Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice'
This work addresses the challenge of making academic management theories practically applicable in data science for businesses, though it is incremental as it builds on existing ontology methods.
The paper tackles the problem of bridging management theories with data analytics by proposing to represent theories as formal ontologies and apply them to corporate data, demonstrating a preliminary case study with an accounting theory in First-Order Logic.
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.