Sanitas Insurance Transforms Healthcare Data Analytics with Google Cloud BigQuery and AI
Sanitas, one of Switzerland's largest health insurers, modernized its 19-year-old legacy Oracle data warehouse, which was causing critical delays for healthcare analytics with 24-hour batch processing cycles. They collaborated with Google Cloud and partner ipt to migrate to a modern lakehouse architecture based on BigQuery with real-time ingestion, automated data pipelines, CI/CD integration, and layered data governance compliant with healthcare regulations. The new architecture leveraged Cloud Datastream, Dataplex, Cloud Composer, and innovative BigQuery features like table cloning for parallel development. Sanitas deployed eight production AI services, including a Sales Assistant built with Vertex AI providing real-time personalized product recommendations integrated within CRM workflows. The solution reduced data warehouse costs by 25%, eliminated downtime during migration, enabled real-time analytics (previously daily batch), and improved agility and decision-making across multiple business domains.
- Organization
- Sanitas Group
- Industry
- Healthcare
- Location
- Switzerland
Reported outcomes
−25%
costCost savings
Strategic outcomes
Catalog median for cost savings deployments: −45% across 346 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 3
- 1Data Modernization
- 2Real-time Analytics
- 3Generative AI for Recommendations
- Legacy data warehouse caused delays in healthcare analytics with 24-hour batch processing cycles.
- High data volume and scaling issues hindered real-time insights for claims, fraud detection, and marketing.
- Seasonal insurance business forced over-provisioning of infrastructure, resulting in high costs and unused capacity.
- Batch data failures caused crisis mornings with high management stress and slow response times.
- Migrated to serverless BigQuery-based lakehouse architecture with real-time data ingestion via Cloud Datastream and Kafka.
- Implemented complex data pipelines orchestrated by Cloud Composer and used dbt for software engineering best practices in data warehouse development.
- Dataplex used for intelligent data governance with metadata-driven compliance and audit tracking.
- Incorporated AI services with Vertex AI for predictive modeling and AI Sales Assistant for real-time CRM insights and personalized recommendations.
- Reduced data warehouse costs by 25% by moving to serverless, auto-scaling architecture that aligns with seasonal demand.
- Achieved zero downtime during production go-live migration of 3,138 Oracle tables.
- Enabled real-time data analytics replacing daily batch processing, improving decision-making speed.
- Deployed eight production AI services improving claims processing, marketing effectiveness, and customer service.
- Enabled development teams to work in parallel with BigQuery table cloning feature, accelerating innovation cycles.
Architecture
Sanitas and ipt implemented a medallion architecture with four layers for healthcare data governance. Real-time data flows use Cloud Datastream and Kafka. Cloud Composer orchestrates pipelines. dbt is integrated for CI/CD and version control. Dataplex ensures data governance and regulatory compliance. BigQuery table cloning enables isolated sandbox development. AI services leverage Vertex AI and Gemini models for real-time insights and personalized recommendations within CRM.
Sources & evidence1
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