  The core of data science is our fundamental understanding about data
intelligence processes for transforming data to decisions. One aspect of this
understanding is how to analyze the cost-benefit of data intelligence
workflows. This work is built on the information-theoretic metric proposed by
Chen and Golan for this purpose and several recent studies and applications of
the metric. We present a set of extended interpretations of the metric by
relating the metric to encryption, compression, model development, perception,
cognition, languages, and news media.
