Clinical Trial Data Engineering Platform for a Multi-Site Research Organisation
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Problem
Fragmented trial data capture
A contract research organisation running trials across 30 sites captured data through a patchwork of electronic data capture exports, lab spreadsheets and instrument files. Statisticians spent days manually cleaning and reconciling datasets before every interim analysis, discrepancies surfaced late in the cycle, and nothing in the pipeline produced data clean enough for the modelling work sponsors were starting to request.
Solution
Automated AI-ready data pipelines
We built a central data engineering platform for the organisation: automated ingestion connectors pulling nightly from EDC and lab systems, standardisation into CDISC-aligned schemas, rule-based validation that flags discrepancies at load time, and versioned curated datasets. The same curated layer now feeds both statistical review and ML-ready feature tables for enrolment forecasting and site-performance models.
Measurable Impact
What changed after launch
Data from 30 trial sites ingested nightly through 12 automated connectors, replacing manual exports
Dataset preparation time ahead of interim analyses cut from 10 days to under 48 hours
95% of data-entry discrepancies now caught by automated validation before statistical review
3 ML-ready curated datasets feeding enrolment forecasting and site-performance models
Tech & Tools Used
What powered the build
Ready to Build your Clinical Research Business with Data Engineering for AI
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