  This arXiv report provides a short introduction to the information-theoretic
measure proposed by Chen and Golan in 2016 for analyzing machine- and
human-centric processes in data intelligence workflows. This introduction was
compiled based on several appendices written to accompany a few research papers
on topics of data visualization and visual analytics. Although the original
2016 paper and the follow-on papers were mostly published in the field of
visualization and visual analytics, the cost-benefit measure can help explain
the informative trade-off in a wide range of data intelligence phenomena
including machine learning, human cognition, language development, and so on.
Meanwhile, there is an ongoing effort to improve its mathematical properties in
order to make it more intuitive and usable in practical applications as a
measurement tool.
