Some scientists still think that good data visualization is only necessary when presenting work to "the public." In truth, thinking hard about how to learn the most from any data set should always involve some form of graph, map, chart, or other visual statistical display. This talk will demonstrate how visualization techniques that include so-called "linked views" offer new insights to researchers visualizing large and/or diverse data sets. In particular, the talk will highlight a few high-dimensional visualization examples where ideas about linked views first put forth by John Tukey are extended beyond two-dimensional displays and point clouds. Examples will be principally drawn from astronomy and medical imaging, and software highlighted will include the Universe Information System known as "WorldWide Telescope" (worldwidetelescope.org) and a new python-based linked-view system called "Glue" (glueviz.org).