So let's talk about the drawbacks of poor visualizations. Misleading visualizations aren't the only reason a visualization can be bad. Sometimes a visualization can have way too much information. In this example from the US Government's Accountability Office, they are trying to explain the number of derelict vessels, or let's say boats, abandoned in each state. In this case, the data is overwhelming, and there are two competing charts. There's the chart of the US, which looks at how many boats have been abandoned. And then there's a secondary chart which looks like the percentage of the boats that have been recovered. Now, if you look at the top chart, that makes pretty much sense. Okay, you've got a legend, and there's about seven different colors, and each color is representing a particular range. Now one of the nice things about this graph that I want to highlight is that they've used a mix of colors and patterns, lights and darks. Now this is really important, because if you print this out on a black and white printer, you still will be able to see the difference between those individual legends. Now, that's the good news. The bad news, let's take a look at this secondary chart here. I'm not exactly sure what's going on. It took me more than 30 seconds to figure out what was going on. Now, that's a bad rule of the thumb. Typically you want your consumer or your reader to be able to look at the data and be able to comprehend it in 30 seconds or less. Now, after further inspection of this second chart, what we see is it is looking at the percentage of vessels per state that have been recovered. And what they've done is they've used that first legend as the base of the pie chart. So what happens is you're seeing a lot of competing symbols and colors, which really take away from the relative simplicity of the first chart. Now again, it's always tempting to be really fancy and be creative when you're doing data visualization. But in this case, they're adding too much information to this particular chart, and it's overall just confusing what the reader is supposed to take away. Another thing that you want to avoid is creating a visualization that is incorrect. Let's take this example. In this case, the state of Illinois is trying to highlight the lack of correlation between state spending and jobs. This makes sense. They've tried to show a relationship between Mount Everest and a molehill. Unfortunately, the ratio is incorrect. A 5.1 inch molehill versus Mount Everest would be comparable to a 0.00001% growth in jobs, not 0.001%. Now, what is that? That is a 100,000% versus 1,000% difference. Or 100 times more than it should be. So visualizations are really important. One of the ways that I've used visualizations is explaining whether or not a particular product is appealing to consumers. Now one of the things that I often find that clients want to know is whether or not if they combine two particular things, will it be appealing to a particular customer? So it's what we like to call the Reese's Peanut Butter Cup conundrum. If you recall, Reese's Peanut Butter Cup is basically peanut butter and then there's chocolate surrounding the candy. So in order for you to like Reese's Peanut Butter Cups, you really have to like peanut butter and you have to like chocolate. So when we're ever trying to explain to our clients, okay, do they like peanut butter? Do they like chocolate? You're going to need to use a Venn diagram. And that's something that we often utilize in a data visualization. To say okay, we have this percentage of consumers that like this feature, this percentage like this feature, and then the intersection of the two is this. And that that's where a Venn diagram does a really good job of explaining what the intersection of interest is. This is something that I've realized is not one of my key strengths. So typically, when I'm putting together a presentation, I work separately with a designer who is skilled in data visualization. And can take the raw data that I have and then format it in a way where it's really visually appealing and clear and concise. And again, this is a great example of learning what your strengths and weaknesses are as a researcher. Early on in my career, I did very basic graphs. But after a while I've realised that it was better to work with a designer who had a key speciality in this area. I typically spend a lot of time and resources towards making a presentation visually appealing. I would say that probably in terms of cost, it's probably 10% of the cost of a project. And it's definitely a few days of work when you're spending, let's say, two or three weeks working on creating a presentation. Now why is this important? What we've realized is, is that we're visual learners. And especially today, people who are looking at research want to see things in a way that is visually coherent and cohesive. So it's actually a great investment, because what I found is that my clients are really happy when they get a presentation that is clear and crisp and visually appealing. In conclusion, keep in mind a good visualisation can help you take really complex information and present it in a way that is easy to understand and in a way that makes a strong impact. On the other hand, poor visualizations can leave your audience confused or walking away from your presentation with an understanding contradictory to what you're trying to deliver.
