3D microstructure characterization by means of serial sectioning has become a mature field in recent years. The advent of fast sectioning systems (plasma FIB, femto-second laser) make possible the rapid acquisition of large data sets, measuring in the multi-Tb range. Converting this data into a usable 3D model remains a challenging task because many important samples have been exposed to an external influence (e.g., deformation) which often negatively impacts the signal-to-noise ratio of electron back-scatter patterns (EBSPs). Traditional Hough-based indexing approaches are now being replaced by machine learning algorithms, including dictionary-based indexing, convolutional neural networks, and spherical harmonic transform indexing. In this contribution we will highlight the state-of-the-art in EBSD data analysis as applied to large scale multi-layer multi-modal data sets; in particular we will describe example of the recently introduced spherical indexing technique which may, in the near future, rival the real-time performance of Hough-based indexing.