Deformation monitoring in open-pit mining operations is critical to ensure operational safety. Conventional in situ geodetic techniques yield predominantly sparse, point-based measurements, which restrict the ability to characterize deformation processes in a spatially comprehensive manner. In contrast, Interferometric Synthetic Aperture Radar (InSAR) can provide many measurements of ground displacements; however, its operational uptake is constrained by the complexity of data-processing workflows and the challenges associated with interpreting the resulting measurements. Although analysis-ready, InSAR-based ground-motion products have recently become available, their interpretation remains challenging for non-expert users. We propose a semi-automated workflow to post-process EGMS (European Ground Motion Service) displacement time series for operational monitoring of open pits and tailings dams in cold regions with seasonal data gaps, and we evaluate it using two test sites in Finland. In such environments, missing winter data can introduce phase-unwrapping artefacts in the time series, appearing as winter-only displacement offsets of approximately half the Sentinel-1 radar wavelength, which can easily be mistaken for real ground motion. The time series are pre-processed to identify and remove measurement points affected by phase-unwrapping errors. In order to support the interpretation of the data, subsequently time series clustering is performed. This is done either in a reduced-dimensional representation or directly in the full feature space. Cluster selection is automated using a combination of heuristic criteria and a custom metric based on temporal homogeneity and consistency. The findings show that the semi-automatically detected clusters are plausible with regards to a visual interpretation of the EGMS data.