Why FHIR Can Be Your Best Data Product and How to Get It Right
Building a data platform is hard
, really hard.
You can't just dump a bunch of files into an S3 bucket and call it a data lake. Without proper structure, governance, and control, your so-called "data lake" quickly turns into a data swamp. To truly harness the power of data, you need:
A real data layer – built using the medallion architecture (bronze, silver, and gold layers)
Data contracts – to ensure consistency, governance, and trust across data pipelines
But what if you're building on FHIR?
FHIR as a Data Product
FHIR (Fast Healthcare Interoperability Resources) provides a ready-made data model that is tailor-fit for healthcare data. Instead of spending months (or years) defining schemas and terminology, FHIR gives you:
A schema ready for purpose – FHIR resources like Patient, Encounter, and Observation are pre-defined and aligned with clinical workflows
Standardized terminology – Out-of-the-box support for LOINC, SNOMED CT, and other healthcare standards
National implementation guides – Ready-to-use profiles for specific data domains aligned with national policies
Seamless data exchange – You can connect with other FHIR servers and exchange data effortlessly
With FHIR, you're not just building a data platform – you're building a data product that serves multiple stakeholders with standardized data models.
Why Data Contracts Matter in FHIR
Even with a schema provided by FHIR, you still need data contracts to align expectations between data producers and consumers. A data contract ensures that:
The structure and quality of data meet defined standards
Stakeholders (clinical teams, data engineers, and product managers) agree on data governance
Changes to the schema or data pipeline are communicated and managed properly
Success Requires Collaboration
Data engineers can't build FHIR-based data platforms in a silo. Success comes when data engineers, clinicians, and product owners work together to:
Define fit-for-purpose data contracts
Validate incoming data for accuracy and consistency
Monitor and refine data pipelines continuously
FHIR: The Good, The Bad, and The Reality
What FHIR Solves
✅ Standardization of healthcare data concepts and terminology ✅ Managing data from hundreds (or thousands) of sources with one platform ✅ Core platform standardization as a single data product ✅ Future-proofing your architecture for downstream applications
What FHIR Doesn’t Solve
❌ FHIR validation is needlessly complex and costly ❌ Data quality is questionable depending on the source – curation is a must ❌ FHIR's RESTful design is not optimized for analytics – you’ll need to flatten the data ❌ Fit-for-purpose analytics data products still need to be built on top of FHIR
Building Analytics Data Products with FHIR
Even with FHIR’s standardization benefits, analytics data products need to be curated. Since FHIR data is optimized for exchange and not for reporting, you’ll need to:
Flatten and transform data for analytical queries
Enrich and aggregate data to support reporting use cases
Apply medallion architecture to layer and optimize data for downstream insights
Final Thoughts
FHIR makes building a healthcare data platform easier, but it’s not a silver bullet. You still need:
Data contracts to define, govern, and enforce data integrity
A data engineering strategy that flattens and curates FHIR data for analytics
Collaboration between data engineers and domain experts to align data products with business needs
When done right, FHIR doesn’t just standardize your data – it transforms your entire data platform into a future-ready, scalable data product.