  The trouble with data is that it frequently provides only an imperfect
representation of a phenomenon of interest. Experts who are familiar with their
datasets will often make implicit, mental corrections when analyzing a dataset,
or will be cautious not to be over-confident in any findings if caveats are
present. However, the implicit knowledge about the caveats of a dataset are
typically not collected in a structured way, which is problematic especially
when teams work together who might have knowledge about different aspects of a
dataset. In this work, we define such analyst's knowledge about datasets as
data hunches. We discuss the implications of data hunches and propose a set of
techniques for recording and communicating data hunches through data
visualization. Furthermore, we provide guidelines for designing visualizations
that support recording and visualizing data hunches. We envision that data
hunches will empower analysts to externalize their knowledge, facilitate
collaboration and communication, and support the ability to learn from others'
data hunches.
