Within the heavy commercial vehicle sector, fleet availability stands as a crucial factor impacting the productivity and competitiveness of companies. Despite this, the core element of maintenance strategies applied in the sector still relies solely on mileage or component usage time. On the other hand, the evolution of the industry, particularly the advancement of Industry 4.0 enabling technologies such as sensorization embedded in components, now provides a vast amount of operational data. The severity levels of application, driving style influence, and vehicle operating conditions can be indicated through the treatment of these data. However, there is still little practical application of using this data for effective decision-making regarding maintenance strategy in the sector, correlating the severity level with component failure possibility. Seeking a disruptive approach to this scenario where data analysis supports decisions related to component maintenance strategy, a literature review was conducted to understand how aspects of Industry 4.0 and data analysis can influence maintenance strategies. As a result of this review, a methodology is proposed for applying structured data analysis based on a robust statistical foundation. A case study of applying this methodology is presented, with the analysis of operational data from a specific component installed in a fleet of heavy commercial vehicles. Through the application of statistical techniques, a variable representing component wear is correlated with variables describing application severity, demonstrating that enhancing maintenance strategies based on data analysis is feasible. With the increased accuracy of component maintenance criteria, a 10% increase in availability is estimated.