Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at the near-atomic scale . The data obtained by APT takes the form of a mass spectrum, from which the composition of the analysed material can be extracted, and a point cloud that reflects the distribution of all the elements within the region-of-interest of the material being studied. Material-relevant data must be extracted from this point cloud through the use of data processing or mining techniques. These go from simply the local composition of a phase or a microstructural object, sometimes extracted via cluster-finding or nearest-neighbour algorithms today classified as machine-learning but used in the APT community for many decades . Phase morphology or even partial structural information can be obtained but the information can be limited or distorted because of trajectory aberrations that are caused by heterogenities in the specimen's end shape down to the near-atomic scale. Today, data reconstruction and processing is most often done in commerciallyavailable software, which does not allow for exploiting the cutting-edge methods arising from big data and machine-learning, and also remains very much user-depedent . The enormous potential to mine atom probe data is clear, but this requires complete FAIR-compliant analysis workflows that make use of machine-learning to facilitate more reliable and reproducible data processing and extraction, to really go beyond what human users can achieve. This section reviews challenges of APT data analysis (partially) solved by the application of machine learning and points out the remaining crucial locks to be addressed in the future.