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Facilitated regulatory compliance and auditing. Interoperability enables data-set integration. Data exchange across functions enabled insight generation (e.g., competitive intelligence, market access, clinical development), and quality AI training data sets for LLM.
Level 4 - Questions to ask
Are the metadata “grounded” in shared vocabularies?
Does the data and metadata make relaxed use of ontologies and vocabularies that are themselves, FAIR?(WIP input needed): questions that speak to other dimensions
How is FAIR data distributed across departments and divisions within the organization , and what are the key characteristics of this data in terms of identifiability and resolvability?
How are cross-domain analytics tools enabled within the organization?
How does leadership ensure that FAIR implementation is integral to the organization's data governance processes and risk management practices?
Describe the components of the FAIR data strategy in place, including its accommodation of both centralized enterprise-level and federated domain-specific FAIR data.
How does a Citizen Data Scientist utilize FAIR data within a pharmaceutical company?
Explain the involvement of business roles in contributing to the creation of FAIR data resources (e.g. knowledge graphs). How are these roles promoting and monitoring the value of FAIR data within the organization?
Discuss the development of organization-wide standards for FAIR data management and the integration of FAIR processes within a broader community .
How are assets and support utilized to appropriately train everyone in the organization on FAIR data?, and
How does the organization establish a registry of FAIRification tools and ensure their integration into company-level processes to support FAIR data adoption and governance?
Level 4 - FAIR data
One can find "FAIR" data across departments and divisions in an organization.
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