Knowledge Discovery from Data is one of the major challenge in the Digital Revolution, carried forward within the Industry 4.0 transformation. Role of new technologies is to centralize information and derive data-driven insights to enhance business performance. Derive real value from collected data is the main purpose of Data Analytics, achieved leveraging state-of-the-art techniques of Machine learning. This thesis deals with a relevant issue for automotive industries: Predictive Maintenance. The aim consists in identify in advance eventual failures in order to improve workshop repairs management. This work considers error signals on trucks as subject of the predictive analysis. Primary role is represented by Telematic, a complex system embedded in vehicles that, continuously monitoring their status thanks to numerous sensors, is able to create an increasingly connected fleet.