[MUSIC] Welcome back. In the previous courses, we discussed the importance of considering the user's needs when designing a visualization. For this lesson, I want to expand our thinking and discuss audience in the context of data stories. After this lesson, you will be able to identify the needs of your audience and tailor your data story for maximum impact. Let's begin. Hi, and welcome back. If you truly want your data stories to connect with your intended audience in a meaningful way, it helps to have some sense of who they are. That includes their level of understanding of the subject matter, their reasons for viewing the story, their context while viewing it, and what you intend for them to walk away with. Also, having an idea of their level of familiarity with statistical visualization techniques and charts can be crucial. For example, a general audience may find a box plot to be baffling. But for an audience with a more sophisticated knowledge and need for statistical precision, it might be the perfect choice. There may be more than one type of audience segment, and if so, you want to be able to distinguish among them and use that to guide your design choices about charts, visualizations, and story structure. There are two fundamental and complimentary approaches to getting a sense of your audience. Quantitative and qualitative. Both can play important roles in guiding your design decisions. The quantitative side includes surveys to capture common patterns in potential audiences. The qualitative side includes personas that can help you get a better real-world sense of the people who will be looking at your data story. Among other things, the qualitative research can provide a solid, big picture background view, and the qualitative can help you see your design from a perspective entirely different from your own. Now, If done well, personas can be useful tools in helping make design choices. This means creating them based on solid research and careful listening. A persona is, as we've learned in prior courses, a concise, concrete description of each type of audience member. As we've learned in previous course, they are highly specific archetypes representing a particular user segment that can help you prioritize design requirements. They are not homogeneous constructs of a, quote, average user segment. That is, just as an actual audience member could not possibly have 2.5 children, a persona would never have a 0.5 child either. A key part of the persona description is a list of needs and goals. These help set priorities, and focus the design. Adding some details, like the names of a persona's pets, can occasionally be helpful in breathing life and concreteness into a persona, and make them believable and relatable. But, typically, they are not essential elements. Now in interviewing people in a target audience segment, there are a few techniques and approaches to apply, and others to avoid. Perhaps the most important technique to consider is not to ask people leading questions at the outset of the interview. For example,I would not recommend you start by showing an interviewee a visualization or a data story prototype, explaining in a lot of detail, and then asking what's great about it. In that situation, it becomes impossible to determine what the user truly understands and likes, or doesn't, without prompting. Instead, ask the interviewees in a more open ended way about their needs, goals, and pain-points, and their level of understanding about the story that they are looking at, without a lot of prompting in advance. If you start asking interviewees narrow, highly specific questions right at the beginning, then your feedback may not be as helpful. There's a quote often attributed, but maybe not necessarily from Henry Ford, that captures this idea perfectly. So I'm going to say it anyway. Quote, if I ask people what they wanted, they would have said faster horses. What he means is, he knew that there was a need among his customers for something better than they were doing getting from point A to point B. Now, since they were only thinking of horses, they may have just said faster horses. But he recognized that he wanted them to go from point A to point B faster, and he had a new solution for doing that, the automobile. Now, this doesn't mean it's not good to talk to audiences about their ideas. Rather, it's about asking the right questions about their goals and background without necessarily providing the specific solution up front. The specific solutions are part of your job as a designer. Here are a few questions and assessments that can help you optimize your visualization for the audience. What is the persona's knowledge of the subject matter? Familiarity with the data displays in general? The context and platform with which they might be viewing data stories? Their interests, needs, and goals that the story might be potentially serving? And, also, potentially, how often they may be looking at this data story or who else they may share it with? Now, in a later interview, it might be helpful to show a few design ideas once you've established all the open-ended questions and answers. Showing a few design sketches can be better than just showing one idea, because it provides a better way for the interviewee to consider and respond to alternative approaches rather than immediately locking in to only one choice Now the context or scenario in which your audience will be viewing the story is a key design consideration. Will this presentation be in a meeting with a large and varied group with a set of different personas, or will it be a very narrowly focused group? How do you determine the most effective level of detail to show? How much interaction is there with the presenter and the visualization? How long is the story? These may seem like a lot of questions, and they are, but many of these considerations can quickly become second nature once you start working on your design projects. The ability to put yourself into the shoes of your audience will make you a better communicator. Along with the specifics of the individual persona, there are also common characteristics about how the human brain operates that apply to just about everyone. And that can also affect the way that you present and create your story. This is the crossroads of psychology, and data science, and neuroscience. In the next lesson, we will dive deeper into this topic, but I'll note a few ideas here. In general, people are very good at pattern detection of many kinds, and reading stories into them, even if there is no real story there, as we've already learned. That's sometimes called a false narrative. When you're looking for what stories to tell to your audiences, you need to make sure you are not seeing stories that don't really exist and then presenting and perpetuating those same stories. You also need to consider that people can sometimes read stories into your visualization that you did not intend or ever expect. It's something to consider. Let me tell you a little story, when I was a child, that I've never forgotten, and I think pertains to this. When I was a young boy, I loved dinosaurs. I'd look at some hardened clay on a hillside near my home and think, there must be a Tyrannosaurus skull in that dirt. I'd dig and chisel around the contours of what seemed like a fossil. And the more I chiseled, the more it looked like what I was expecting. I did this until the rains came, turning that excavation into a pool of mud. That dinosaur skull was never really there. I created it based on my expectations. The same thing can happen to you when you're digging through data, and think you're seeing something, and then pursue that. This is sometimes called confirmation bias. You look for and amplify things that you expect to find. Simple awareness of this bias can help you avoid doing it. Make sure that your data story really includes everything that's needed for a complete picture. Because stories and visualizations can have a powerful effect on the brain and can easily create false narratives that seem very true, or a partial story that seems complete and lead to wrong conclusions. We need to be very careful about that. So understanding your users in terms of personas, in terms of their particularities, and in terms of the way their brains and minds work, is very important. We'll examine some more examples of this in the next lesson. See you later.
