. Don’t forget about grey. It might be the most useful of them all because it conveys information without fighting for attention.