[MUSIC] Welcome to this lesson. When presenting your visualization to your intended audience, your intent is to make the visually usable, clear to understand, and effective at answering the question they are trying to solve. However, the human brain is a powerful tool and is always processing multiple things all the time. In terms of visualization, it is our job to help lessen the cognitive load and decrease clutter, so that when the audience is looking at your visual, they can clearly process it. For this lesson, we are going to define the terms of cognitive load and clutter as it is necessary to understand these concepts for your basis of all your visualization designs. First off, let's talk about cognitive load. It seems like a pretty intuitive sounding term. The definition is it is the amount of mental effort that we use to get the information that we need. For data visualization, we need to learn how to minimize the cognitive load while also communicating the message accurately. There are three types of cognitive load, intrinsic, extraneous, and germane. Intrinsic cognitive load is the amount of memory that we need to understand something. So if I'm adding 2 plus 2, I could do that while I'm distracted or even while I'm speaking to you right now. But if I have to do long division, there is no way I'm going to be able to present this material and do that long division at the same time. I'll need to concentrate and can't be distracted. Otherwise, it'll take a lot longer, or I won't be able to do it at all. The next one is extraneous cognitive load. That is the amount of extra brain power that I need to deal with poorly designed visualizations. It is the ability to not only visually spot the issues but be able to describe them, as well. It is how information is presented, and if it is ineffective, that puts extra pressure on our cognitive load. Finally, germane cognitive load is a way for the brain to look for patterns to develop context. As with extraneous load, it is to help take a cognitive issue and present it in an easy and meaningful way. We'll use these three types of cognitive loads instinctively throughout this specialization. Okay, with cognitive load defined, let's talk about clutter. As I'm talking here, I see a pile of papers on my desk. My desk is cluttered, I have to admit. This stuff is clutter, as we know in common parlance. So clutter is definitely one of those things that we know when we see it. For data visualization experts like you, it's about determining what needs to be in a visualization and doesn't need to be in the visualization to reduce the clutter. So to take the desk analogy, most of these papers don't have to be on my desk. They can either be recycled or filed somewhere else. But they're still here, I need to remove those papers and thus reduce the clutter. So having defined both terms, we could see that cognitive load and clutter are fully intertwined. The reason we want to reduce clutter is so that we can minimize the cognitive load of the reader for the data visualization. If you remember in the last module, we talked about ineffective visualizations, and some small ways we can improve them. What I didn't mention at the time is a lot of that ineffectiveness or the WTF moments as the website calls, it are actually because of the large amounts of clutter. So here's an example of a chart that is cluttered, but probably wouldn't be so bad that it would get submitted to the website biz.wtf. It's from the United States Department of Agriculture, or USDA's, Economic Research Service. They have a renowned set of researchers who collect and report on data connected to agriculture. And this is a regular report that the USDA puts out on its website on a regular basis. I'd like you to think about why this chart might be cluttered? What should be eliminated? What could they do differently to show the other elements, if any? So here are what I would consider to be clutter in this chart. The first is the bad 3D effect. I think I mentioned several times about 3D effect. I like to get rid of those. Let's not do that unless you're graphing three dimensionally. The second is dark grid lines. Dark grid lines are really only helpful if it's going to aid in the visualization itself. Having dark grid lines really ruins what you're trying to actually show the reader, which is the visualization itself. So really avoid the dark grid lines. Third is the overuse of bright and bold colors. We're actually going to have a big, deep dive into colors and how we can use it very strategically. This is not a strategic use. Number four is no apparent sorting. Would be really helpful if we know why the data were sorted in that way. And number five is an unhelpful axis. I'm not sure what the axis is there. In the last lesson of this module, of this course, we're going to declutter this for the USDA. But we have a lot more to cover before we get there. Now why do I say not to use three-dimensional visualizations? Well, put simply, almost all of our visualizations are in two-dimension, so plotting stuff in 3D is mathematically just wrong. But less mathematical and less theoretical is that 3D bar graphs and other 3D types of visualization tend to give a very skewed perspective. When there's a large difference between each of the different categories, it might not matter. But why take the chance? Just avoid using 3D visualizations. Now, just because something is cluttered doesn't mean it shouldn't be there. That's why when we talk about visualization, it's sometimes considered an art form. And this is where it plays in. There are times when redundancy might be considered clutter. But it nonetheless helps with the cognitive load. There aren't a lot of these. So I want to go over them quickly here, so you have them moving forward. One of the fundamental areas where redundancy elements might feel like clutter but is necessary for cognitive load is when labeling. I believe it's absolutely essential to keep the currency symbol, the dollar, euro, pound, whatever domination money you need to use, in the visualization. Also, the percent sign is very important, so that readers know that it's a percentage and not a number. Use commas for large numbers and labels, so that it's just easier to read. It will really help make things clear when viewing it. Also, if you're visualizing very small numbers, make absolutely sure that the scientific notation is carried over on each label so that everyone understands what's going on there. It seems like a small point, but the goal is actually to reduce cognitive load, even though it might, in fact, increase the clutter. You never want your visualization to be unclear or difficult for your intended audience to understand. Okay, now that we went through this topic to help build your baseline, in the next lesson, we're going to discuss principles of visual perception. So thank you for joining me, and we will see you soon.
