A critical challenge is that present-day APT data processing tools and workflows are inherited from "traditional" interactive data analysis based on user-interactions, through a fixed set of data analysis techniques and visualization that leaves little flexibility to explore novel and processes, as summarised in Figure . User-input includes assignment of peaks to particular atomic or molecular species to manually retrieved structural information and microstructure segmentation and quantification. Machine-learning has the potential to automate many of these analysis steps, with models that are based on physical input and constraints. Some progress has been made across the community with dedicated machine learning algorithms to mine compositional and structural information . For instance, for mass peak assignment, we introduced an approach that uses known isotopic abundances to identify patterns in mass spectra, outperforming human users without loss of accuracy . Following reconstruction of the 3D point clouds, automated identification and quantification of grain boundaries were proposed, and for more general microstructure segmentation, Saxena et al. introduced an approach that uses clustering in the compositional space, demonstrating unique capabilities for segmentation of the various phases, along with the quantification of their composition and morphologies. These would normally have been extracted through manually positioned regions of interest, which is time-consuming and error-prone. Structural imaging by APT is hindered by the anisotropic spatial resolution and the limited detection efficiency . Recent efforts have managed to overcome these for the ever-challenging analysis of chemical short-range order (CSRO) by using convolutional neural networks, using the workflow in Figure 2 . A key challenge for the future is to move away from the developing individual tools to tackle isolated problems to think about complete data analysis workflows, from patches to a logical patchwork that will also facilitate adoption. A way to solve this would be to open the programs themselves via APIs at all levels, or at least facilitate data exchange through open data formats accessible to external processing by independent tools.