Most modern engineering materials exhibit a complex microstructure that underpins the properties of the material in beneficial -or sometimes detrimental -ways. This applies to structural alloys, to ceramic materials like concrete or protective coatings, as well as to functional materials for energy storage, electronics, heterogeneous catalysis, etc. Steels, for example, consist of several meta-stable phases formed during casting, thermo-mechanical processing, or in operation. To image the interplay of grain morphology and texture, chemical composition, crystallographic relationships, and local properties of distinct regions, various complementary 'imaging' experiments are available, such as electron microscopy, atom probe tomography, beam diffraction (electron, X-ray, synchrotron), or spatially resolved spectroscopy. Thanks to progress in experimentation, data storage capacities, and digital data processing, these techniques yield an ever-increasing data pool. A single experiment can provide GBs or even TBs of data, which is further multiplied by high-throughput experimentation or in situ monitoring of transformations that add a time dimension. This big data is both a challenge and a great opportunity for data-driven research.