The significant increase of rotary blasthole drilling technology in recent years requires enhanced utilisation tactics of the “Big Data” being produced, to gain insight into operator performance and increase drilling efficiency. An area often overlooked by production is the drill cycle, due to the delayed nature of the downstream effects resulting from poor drilling. The project objective was to develop a prototype Key Performance Indicator (KPI) scorecard model to assess and rank drill operator performance, and provide a two-way feedback mechanism for training and development. Furthermore, to identify applications of the KPI scorecard model to enhance utilisation of Big Data in large-scale drilling operations. The prototype scorecard model was based on three main KPIs - rate of penetration, accuracy to plan, and cycle time. The dataset used to develop the scorecard was based on four different through-seam drill patterns. A main component of the scorecard model is a ranking system that enables feedback on an operator’s proficiency based on these indicators and supporting parameters. By analysing the established scorecard model and ranking system it was found that 13 operators, of the 37 in the dataset, had sufficient data to produce a scorecard and rank them. The results reflect trends in the raw data and provided a strong indication of operator proficiency, identifying areas for improvement.