SAFETY DATA SCIENCE: BASIC FOUNDATIONS AND CRITICAL FACTORS

Abstract

This paper explores critical factors related to the application of data science in the field of safety and discusses plausive causes to explain why the herewith called safety data science (SDS) is still a distant reality in most organizations. To do that, existing literature was screened and inductive reasoning was used to identify potential barriers for the massive use of SDS. As a result, basic foundations are introduced and three critical factors are discussed: (1) the lack of theoretical foundations in SDS to guide empirical applications; (2) unavailability or poor safety data at the organizational level, and (3) the lack of expertise related to data science from the perspective of safety professionals. The study has important implications. From the theoretical perspective, it offers an initial conceptual baseline for SDS and presents its critical factors. Also, directions are given for safety practitioners to explore SDS at the organizational level.

Keywords

• safety data science
• sds
• critical factors
• barriers
• safety management