Wide-spread availability of low-cost digital sensors has made the acquisition of full-field experimental measurements less challenging, with modern measurement systems capable of obtaining three dimensional (3D) data fields. This presents difficulties when comparing computational and corresponding experimental data that often do not share the same orientation, scale, coordinate system or data pitch. This paper presents a method for performing quantitative comparisons of 3D data fields, irrespective of the source from which they are acquired. Two case-studies, each involving a pair of computational and experimental datasets, were used in this paper to demonstrate the capability of the method. The first case study represented the internal 3D strain fields in a reinforced-rubber matrix specimen under tensile load, measured using digital volume correlation, whilst the second study involved time-varying, surface displacements of an aerospace panel under resonance, which were measured using digital image correlation. The proposed orthogonal decomposition-based method works by representing 3D datasets as feature vectors, thereby allowing one-to-one comparison of the datasets within the feature vector space regardless of whether the original datasets share the same coordinate system, scale or data pitch.