The Evolution of Data Science

Jennifer Lewis Priestley,Robert J Mcgrath

doi:10.4018/ijkm.2019040106

Abstract

Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production in other traditional academic disciplines as well as inform established knowledge management practices grappling with the emerging challenges of Big Data.

Highlights

The terms “big data”, “data science” and “analytics” have pervaded the global common speak over the past decade
Informed by Gibbons’ framework, the evolution of data science as an academic discipline can be contextualized using the lens of academia with an eye towards a sustained and formal contribution to the revised knowledge management pyramid (Jennex, 2017)
The authors posit that the evolution of data science can be explained using Gibbons’ framework of a “Mode 2” discipline of knowledge production; without the attributes present in Mode 2, data science would not/could not become a unique academic discipline

Summary

Introduction

The terms “big data”, “data science” and “analytics” have pervaded the global common speak over the past decade. Informed by Gibbons’ framework, the evolution of data science as an academic discipline can be contextualized using the lens of academia with an eye towards a sustained and formal contribution to the revised knowledge management pyramid (Jennex, 2017). The authors posit that the evolution of data science can be explained using Gibbons’ framework of a “Mode 2” discipline of knowledge production; without the attributes present in Mode 2, data science would not/could not become a unique academic discipline.

Results

Conclusion