Cost-Benefit Analysis of Data Intelligence -- Its Broader Interpretations

TL;DR

This paper explores the broader interpretations of an information-theoretic metric for analyzing the cost-benefit of data intelligence workflows, connecting it to various fields like encryption, compression, and cognition.

Contribution

It extends the interpretation of a data intelligence cost-benefit metric by relating it to multiple disciplines and practical applications.

Findings

The metric can be related to encryption and compression processes.

It provides insights into model development and perception.

Connections to language and media are established.

Abstract

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.