The design and evaluation of most current information visualization systems descend from an emphasis on a user’s ability
to “unpack” the representations of data of interest and operate on them independently. Too often, successful decision-making and
analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although
humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across
users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations
in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a
framework for the design and evaluation of information visualization systems, and demonstrate its use.