The people at the organization you’re working with have an understanding of their organization and their data, but also in terms of visual language in general. Zach said that this is true of when you’re working on a data visualization project. But it’s enough from your life experience to put together, “Oh! It’s a row of Pepsi logos.” You don’t see a single complete logo, but you can take the incomplete thoughts and knowledge of grocery stores to figure out what it is. You can see part of the image on each one but you can’t quite see what they are. He went on to share an example about a grocery store aisle with two-liter bottles. You’re not always going to be able to stand next to it, hold their hand and explain exactly what it means.” So, when you’re constructing your visualization, you and the audience are both pouring meaning into it. He explained that, “In the sense that in everything you create, there’s a silent partner that you have (a second creator) which is the audience. Zach said that people been pictorially expressing ideas for a very long time and that a big part of the process of taking something visual and explaining it to someone is the idea of closure. It’s impossible to satisfy all audiences, to bridge all gaps.” And if it’s for everyone, it’s really for no one. “ Because if you don’t know who it’s for, it has to be for everyone. “One of the things I find most important in terms constructing a data viz is your understanding of who the audience is – who is this for?” Zach said. Zach and I both agreed that understanding your audience is paramount. Idea #3: An icon array focusing on how one-third of their revenue comes from ticket sales.Idea #2: A bar chart to make it easier to compare each revenue source.We also used direct labels in lieu of the separate legend. We developed a pie chart with one dark slice and the rest grayed out. It was 3D, had a separate legend, and used tiny font… All of the usual challenges. Their original graph was a pie chart with seven or eight slices representing their revenue sources. (This is usually the winner, and what people actually need).įor example, I recently worked I worked with a transportation agency. You come up with three ideas. I call these “ideas,” not “makeovers.”.You listen to the request for data. You’re listening, you’re nodding, not interrupting, asking clarifying questions.Designing Three Dataviz Ideas to Narrow Down What Users Actually Need “ You could easily put together something that is technically correct and absolutely worthless,” he explained. It’s not to say one is better than the other, just that they’re different roles.” Zach added, “A data professional is not more skilled than the IT worker. You might ask what the data’s going to be used for and what types of actions will be taken based on that data. The value you can add is to ask more questions and dig deeper. You read between the lines of what’s requested and then give them what they actually want and need. Professionally and respectfully, of course. The value-add of the data professional is that you’re supposed to push back a little bit. Those working as a data professional also might receive those requests. They might’ve even requested a specific chart type, like “Make a bar chart about xyz topic.” Sometimes it’s a specific request with lots of planning, and you can tell the person making the request really thought things through. Let’s pretend they’re being asked to make a dashboard. Those working in the traditional IT role typically receive instructions or a request. We discussed an idea Zach brought up in a previous podcast: The difference between a traditional IT role and a data professional. Distinguishing between IT Professionals and Data Professionals Prefer to listen? Download the episode here. Watch Our Conversation Listen to the Podcast We sat down at night after our kids were in bed (8 pm for Zach and 9 pm for Ann!) and talked about how to distinguish between IT and data professionals, how to narrow what users *actually* need, understanding your audience and how to speak up when data isn’t useful. Zach is a data analyst, a Tableau Public Ambassador, and passionate about data visualization and data storytelling. I recently had the chance to talk with Zach Bowders on his podcast, Data + Love.
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