This talk provides different perspectives on using data visualization to assist and inform choices. We face many choices in our personal and professional lives. Computing has made it easy to compile large numbers of options to choose from. Identifying the best solution among such a set is called multi-attribute choice. With no objectively optimal solution present, our human judgment is needed to trade off conflicting goals.
Data visualization is a powerful tool to help us explore and make sense of available courses of action. While many interactive visualizations already live in the context of decision-making, how to design for humans who make decisions with visualized data continues to be a vibrant research area. In this talk, I will outline several properties of multi-attribute choices that we encountered when studying real users and data. I will also hint at how disciplines like decision theory might help with that. Finally, I will layout some open visualization challenges along with two examples, where our visualizations helped engineers learn what level of performance is achievable under which conditions, even for co-dependent choices.