The challenge
The organisation relied on a legacy care management platform with deeply interdependent data structures — covering clients, workers, bookings, financial transactions, communications, and compliance records. Data quality varied significantly, with historical inconsistencies, duplicated relationships, and fragmented audit trails.
A traditional migration approach was estimated at five to six months, with high risk of data loss, extended downtime, and limited ability to validate outcomes at scale. The organisation also needed to uplift its reporting capability, moving from static extracts to near real-time operational dashboards.
What we did
We designed and executed a cloud-native migration into a modern data platform, underpinned by a dimensional model aligned to reporting and analytics needs.
AI played a central role in accelerating delivery:
- AI-assisted code generation rapidly produced and refined 80+ migration scripts across complex entities and relationships
- Automated data profiling and anomaly detection surfaced edge cases early, including orphaned records and duplicate relationships
- AI-supported query optimisation improved performance for large-scale data movement and transformation
- Intelligent reconciliation routines compared source and target datasets at scale, significantly reducing manual validation effort
We implemented a phased migration approach with full auditability, including:
- Pre- and post-migration data quality checks
- Automated logging and reconciliation reporting
- Incremental performance tuning across multiple migration cycles
Outcome
The migration was completed in just eight weeks — more than 60% faster than initial estimates.
AI-enabled automation removed thousands of hours of manual scripting, testing, and validation. The resulting platform delivered:
- A clean, governed data foundation ready for analytics and reporting
- Improved data integrity across key operational and financial data domains
- Near real-time visibility for operational teams through modern dashboards
- A repeatable migration framework for future platform evolution
Most importantly, the organisation transitioned with confidence — knowing their data was complete, accurate, and ready to support better outcomes for the communities they serve.