As global demand for lithium, rare earth elements (REEs), nickel, and cobalt continues to grow, mining operations face increasing pressure to maximise recovery while supporting long-term sustainability. In this context, advanced analytical methods are required to reduce geological, metallurgical, and economic risks at every step of the ore-to-metal process. X-ray diffraction (XRD) is an established tool for mineralogical analysis and is conventionally used to provide phase identification and quantification on a per-sample basis. This paper presents a suite of case studies that combine traditional XRD approaches with large-scale data analytics. This integration improves result reliability and extends the value of XRD beyond simple phase identification. We demonstrate that quantitative phase analysis combined with data clustering provides a powerful framework for trend identification and geological modelling. Clustered XRD data from nickel laterite deposits differentiate between saprolite and laterite horizons, supporting efficient grade control and ore blending, and informing the selection of appropriate processing routes. The same approach, when applied to lithium and REE systems, provides mineralogical domain definitions that support processing performance prediction and exploration targeting. Integration of XRD with Partial Least Squares Regression (PLSR) enables reliable prediction of key process parameters, including material grindability. Finally, we present the Phase SNR (signal-to-noise ratio) method for smart filtering of minor mineral weight percentages. This approach enables more reliable automated quantification of problematic minor phases such as clays and quartz. Collectively, these case studies demonstrate that XRD, when combined with multivariate analytics, evolves from a descriptive mineralogical tool into a predictive framework for process control, geometallurgical modelling, and energy optimisation.