- [Instructor] Typography is at the foundation of great design and it's at the foundation of really good data visualization as well. All the standard design typography principles apply but it's extra important here because every emphasis that you make in data visualization is so important. It can really change your audience's perception and understanding of the data your communicating if your typography is done weirdly or if it's not done thoughtfully and strategically. Accuracy is the key, so you have to be extra thoughtful about your typography to call attention to things to emphasize the things that you want to emphasize.
This video won't cover the fundamentals of typography but there are other great courses like this one from Ina Saltz here on Linkedin. So you should definitely check that out. But I will point out some specific ways typography applies to infographics and data vis specifically. So, there are basic types of type in charts and graphs. They may seem obvious how to approach but there are some nuance between them. And so you want to think about these categories and use your type design skills to help users understand what type of information they're looking at, right? Legends and axes labels versus data labels versus callouts, et cetera.
So in other words, you want to sort of keep legends and axes in one style for instance. Labels in another style, callouts in another, et cetera. Makes it easier for your audience to quickly understand the visual language of the visualization that you're creating for them. Axes and legends should always be labeled, right? Here we have Snoods and Whatchamacallits and if there were no labels on the y and x-axis how would I know what I was looking at? Right? The norm is to use small text, you don't want to draw attention to it.
And I always recommend that you use gray if you're on a white background, right? You want your legends and your labels to be faded back. They have to be legible but they're not what should be drawing the eyes, so they should not be high contrast. You very typically see labels like this where we have a lot of stuff going on so people will turn the labels on their side just to squeeze them in. While not strictly forbidden, it's not really a good idea. Not to mention in this example, you don't really need to label every single bar.
Once I know that the black bar is Whatchamacallits and the lightest bar is Gizmos, I don't have to repeat that labeling. Might as well reduce visual distraction and not label it all the way across the chart. But a better way to do it is to not force your audience to tilt their head to read your labels. So, you know, not that this is the only way to do it but make 'em, you know, horizontal so they're legible and readable. Don't really make your audience tilt their heads. Now, of course, usually, almost always in a chart, you need numbers on it, right? I need my y-axis to tell you that it goes from zero to 1,000 in this case.
No numbers doesn't make sense. Sometimes you need to add more numbers, right? There may be an argument for including 250, 500, 750, et cetera in this chart. You know, it's not always the right choice, I always say at a minimum you have to have the bottom number and the top number. But if there's compelling argument for including numbers in the middle, include 'em. And maybe 750 is a really important number. So in this case, add 750, make sure it's really bold and red and maybe even draw a dotted line. If it's a really important to see that third black column to the right that it's almost at 750 but not quite, then that's a compelling argument for making sure 750 stands out.
Maybe 750's a benchmark, a target, another argument for making sure that it's really there and bold and bright and visible. As with axes labels, there's a constant tension with data labels, right? There's a balance between being accurate and also remaining legible and readable and beautiful, right? Because, you know, a designer would say let's label nothing, it's much more pretty and aesthetically pleasing. And a data person says, label everything 'cause every single data point is important. And you have to find that balance for yourself.
I would certainly tend towards labeling fewer things rather than everything because when you label everything you're saying, everything is important. Really what you're saying is that nothing is important. So, I can label everything or I can label nothing or I can just label maybe the few things that are actually important to this audience. The things that I'm really focused on. Maybe, sometimes, it's really just one thing that needs a label. So I can just label that one thing and make it really big and bold. Or, use design, even better, to really make it pop and really make it stand out with background color, with contrast, with design, et cetera.
Good design will always find that balance between accuracy, readability, storytelling, the granularity of the data you need to communicate, as well as aesthetics. You can make the case that this really isn't even a label anymore. This is more of a callout, right? This is really drawing attention to something. You can use different font face, weight, color, backgrounds, images, color, et cetera as I have here, to really make a big impression. To really make a callout draw the eye to something. With, you know, not really crazy adjustments to your typography, it's sort of like a label, it's sort of like a callout.
And, you know, in this case, it's really making it clear that I want you to look at that one data point. Here's an example from the real world. A project that my company did for a client. And just about every example of typography is at play here. And there's a lot of variety in the typography without it feeling like it's 800 fonts and all kinds of things going on. You know, we have a title which is the very large, bold type in the upper left hand corner. We have a callout quote which is clearly different typography than above and draws the eye in a very different way.
Obviously, the most important data point that we want you to see is that 5.69 in the center of the image there. That's the number that is the most important number here so I'm really making it clear to look there. But then using a very similar graphical style, with a little callout box, for the other similar types of data points but lighter typography to make it clear that they're sort of less important, in addition to the size of the callout box. You'll also notice that we have two different types of labels on our axiss.
So we're showing both the percentiles, the 25th, 50th, et cetera percentile of the scores. As well as the actual values of the scores the 5.5, 5.7, in the lighter gray. And you'll notice in this case, you know, you can make the case, hey why is the value, the 5.7 a lighter number when the actual number down below, the 5.69 in the big callout, is the number we wanted you to see. And that was just a client decision. They wanted to sort of emphasize in the labels, the percentiles but emphasize the value in the callout.
So that was a conversation that we have, it was a very strategic decision. Even though the decision seems like it's sort of at odds, you know, internally within the same graphic. And again, typography, just from that standpoint, on the far left side in the bottom we have our sort of legend explaining the relative value of the scores, one versus seven. And then finally, the very small type, you know, very deemphasized typography where we sort of have our footnote. So a lot of different examples of typography all in the service of a data visualization using a lot of different sort of basic principles of typography but nothin' fancy goin' on here.
So I started this with a list of four things but really only talked about the first three. We talked about axes and legends, data labels, callouts. You know, infographics are sort of their own thing, right? They're like any design project. All of your type design skills and experience will go into creating a full infographic. Great infographics always have great type design. So, I recommend looking at the typography courses here on LinkedIn for a deeper dive into the basic principles. It's a really fascinating and important topic for designers.
It's a very powerful tool to make your work more beautiful and to help improve understanding and impact for data visualization specifically. Follow the rules and best practices for typography in your data visualization and you'll be doin' really well. And then think about the nuances to type that are specific to visualization, such as, making your axes labels gray and desaturated, your footnotes really small of course, and really calling attention to the data points that are the ones you really want to call attention to.
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