This is an article from the Pistoia Alliance FAIR for Pharma community. For more information about this cross-industry initiative, visit our community webpage.
Clinical trial data locked in silos, PDFs and slide decks? FAIR4Clin is a practical guide to map the complexity of FAIR implementation in the clinical space.
You ran a clinical trial. The data exists. Somewhere.
When a colleague needs a cross-trial comparison, or when a regulatory question surfaces three years after database lock, the hunt begins. Spreadsheets, CRO hand-offs, metadata buried in a slide deck from 2019, a key variable defined slightly differently in every study. Weeks of detective work before any actual science, or an answer to a regulatory question, can happen.
This is not a technology problem. It is an organisational design problem. FAIR data would be the answer. And FAIR4Clin is the guide to navigate the complexity of FAIR in the clinical space.
What this article is about
FAIR4Clin, the free guide from the Pistoia Alliance, tackles three things this article will preview: why clinical and real-world data needs FAIR specifically, what a FAIR-by-design approach looks like across the full study lifecycle, and what leading pharma companies are already doing to make this real.
Who should read this
If you work in clinical data management, biostatistics, regulatory affairs, RWE, data architecture, or digital and AI strategy in pharma or life sciences, and you have felt the pain of data that is technically “there” but practically unusable, this is for you.
It is also for those building the next generation of data platforms and asking: how do we avoid recreating the same silos in a modern wrapper?
Is Your Clinical Data Working Hard Enough?
1. The clinical data problem has a name — and a specific answer
Clinical data is not “just data.” It is multi-source, heavily regulated, generated under conditions designed for regulatory submission, but not for future reuse, cross-study integration, or AI.
The result is predictable: fragmented datasets across CROs, sites and internal systems; inconsistent or missing metadata; consent terms never designed for secondary use; and standards (CDISC, OMOP, FHIR, SNOMED…) that are each valuable but often applied in isolation, without the semantic glue to make them talk to each other.
FAIR (Findable, Accessible, Interoperable, Reusable) gives a common language to diagnose and address these gaps. But generic FAIR guidance rarely speaks the language of clinical trials. FAIR4Clin does.
It is written specifically for study-level data: clinical trials, registries, and real-world evidence. It helps you identify where the gaps are in your own processes and understand what “FAIR” means in practice; not as an abstract framework, but as concrete choices made at each phase of the study lifecycle.
2. FAIR is a design principle, not a last step
One of the most useful contributions of FAIR4Clin is this: FAIR is not something you do after a study is complete. It is something you design in from the start.
The guide walks through the clinical study process through a FAIR lens, from protocol and data management plan through data collection, curation, analysis, and ultimately secondary use and sharing. At each step it asks: what metadata is needed? Which standards should apply? How are consent and permitted uses encoded so they remain machine-readable downstream?
It also clarifies how the major clinical data standards relate to each other and to FAIR:
- CDISC (CDASH, SDTM, ADaM): the regulatory foundation for structured clinical trial data.
- OHDSI / OMOP: common data model for large-scale observational and real-world data analytics.
- FHIR®: modern API-driven health information exchange across EHRs and registries.
- Semantic models (BRIDG, NCIT, others): the conceptual glue that aligns meaning across systems.
The key point FAIR4Clin makes explicit: using CDISC or OMOP alone does not make data FAIR. The missing pieces are machine-actionable metadata and semantic clarity, including persistent identifiers, provenance, licensing, and shared vocabularies that let datasets find each other and be trusted when reused.
This is what distinguishes FAIR4Clin from a standards compliance checklist. It is a roadmap for designing clinical data that can be discovered, trusted, and reused for follow-on analyses, regulatory queries, AI, and cross-organisation collaboration.
3. Leading companies are already doing this
FAIR4Clin is not theoretical. It documents how major pharma companies are operationalizing these principles today:
- Roche: prospective FAIRification at the point of entry. Microservices harmonise clinical and non-clinical data, embedding standards and quality checks from the outset rather than retrofitting them years later. Read more
- Bayer uses COLID, a corporate metadata and identifier platform using URIs, RDF and SPARQL to make data assets discoverable and linkable across the enterprise. See also Streamlining Real-World Data Access: FAIR Data Practices at Bayer
- AstraZeneca: set up very early one enterprise URI policy across business domains, including clinical, ensuring data can be found, linked and reused consistently regardless of source system. Read more
These are not pilot projects. They are infrastructure decisions being made at enterprise scale. The message is clear: FAIR is becoming core clinical data infrastructure. Organizations that design for it now will avoid costly retroactive work later.
What can you do with this?
You do not need to redesign your organisation to start. Three concrete steps for the next 90 days:
- Run a quick FAIR baseline. Pick 2–3 representative studies. Ask: can we easily discover them? Are identifiers stable across systems? Is metadata structured and machine-readable?
- Update your protocol and DMP templates. Add a requirement for standard identifiers, named vocabularies (CDISC, SNOMED, LOINC), and a high-level secondary-use plan. This costs almost nothing and prevents enormous friction later.
- Pilot on one concrete use case. For example: “make our oncology portfolio FAIR at the study level for cross-trial analysis.” Measure time saved. Build your internal case from evidence, not advocacy.
And if you lead a data, digital, or R&D function: treat FAIR as clinical data infrastructure, not a compliance checkbox. The organisations named above are making it an enterprise decision. The sooner it is designed in, the less there is to retrofit later.
Where to learn more
The full FAIR4Clin guide is free, open and available here.
We also welcome your feedback as we develop the next version!
Also consider joining the FAIR for Pharma community at the Pistoia Alliance.
This cross-industry community has been creating practical tools, frameworks, and thought leadership for implementing FAIR data since 2019. Consistent, reusable clinical data builds on a shared infrastructure for the whole industry, built once by the industry. Learn more
If your organisation is not yet a member of the Pistoia Alliance, this is an opportunity to explore how we support collaboration across data, AI, interoperability, and digital transformation in life sciences.
About the authors
This article reflects the collective expertise of members of the FAIR for Pharma Community within the Pistoia Alliance.
Our main contributor was Tara Kumar Gajula. The final article was edited by the community facilitator, Giovanni Nisato.
With thanks to the FAIR for Pharma Steering Group and the Best Practices and Business Value working groups for their contributions, ideas, and energy throughout the year.
Join the conversation
- Where does your organisation sit on FAIR for clinical and real-world data — designed in from the protocol, or bolted on after database lock?
- What’s the single biggest barrier to making your study data findable and reusable?
- Share your perspective in the comments and connect with others working to make clinical data FAIR!
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