A significant challenge for graphene nanoplatelet (GNP) suppliers is the meaningful characterisation of platelet morphology in an industrial environment. This challenge is further exacerbated to platelet surface chemistry when scalable functionalisation processes such as plasma treatment are used to modify the GNPs to improve the filler-matrix interphase in nanocomposites. The costly and complex suite of analytical equipment necessary for a complete material description makes quality control and process optimisation difficult. Raman spectroscopy is a facile and accessible characterisation technique with recent advancements unlocking fast mapping for rapid data collection. In this work we detail several big-data methodologies that extract full value out of Raman spectra so that GNP morphology and surface chemistry can be characterised. A unsupervised peak fitting and processing algorithm was used to extract crystallinity data rapidly and accurately and correlate it with laser-diffraction derived lateral size values for a commercial set of GNPs. Classical machine learning was used to find the most informative Raman features for classifying plasma functionalised GNPs. The initial material properties were found to affect which peak features are most useful for classification. In low-defect density and low specific surface area GNPs, the D peak FWHM is found the most useful, whereas the 2D/G ratio is most useful in the opposite case. Finally, a convolutional neural network was trained to discern between different GNP grades with 86% accuracy. This work demonstrates how computer vision could be deployed for rapid and accurate quality control on the factory floor.