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Storytelling with Color Tips

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Instructor

Ryan Sleeper

Leverage color to improve your designs and help communicate insights

Color tips include reducing the saturation of marks, keeping color choices simple to reduce the amount of work your audience must do to process the data visualization, and beware of your audience’s expectations of what colors represent.

Hi, this is Ryan with Playfair Data TV, and in this video, I’m going to be sharing some storytelling with color tips. I’m going to show you how to reduce the saturation of bright colors. We’re going to be discussing the topic called ‘cognitive load’, and how you can make it easier to have your end user process your view using color. And we’re also going to talk about schemas or expectations that your end user might have for certain colors.

For all these tips, we’re going to start with this viz over here in Tableau Desktop using the Sample Superstore data set. We’ve got Sales broken down by Region, and those bars are colored by their region using the jewel bright color palette that comes with Tableau Desktop.

This color palette definitely has a place, especially on reverse contrast charts. But when viewed in a vacuum, some of these colors can be kind of harsh on the eye, but I still like the colors themselves. So if you’re in a similar situation and you just want to make it a little bit easier to look at this, we’re going to do what I call ‘muting’ the colors, but the science behind what we’re doing is reducing the saturation of these bright colors. You can easily do this in Tableau Desktop by clicking on the Color Marks Card and dragging this opacity slider over to the left.

One hundred percent is opaque, so the color would be at its full saturation. By dragging this to the left a little bit, we’re adding some transparency. I usually drag this to the left to about 85% or 90%, but you’ll see what happens when I let go. Those colors just get a little bit dulled down. It was almost so subtle that we don’t really know what happened, but they’re just a little bit easier on the eye now, a little bit easier to look at.

You don’t want to go so transparent that you can see underlying lines and marks and things like that, but go just enough to kind of take that sheen or that kind of harshness off the color. That’s called reducing the saturation.

My next tip for you is to reduce the cognitive load. This is a topic in data visualization that I think of as being synonymous with the amount of work that my end user needs to do to process this visualization.

This is a little bit of a controversial tip, but what’s happening on this screen right now, at least when this chart is in a vacuum like this, is we’ve got some double-encoding going on. We’ve already got a bar for each of our four regions, because we have the Region dimension on the Columns Shelf, but then we also have Region on the Color Marks Card. So it’s doing the exact same thing, it’s breaking the sales values down by Region, just in different ways. The first way is across columns. The second way is across color.

But the issue, and why this actually increases the cognitive load or makes it harder on my end user to process, is they already had the information. They already had the breakdown by Region. Now, because color is also on the Color Marks Card– or Region is also on the Color Marks Card, they’re having to look back and forth to determine if the colors mean the same thing as the columns.

So easy way to fix that is just to drag Region off the Color Marks Card. We got rid of the double-encoding. So this chart is actually, in my opinion, slightly more effective than the one that also had Region on Color.

One big caveat to that though is if we plan to use this bar chart later on on a dashboard with other views that are also being colored by the Region dimension, in that case, I do like that double-encoding, because it helps us make that Region association between the different charts on a dashboard. So in that case, we’re actually improving the cognitive load. We’re reducing it, making it easier on our end user to process.

So this is a little bit of a case by case basis, because it can work both ways. When in a vacuum, I recommend avoiding the double-encoding. So when a chart’s being looked at one at a time. But if you’re going to be using it in association with other charts, on a dashboard for example, the double-encoding might actually help.

Another general tip I have for you, which is also related to this topic of cognitive load, is to try to keep your color selection to five or fewer. Because every time you add a new color, you’re putting a little bit of a burden on the end user to look back and forth at that color legend to figure out what’s going on.

So try to keep it to five or fewer. In most cases, you can actually get away with two colors or fewer. Then actually this works really, really well to help make one of your insights kind of pop on the view. For example, one of my colors might be a neutral gray or light brown, while the other color can be a brighter color that draws attention to a certain insight.

Let me give you an example on this chart. I’m going to drag Region back onto the Color Marks Card, but I’m going to remap these colors. Let’s say that we want to draw attention to the South region. So let’s pretend we’re building a bar chart in a vacuum. We’re going to put this in a PowerPoint deck. It’s going to be looked at just by itself.

If we’re wanting to draw attention to that insight, we can remap the colors by double-clicking on the Color Legend and choosing different colors for each of our four regions. I’ll make that South region the red, but then I’ll switch over to the Seattle Grays palette and make the other three regions a light gray color. And now, not only are we using five or fewer colors, we’re using two or fewer colors, and it’s helping our insight pop out on the view. We’re going to focus on the South region during this analysis is what we’re implying with these color choices.

And my last tip for you is to be aware of what end users expect different colors to mean. For better or worse, I bet every single person watching this video has an idea of what these two colors on the screen represent, or at least they think they know what these two colors represent. Green’s good, red’s bad. That’s just kind of been beaten into our heads our entire lives. I personally, honestly, can’t remember a single person ever telling me that green meant good and red meant bad, but for whatever reason, that’s the association that people make with these colors.

So my last tip for you is to be aware of your end user’s expectations of what different colors mean. I like to use the joke that if you’re making a visualization about fruit, don’t make the oranges purple and the grapes orange, because it’s going to confuse the end users. Or don’t completely flip this schema or expectation that’s been planted in their mind their entire careers, and make bad things colored green and good things colored red, because it’s very much going to confuse your end users.

This has been Ryan with Playfair Data TV – thanks for watching!