The Challenge
A regional health network had grown through acquisition, inheriting multiple data systems along the way. Their analytics team was spending more time fighting infrastructure than generating insights. Reports that took hours were blocking clinical decision-making. New analytics use cases were impossible because the underlying data wasn’t accessible.
They’d attempted modernization twice before - once with a big-bang data warehouse replacement (failed), once with a DIY data lake (became a data swamp). They were understandably cautious about trying again.
Our Approach
Understanding Before Proposing
We spent the first six weeks understanding their current state in detail. Not just the technical architecture, but the people and processes around it:
- Which reports were critical for clinical operations?
- What was the actual query pattern, not the theoretical one?
- Who owned each data source, and what were their concerns?
- What had gone wrong in previous modernization attempts?
This discovery phase shaped everything that followed. Their previous failures weren’t technical - they were organizational. Solutions that ignored the human factors would fail again.
Incremental Strategy
We designed a migration that could be paused or reversed at any point:
Phase 1: Stand up the new platform alongside the old. Run them in parallel. Build confidence.
Phase 2: Migrate analytics workloads one domain at a time. Validate each migration before proceeding.
Phase 3: Gradually decommission legacy components as their consumers move to the new platform.
No big bang. No data migrations that had to work perfectly the first time.
HIPAA Throughout
Healthcare data modernization has an extra constraint: you can’t compromise compliance in pursuit of capability. We maintained a clean audit trail of all data movement, implemented role-based access controls from day one, and ensured all PHI handling met HIPAA requirements at every stage.
Technical Architecture
Data Lake Foundation: Delta Lake on AWS S3, providing ACID transactions and schema evolution on top of object storage. This gave us the flexibility of a data lake with the reliability of a database.
Transformation Layer: dbt for analytics engineering, with rigorous testing and documentation. Each transformation is version-controlled and reproducible.
Query Engine: Databricks for interactive analytics, with proper resource isolation between workloads. Query performance improved 10x for most reports.
Integration Pattern: Change data capture from source systems, maintaining near-real-time freshness without impacting operational databases.
Data Catalog: Comprehensive documentation of all datasets, including data lineage and quality metrics. Analysts can find and trust the data they need.
Managing the Migration
The biggest risk in any migration is data accuracy. We implemented extensive validation:
- Row count reconciliation between source and target
- Statistical distribution comparison for numeric fields
- Business rule validation against known-good results
- Parallel running of critical reports with difference analysis
When we found discrepancies - and we did - we traced them to their source, fixed the issue, and documented the root cause. By the end, stakeholders trusted the new platform because they’d seen the validation evidence.
The Outcome
The modernization took 18 months and achieved all its goals. Reports that took hours now take minutes. Analytics use cases that were impossible - like real-time operational dashboards and ML-based predictions - are now routine.
More importantly, the platform is maintainable. Their internal team owns it and has continued to extend it since our engagement ended. We weren’t building something they’d need us to run forever.
Lessons Learned
Organizational change is as important as technical change. The data team needed new skills and workflows, not just new technology. We invested significant time in training and process development.
Parallel running reduces risk but increases short-term cost. The organization had to fund both systems for longer than expected. This was worth it for the reduced risk, but should be budgeted upfront.
Data quality issues surface during migration. We found and fixed data quality problems that had existed for years but were invisible until we looked closely. This was valuable but added scope.