Introduction
Researchers at universities, commercial firms and public organisations, develop, deploy, and evaluate machine learning (ML) algorithms (Balducci and Marinova, 2018; Chapman et al., 1999; Ma and Sun, 2020; Shankar and Parsana, 2022). Although Chapman et al. (1999) propose guidelines for deploying ML models, organisations often fail to consider their analytical capabilities and limitations when making such decisions, leading them to implement overly sophisticated data storage systems or prematurely introduce ML algorithms. Imagine an organisation that makes limited use of data. It is not ready for ML; it would need to build a data warehouse first or purchase external data...