Unifying biobank research at scale with a metadata knowledge graph & RAG automation

Setting the Scene

The customer’s R&D teams depend heavily on population-level biobanks to understand disease pathways and validate hypotheses to accelerate drug discovery. Each biobank operates in isolated, secure environments with different metadata models, tools, and scripting requirements.

This forced researchers to rebuild workflows, relearn technologies, and repeatedly validate logic, slowing scientific progress and increasing operational and governance overhead.

The customer sought a solution to eliminate duplication of effort, standardize how research questions are executed across biobanks, and create a governed foundation for AI-assisted scientific workflows. They partnered with Datavid to deliver an 8-week Proof of Value demonstrating how an ontology-driven metadata foundation and agentic RAG workflows could standardize cross-biobank research without compromising security.

WHY THIS MATTERS FOR CDOs 

This initiative directly supports the CDO mandate to make enterprise data governed, interoperable, and AI-ready. By harmonizing metadata and automating script generation, the PoV demonstrated reduced operational friction, improved data traceability, and enabled faster access to trusted insights in secure, siloed environments while maximizing the value of existing data investments.

The Challenges

As the customer’s teams navigated different biobank platforms, models, and constraints, several critical challenges emerged that needed to be resolved before scalable cross-biobank research could become a reality:

1. Technological fragmentation
   Each biobank required different development approaches and platforms, increasing training and onboarding effort and slowing time-to-insight across research teams.
   

2. Isolated development
   Biobanks operate in highly secure, restricted environments that prevented reuse of shared logic and standardized tools. This forced researchers to manually debug and redeploy code for every environment.

3. Cross-biobank inefficiency
   Solutions had to be rebuilt for each biobank, reducing efficiency and complicating validation across cohorts.
   

4. No automation possible
   Scripts had to be executed manually due to security rules, slowing analysis and creating unnecessary operational overhead.
   

Is your organization ready to make its
data interoperable, AI-ready, and reusable at scale?
Datavid enables CDOs to establish metadata-driven foundations that accelerate
insight generation and reduce operational overhead.

The Solution

The customer envisioned an ontology-driven metadata platform, powered by the Datavid Rover semantic layer and RAG architecture, to standardize how research questions are asked, interpreted, and executed across multiple biobank environments.

Key elements of the solution: 

• Governed metadata knowledge graph 
  Unified data definitions across two biobanks using both internal and industry-standard models (OMOP 5.4) to ensure consistent understanding of research concepts.
  

• RAG-driven workflow automation
  Translated natural-language research questions into fully validated, ready-to-execute analytical workflows, reducing manual effort and ensuring consistency across biobanks.

• Agentic LLM orchestration 
  Used specialized agents for query interpretation, script creation, validation, and guardrails, ensuring accuracy and reproducibility across environments.
  

• Secure and scalable architecture 
  Implemented a governed, enterprise-grade foundation that supports large-scale ingestion, enrichment, and analysis across biobanks while maintaining strict security controls.
  

Together, these capabilities showed that cross-biobank research can be standardized, automated, and scaled without compromising security or compliance. The PoV validated a repeatable model for onboarding additional biobanks and supporting future R&D initiatives, all within an 8-week delivery window.