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Data silos and inconsistency impede accessibility and integration, resembling a "wild west" scenario. Limited awareness and engagement hinder the adoption of FAIR principles, with minimal leadership involvement and a lack of strategic focus. A formal FAIR strategy is necessary to avoid reactive application and the absence of structured pathways. Resistance and cultural barriers may impede FAIR adoption, accompanied by a lack of FAIR-related knowledge. The organization needs FAIR processes, and implicit processes may divert efforts from FAIR implementation.
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The organization initiates data conforming processes to local models in a shared data platform and progresses to system-level controls. These processes include Tools for metadata, controlled vocabularies, and persistent identifiers. Data is more “findable” thanks to unique identifiers. Emerging metadata and controlled vocabularies make accessing data with less specific knowledge requirements possible. Leadership awareness grows, initiating initial FAIR projects and forming champions within the company. Vision, strategy, and role development follow, integrating FAIR as a key element in the broader data strategy. Designated roles emerge, fostering prototypes and showcasing value. Formal training frameworks are structured, and pilot projects and governance considerations accompany the initiation of culture change processes. Informal communities of practice begin to form, facilitating knowledge exchange. We establish Infrastructure for Proof of Concepts (POCs) d, leading to organization-wide plans, RFPs, and evaluations of FAIR tools and profiles. At this journey stage, an increasing commitment to FAIR data principles, encompassing leadership engagement, role development, and systematic infrastructure implementation, is present. https://pistoiaalliance.atlassian.net/wiki/x/BIDFy
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