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