The Royal Netherlands Navy (RNLN) aims to bring new platforms into service across its force structure, including a combat support ship (CSS), anti-submarine warfare frigates (ASWF), air defenders, submarines, and various auxiliary vessels. Constant pressure to reduce ships' crews and the increasing complexity of systems aboard naval ships create challenges for the maintenance of these future vessels. This necessitates the development of improved shore support, provided by the Directorate of Materiel Sustainment (DMI). The rise in the number of sensors on board and the emergence of learning algorithms is essential to facilitate this. It offers an opportunity to identify failures at an earlier stage, better plan maintenance, and reduce (corrective) workload aboard ships through data analysis. Consequently, the RNLN is actively transitioning from its traditional approach of planned periodic maintenance with a high corrective workload towards embracing data-driven maintenance. This shift encompasses the increase of condition-based maintenance (CBM) and the adoption of predictive maintenance (PdM) based on advanced condition monitoring and data analysis techniques. This paper adopts a design science research approach, beginning with the identification and motivation of the problem. We then delve into an examination of the organizational challenges associated with the introduction of data-driven maintenance and explore solutions outlined in existing literature. Within this study, we employ four lenses as guiding design principles for the development of the social roadmap: maturity models, work system approaches, technology acceptance models, and change management models. We then proceed to outline the initial steps towards designing a social roadmap based on six guiding design principles from the four lenses. Furthermore, this paper presents practical examples of developments and challenges encountered in the implementation of data-driven maintenance, shedding light on the social dynamics involved in implementing data-driven maintenance within the RNLN's maintenance organization. By sharing these examples, we aim to provide insights into real-world experiences and considerations for practitioners and researchers. The paper concludes by outlining future steps envisioned for the ongoing implementation of smart maintenance within the RNLN.