Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically non-trivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that ``intelligently'' help users to analyze their data.This article provides a first survey to almost 30 years of research on Intelligent Discovery Assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ``ideal'' future IDA as well as the challenges on the road ahead.