The success of data-rich imaging techniques in material science lies in the promise that materials' properties are linked to recurring patterns that can be discovered by inspecting a few representative examples. Machine-learning techniques can leverage this approach by removing the human inspection as the limiting factor to digest larger and larger amounts of data in order to discover relevant, but possibly rare patterns. At the same time, they offer the unique chance to characterize the underlying distributions in a statistically significant manner as more data becomes available, thus generating secondary high-level characterizing data that might serve as valuable descriptors for associated properties. Digitalizing the entire workflow from synthesis, sample preparation, data acquisition and post-processing in an integrated way as sketched in Fig. is critical to achieve these goals.