Skill Level Intermediate
(upbeat music) - The world faces many challenges, war, poverty, environmental degradation, lack of access to education, among others. But there's one common thread among all of these issues. Data. For instance, wars are being fought for natural resources. There's data about where these resources are, who has physical access to them, and who has financial control of them. This data is at the heart of the problem but not to worry. This also means that it's at the heart of the solution. Data analysis, storytelling and visualization can help solve some of our most vexing problems. But it's not so simple. There are particular challenges when trying to communicate with data in these areas. There's the issue of the politicization of everything these days. There are challenges around access to reliable, objective data and there's the difficulty of using data visualization to successfully persuade an audience to devote time, dollars or votes to support a cause. In this lesson, I want to focus on persuasion. Say you want to support a cause and your goal is to share data about that cause in the hopes of convincing people to support a political candidate who has vowed to address that issue? The good news is that data visualization is proven by research to be persuasive in certain circumstances. The most established theoretical model of persuasion is called the Elaboration Likelihood Model. This model shows that the nature of persuasion depends on the extent to which the audience digs into the arguments. If they heavily scrutinize the message, then the ability to persuade them will be based on the quality and the strength of the argument. So if you're creating a data visualization to make your case, you'll need to create a high-quality visualization using high-quality data that strongly reinforces your case. If they don't scrutinize the message closely, so they're at a glancing it, then their persuadability will rely more on the credibility of the data source and aesthetic factors, also referred to as vividness. So it's important that your data visualization uses reliable data and you provide the source so they can confirm that it's reliable and that it isn't ugly as a dog. Aesthetics matter. A more beautiful, vivid and professional visualization will be more persuasive. Now, assuming you don't know whether your audience will dig deep or not, then you should strive to hit all of these notes. A strong and high-quality data argument presented in a vivid way with highly credible source data. This is pretty obvious perhaps but we're not relying on our gut instinct here, these three attributes of data visualization have been proven by research to drive higher rates of persuasion. What else? Well, here's where it gets interesting. A study was done looking at whether visuals, charts were more persuasive than tables of numbers. And this study found that it depends on your audience's initial attitude about the issue. If your audience is neutral, not already believers in your cause and not hostile to your cause, then the charts were more persuasive than tables of numbers. But if your audience is hostile, then the tables of numbers are more persuasive. This could be for a lot of reasons. Maybe they're more likely to think your fancy pictures are trying to trick them or that you've dumbed down the data or you've cherry picked the numbers. The research didn't answer the why question but it's important to understand that visualization can be risky with a hostile audience. It's also worth noting how important emotion is in data visualization for a cause. I'm going to cover emotion in another lesson, a lesson series video later, so we'll cover this in more detail at another time. So what are you supposed to do with this when doing data visualization for a cause? Here's my advice based on the research I've been reading. One, make sure your data stories are making strong arguments using credible and easily referenced data. This is the given and what you're already doing, I'm sure. Two, assume that your core audience is the great, fat middle, okay? Those people in the middle of the discussion who haven't formed strong opinions one way or the other and they're open to the data and its visual representation. In other words, assume neutrality. Make sure you're targeting them with your visual output. And finally, number three, understand that a portion of your audience is going to be hostile towards your cause and therefore, your output. So be sure to prominently include a link to the data source so they can find the data to confirm the voracity of what you've created and consider even including the raw data in table form yourself to make it even easier for them to see the data which might be more persuasive than the visual expression of it. Next, we'll talk to Olga Tsubiks, founder of Data For a Cause which connects volunteer data visualization practitioners to nonprofits to support causes like increasing peace, reducing hunger and supporting the environment.