IndustriesLife sciences & healthcare
Clinical data pipelines, verified before they run.
More data engineering capacity for clinical and operational data: built, verified and operated end to end, with every change approved by your domain experts.
- Lineage
- EDC
- Visits
- SponsorDataset
- Build
- Deterministic and reproducible
- Simulated
- Late and deleted records replayed
- Policies
- PII removed before delivery
- Approved
- Clinical data engineering, pull request #112
01 / Use cases
Where teams start.
- 01
Per-sponsor or per-customer data delivery
A dedicated, governed pipeline for every customer, generated and operated at scale: the Medidata pattern.
- 02
Clinical data platforms
Study, site and patient data integrated into one platform with lineage from every source.
- 03
Operational data APIs
Current data for trial, care and supply operations, served to applications and AI agents.
Each one is a single pipeline you can scope, build and evaluate in a Proof of Value.
02 / Why it matters
In clinical data, a pipeline error is a data-integrity finding.
Your quality and validation teams need to see what was built, how it was tested and who approved it, for every version that ever ran.
03 / Your team
What your team can build next.
From one pipeline to a product line
Deliver a governed dataset to every sponsor, site or customer from one specification. Medidata runs 18,000 that way.
Domain experts in the loop
Clinical data managers and scientists review each change in SQL they can read, so the people who know the data shape it.
Capacity without new hires
An agent drafts each change, DataSQRL verifies it and your engineers approve it. The team you have builds and runs far more.
A standard your quality team can rely on
Every pipeline built, tested and recorded the same way, so each validation package is faster than the last.
04 / Evidence
What your control functions ask for, and what DataSQRL produces.
| They ask | DataSQRL produces |
|---|---|
| Validation of computerised systems | Deterministic, reproducible builds with stored test results: the raw material of a validation package |
| Data integrity: complete, consistent, traceable | Lineage on every compile; event-time simulation including late and deleted records |
| Privacy and residency | PII and residency policies enforced at compile time; deployable inside your own environment |
| Change control | Every change approved as a pull request and kept in Git history with its evidence |
DataSQRL produces evidence for your controls; your teams decide how it maps to your obligations. Security and governance →
05 / In production
Medidata, a Dassault Systèmes company, runs 18,000 customer data pipelines on DataSQRL.
- 90%
- TCO reduction
- 3
- Months from Proof of Value to production
- 0
- Additional hires while workload tripled
06 / First step
Start with one use case. Decide on evidence.
A scoped Proof of Value on a use case you already have, with success criteria agreed before we start.