The asymptotic equivalence of canonical and microcanonical ensembles is a central concept in statistical physics, with important consequences for both theoretical research and practical applications. However, this property breaks down under certain circumstances. The most studied violation of ensemble equivalence requires phase transitions, in which case it has a `restricted' (i.e. confined to a certain region in parameter space) but `strong' (i.e. characterized by a difference between the entropies of the two ensembles that is of the same order as the entropies themselves) form. However, recent research on networks has shown that the presence of an extensive number of local constraints can lead to ensemble nonequivalence even in the absence of phase transitions. This occurs in a `weak' (i.e. leading to a subleading entropy difference) but remarkably `unrestricted' (i.e. valid in the entire parameter space) form. Here we look for more general manifestations of ensemble nonequivalence in arbitrary ensembles of matrices with given margins. These models have widespread applications in the study of spatially heterogeneous and/or temporally nonstationary systems, with consequences for the analysis of multivariate financial and neural time-series, multi-platform social activity, gene expression profiles and other Big Data. We confirm that ensemble nonequivalence appears in `unrestricted' form throughout the entire parameter space due to the extensivity of local constraints. Surprisingly, at the same time it can also exhibit the `strong' form. This novel, simultaneously `strong and unrestricted' form of nonequivalence is very robust and imposes a principled choice of the ensemble. We calculate the proper mathematical quantities to be used in real-world applications.