The aim of this study is to compare two archetypes of data fusion algorithms in order to provide a matched data file of EU-SILC and HBS where income (from EU-SILC) and consumption (from HBS) is jointly observed on the micro level. One, Random Hot Deck, is a classical Nearest Neighbour approach which was proposed by Eurostat, whereas the alternative data fusion method is based on Predictive Mean Matching. We discuss results from a simulation study to investigate benefits and potential drawbacks of both variants, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck.