The Royal Netherlands Navy (RNLN) aims to bring into service new platforms across its force structure, including a combat support ship, anti-submarine warfare frigates, submarines, and various auxiliary vessels. A constant pressure on reducing ships' crews and an increasing complexity of systems aboard naval ships creates pressure on the maintenance of these future naval ships. The increase in the number of sensors on board and the emergence of learning algorithms offer an opportunity to identify failures at an earlier stage, better plan maintenance and reduce the (corrective) workload aboard ships with the help of data analysis. The RNLN therefore works on a transition from planned periodic maintenance towards condition-based maintenance and predictive maintenance based on advanced condition monitoring and data analysis techniques, which necessitates the development of improved shore support. The RNLN developed the smart maintenance roadmap to guide this transition towards a data driven maintenance organisation. This roadmap covers the process of collecting raw operational ship data to the change in the RNLN’s asset management process and consists of five strategy lines: data acquisition, data infrastructure, data governance, data analysis and asset management. The activities described in this paper have been developed in close collaboration with academic partners and industry. This paper presents the smart maintenance roadmap and gives practical examples of developments and challenges within each of the five strategy lines. This paper concludes with future directions that are envisioned for the development of smart maintenance within the RNLN and reflects on the social aspects of implementing smart maintenance within the maintenance organisation of the RNLN.