Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post acquisition processing and visualisation of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualisation of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilising data from a 256$times$256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterisation of the structural properties of materials. These include the techniques of virtual detector imaging; higher order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate nanoscale lattice parameter mapping with a fractional precision $le 6times10^{-4}$ (0.06%).
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