Correlation analysis has been a powerful paradigm to discover and analyze hidden properties and patterns of large-scale datasets for decades. At now, correlation analysis turns to be a perfect tool for supporting big multidimensional data analysis and mining, with a wide range of relevant properties, including the amenity of supporting meaningfully exploration and discovery of multidimensional ranges kept in such kind of datasets. These operators are thus the basis for several multidimensional big data analytical tools that can be designed and implemented on top of the foundations defined by correlation functions. In line this this scientific area, the talk will provide introduction and motivations, models and algorithms, and, finally, best-practices guidelines for effective and efficient implementations of correlation-analysis-based tools over big multidimensional datasets.