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The analysis of battery materials regarding their microstructure provides key insights on their performance in the target application, e.g., in terms of electrical conductivity, durability, or resistance to destructive exothermic reactions upon damage. Typically, high resolution scans on a large fields-of-view are required for this purpose, which implies rapidly increasing dataset sizes. This work introduces a big data analytics approach integrating segmentation and quantification techniques, which are scaling with large high-resolution computed tomography data, in order to generate rich computed tomography data. Subsequent visualizations support the final decision making. Representative results of this method are demonstrated on a commercially available 18650 cylindrical lithium-ion battery cell.