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Role of Analytics for Operational Risk Management in the Era of Big Data
Author(s) -
Araz Ozgur M,
Choi TsanMing,
Olson David L,
Salman F. Sibel
Publication year - 2020
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12451
Subject(s) - computer science , data science , analytics , big data , process (computing) , data analysis , focus (optics) , perspective (graphical) , data management , process management , data mining , engineering , artificial intelligence , physics , optics , operating system
Operational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man‐made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well‐regarded OM journals, including both empirical and analytical outlets. We highlight how the use of data analytics tools and methods have facilitated ORM. We discuss the need for data monitoring and the integration of various analytical tools into decision making processes by classifying the literature on application fields, analytics techniques, and the strategies used for implementation. We summarize our findings and propose a process to implement data‐driven ORM with future research directions.