Real-time in-pit truck allocation (dispatch) systems have been used in the mining industry to improve the performance of truck-shovel operations. These systems also produce data that is traditionally used to generate production reports and ultimately production rates. Modern systems are generating increasingly more data however; these quantities are becoming larger than can be feasibly analyzed by humans. These 'data rich, information poor' issues have been addressed in other industries through the use of data mining. Data mining is set of new and old analysis tools that find patterns and relationships in data. This paper describes research that involves the detailed analysis of historical truck allocation records. The analyses included determining how the trucks performed under particular conditions and allocation systems. The long-term benefits of this work are to identify strengths and improvement opportunities in truck assignment systems, develop more complex haulage performance metrics, and to establish the skill sets and IT infrastructure needed to undertake more complex data-driven technology such as a truck dispatcher trainer.