Episode description
Episode description
This episode is all about creating meaning from data, and making it easy for your audience to understand by making it visual. We can apply the same principles we use to design dashboards to presentations - whether you’re communicating design concepts (and the data-driven decisions behind your design) or user research findings. I had such a great time talking with my guest Thomas Watkins about how to show meaning using data, I know the term ‘data visualization’ sounds a little overwhelming to some people in UX, if you aren’t designing dashboards. But it doesn’t need to be. I think we did a pretty good job of making data visualization accessible for any level of data interest in the episode.
Everyone in UX should be using data - whether you’re in design or research or PM or developer or marketing - and using data in a visual way to communicate important information to the person reading it. If you create presentations, for your design or for your research, if you have any opportunity to use data to explain your design or research recommendations… this episode has great information for you. Whether you are data-curious or data-shy or you lean in the data geek direction like me. I learned some great guidelines about making data visual in more effective ways, and I’ll bet you will too, even if your eyes glaze over at terms like “magnitude comparison” or “scatter plot.” I love how Thomas talks about explaining the context of the numbers, and not just ‘decorating’ numbers with meaningless donut graphs, one of my pet peeves!
Thomas Watkins is the founder of 3 Leaf consulting, a design collective that combines psychology and design principles to create usable products and services. Thomas is a thought leader, speaker and industry practitioner in Houston TX. The scope of his work has included interface design for mobile, SaaS system architecture, usability research, and data visualization.
LINKS
https://www.instagram.com/3leafmethod
https://www.linkedin.com/in/watkinsthomas/
Show Link - Graph Selection Matrix
https://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf
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