The Findable, Accessible, Interoperable and Reusable (FAIR) principles aim to maximize the reuse of (research) data to researchers all over the world. Thereby, FAIR data contributes to liberty and equity among researchers. Today, FAIR data has become a hot topic, but many organisations are struggling where and how to start improving the FAIRness of their (research) data. People talk about FAIR data, but mostly in meetings that sadly never end with concrete follow-up actions. In this workshop we will teach how to start working on improving the FAIRness of data by playing a game. This FAIR game is based on our own experience of building a FAIR Data Point for the Nivel Primary Care Database. This FAIR Data Point is a single location for researchers to upload metadata about their (primary care) datasets so other researchers can find them across multiple repositories.Learning objectives: – How to start working on FAIRness in your organisation – Awareness of the different required roles (researchers, IT staff, managers) – How to communicate with colleagues and stakeholders on FAIRness, how to speak the same languageWe will go through a real-life session of improving FAIRness by playing a game. All of the attendants have a role to play in this game (researchers, IT staff,  managers). Whether or not this role matches their real-life job description, doesn't matter.  Workshop schedule: introduction (5 min), game round 1 (20 min), game round 2 (20 min), wrap-up (10 min).After playing the game, attendants have experienced how to start working on FAIRness of datasets. They have gained confidence and are ready to start working on FAIRness of datasets in their organisations.Working on FAIRness is not an easy task as it includes multiple related subtasks and it requires collaboration between different professionals (researchers, IT staff, and managers). However, by setting a clear goal, having the right people on board, and speaking each other's language, FAIRness of (research) data can be significantly improved. As a result, available datasets can be more easily and faster reused by researchers which accelerates the research lifecycle.