Myths and misconceptions of data-driven methods: Applications to process safety analysis

With a rapid realization of process digitization, data-driven methods are being increasingly adopted in process safety analysis. However, the use of data-driven methods contains a varied degree of myths and misconceptions resulting from the application of a method or the data representation that does not follow a proper scientific notion. These myths and misconceptions cause significant errors in terms of results and their interpretability. Hence, the purpose of this study is set to analyze the most common myths and misconceptions of data-driven methods observed in the recent literature. In the current work, we have analyzed 500 public domain articles from 1990 to 2020, published in 10 renowned safety journals. The analysis attempts to address the following questions: (i) What are the key data in process safety analysis? (ii) What are the sources of data? (iii) What does the data-driven method mean? (iv) What are the common myths and misconceptions of data-driven methods? and (v) How frequently such myths and misconceptions are occurring? After analyzing the 500 articles, it is observed that most of the myths are related to improper data representation, missing appropriate assumptions, and blanket use of methods without a detailed understanding of their limitations. The authors believe this work will help peers to avoid the myths studied here, use data-driven methods with scientific rigor, and present findings in a meaningful way. (C) 2021 Elsevier Ltd. All rights reserved.