Are Viewers Expecting a Story? Lightning Talk from the DATAcated Expo

Jan 11th, 2022 / Data Visualization
How do you modify a graph so that it’s just right for your audience? Surely a group of scientists will need something different from a group of policymakers. Some audiences adore data. Others don’t. Some audiences have plenty of time. Others don’t. In this blog post, you’ll learn about: the differences between default, traditional, and storytelling graphs; which techniques can help you tell a story with data (e.g., dark colors); and when to use each type of graph.
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Accessibility Quick Wins: Remove Legends and Directly Label

Nov 30th, 2021 / Data Visualization
How do we make our graphs more accessible? There’s a misconception that accessibility takes all day, that’s it’s costly and complicated. Those are all false. Accessibility is woven into all my trainings, but since this is a topic I get asked about a lot, I decided to make a new talk that’s focused just on accessibility for dataviz.
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When Your Graph Is Too Smooshed to Read

You want to display a lot of historical data–great! But sometimes we have so many points in time that our graph’s labels get smooshy. In this post, I’ll show you a before/after data visualization makeover in which we selectively labeled a few key milestones in order to tell our story (and make the graph more legible).
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Removing Redundancies from My Old Graphs

Feb 28th, 2017 / Data Visualization
Data visualizers who critique others before critiquing themselves make me gag, so I opened my recent keynote with a trip down memory lane. Check out this beauty from one of my earliest jobs. I’ve anonymized it but 99.9% of my work from this period had this look and feel, i.e., an overly-labeled graph that I made in Excel and pasted directly into Word without any editing at all.
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