This white paper presents a comprehensive validation of an analytics solution designed to detect and estimate wheel flat lengths using Wheel Impact Load Detector (WILD) data. The solution supports condition-based and predictive maintenance strategies for railway operations. A large-scale study involving over 5,000 wheels was conducted across multiple workshops and field locations in Europe. The validation process integrated digital inspection data with analytics outcomes, demonstrating high accuracy and operational viability. The findings support the deployment of this solution to improve reliability, reduce maintenance costs, and enhance safety in rail freight transport.