Museums contain vast amounts of information and knowledge, providing a vital source of engagement for diverse audiences. As society becomes ever more digital, museums are moving towards making their collections available online to the public. However, just providing a searchable interface to the entirety of the collection could be a barrier to successful engagement. Tremendous craftsmanship is put into creating interesting and informative in-person curations of selected items, and a challenge exists in replicating this online. One solution could be the application of recommender systems, which personalise information to the individual based on their previous interactions and tastes. These systems power many popular online services, but cannot be applied without considerations and decisions being made about the data that is given to the engine. As museum collections vary in their nature and content, particular care should be taken when handling the data – standard methods may not apply. In this paper, we present the challenges of data curation in the context of using machine learning techniques with museum collections, supported by two case studies.