A generalized method is introduced to extract critical information from series of ranked correlated data. The method is generally applicable to all types of spectra evolving as a function of any arbitrary parameter. This approach is based on correlation functions and statistical scedasticity formalism. Numerous challenges in analyzing high throughput experimental data can be tackled using the herein proposed method. We applied this method to understand the reactivity pathway and formation mechanism of a Li-ion battery cathode material during high temperature synthesis using in-situ high-energy X-ray diffraction. We demonstrate that Pearson's correlation function can easily unravel all major phase transition and, more importantly, the minor structural changes which cannot be revealed by conventionally inspecting the series of diffraction patterns. Furthermore, a two-dimensional (2D) reactivity pattern calculated as the scedasticity along all measured reciprocal space of all successive diffraction pattern pairs unveils clearly the structural evolution path and the active areas of interest during the synthesis. The methods described here can be readily used for on-the-fly data analysis during various in-situ operando experiments in order to quickly evaluate and optimize experimental conditions, as well as for post data analysis and large data mining where considerable amount of data hinders the feasibility of the investigation through point-by-point inspection.