Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice'

TL;DR

This paper proposes using formal ontologies of management theories to enhance data analytics by enabling theory-driven inference on corporate data, demonstrated through a case study on accounting theory.

Contribution

It introduces a novel approach of formalizing management theories as ontologies for practical data analytics applications.

Findings

Developed an ontology for accounting theory in First-Order Logic

Demonstrated potential for ontologies to bridge theory and data analytics

Preliminary results suggest feasibility of theory-driven data inference

Abstract

We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliche, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.