The Dutch rail sector is one of the most heavily used rail networks in Europe. It plays a critical role in the domestic transportation of passengers. Disruptions in the operation of one organisation can have a cascading effect on others within the sector. Risk management needs to identify and address these weaknesses to prevent largescale disruptions. The risks a rail organisation faces are diverse, ranging from financial risks to strategic risks. The enterprise risk management methodology addresses all the risks of an organisation to reduce the negative effect and seize opportunities. In recent years, there has been increased emphasis on data-driven work, including within the risk management domain. This research explores the implementation of data-driven work in the enterprise risk management of the Dutch passenger transporting rail sector. This research uses a comparative case study to explore the novel research field. The case study data is collected from interviews and desk research. This research concludes that datadriven work adds value to the enterprise risk management of Dutch rail organisations. Data-driven enterprise risk management improves the predictive capabilities of rail organisations. In addition, it enables real-time monitoring of risks. Hence, it supports the decision-making process more precisely and accurately.