The field of provenance analysis has seen a revival in the last decade as quantitative data-acquisition techniques continue to develop. In the 20th century, many heavy-mineral data were collected. These data were mostly used as qualitative indications for stratigraphy and provenance, and not incorporated in a quantitative provenance methodology. Even today, such data are mostly only used in classic data tables or cumulative heavy-mineral plots as a qualitative indication of variation. The main obstacle to rigorous statistical analysis is the compositional nature of these data which makes them unfit for standard multivariate statistics. To gain more information from legacy data, a straightforward workflow for quantitative analysis of compositional datasets is provided. First (1) a centred log-ratio transformation of the data is carried out to fix the constant-sum constraint and non-negativity of the compositional data. Next, (2) cluster analysis is followed by (3) principal component analysis and (4) bivariate log-ratio plots. Several (5) proxies for the effects of sorting and weathering are included to check the provenance significance of observed variations and finally a (6) spatial interpolation of a provenance proxy extracted from the dataset can be carried out. To test this methodology, available heavy-mineral data from the southern edge of the Miocene North Sea Basin are analysed. The results are compared with available information from literature and are used to gain improved insight into Miocene sediment input variations in the study area.