Data Visualization Feedback: Evaluating Choices, Not Checklists

I’ve encountered a scenario often enough that I’m willing to guess you have, too. You put together a data visualization piece, toss it out to the community, and the feedback starts rolling in (perhaps layered into “praise sandwiches”):

“Don’t use red, people connect it to something negative.”

“Don’t reverse your y-axis, it’s confusing”

“You need more white space, it feels cluttered”.

Directives (“do this” or “don’t do that”) flood in, leaving you with a task list of changes to make. The community encourages us to iterate on feedback, so we offer up a version 2 for more criticism, and then perhaps a version 3, and so on — until we’re left with something we call “done” after crossing off all of the to-do’s we were given.

Directive feedback usually comes from a helpful place, often well-grounded in data visualization theory and best practices. However, directives fail to take into account the creator’s design strategy: why did the creator choose to do things a certain way?

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Read the rest of the post in the Data Visualization Society publication.