The Client
A leading global insurance organization operating across life, health, and specialty lines in multiple international markets, this insurer manages a complex enterprise data landscape spanning operational systems including SAP, ServiceNow, Oracle, PostgreSQL, and HR platforms. As the business pursued digital acceleration and advanced analytics ambitions, the limitations of its legacy Azure Synapse environment in scalability, governance maturity, and AI/ML readiness became a strategic constraint requiring urgent resolution.
The Challenge
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Fragmented Enterprise Data:
Data was distributed across SAP, ServiceNow, Oracle, PostgreSQL, and HR systems with no unified visibility, limiting the insurer’s ability to generate enterprise-wide insights.
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Weak PII Governance and Compliance Risk:
Insufficient PII controls, lineage tracking, and audit readiness created regulatory exposure and constrained self-service analytics consumption by business teams.
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Slow Reporting Turnaround:
The absence of curated, business-ready datasets meant long turnaround times for reporting and insights, reducing organizational agility.
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Legacy Platform Constraints:
Azure Synapse limitations in scalability, analytics performance, and AI/ML readiness prevented the insurer from advancing its data and analytics strategy.
The Impact
- 65% Faster time-to-insight
- 100% PII coverage with automated governance and lineage
- 3X Faster query execution with 60% faster batch processing
The Solution
Hoonartek modernized the insurer’s enterprise data platform by migrating from Azure Synapse to a Databricks Lakehouse using Medallion architecture (Bronze–Silver–Gold). Governance was centralized using Unity Catalog, with RBAC and ABAC controls, PII masking, lineage tracking, and comprehensive access controls — delivering 100% PII coverage across the enterprise. Gold-layer data marts were built to enable faster analytics, self-service reporting, and AI model consumption by business teams.
Automated ingestion, orchestration, monitoring, and governed pipelines were implemented across all enterprise source systems, eliminating manual data preparation bottlenecks. FinOps and cost optimization controls were added to improve platform efficiency and operational visibility, ensuring the cloud investment delivered measurable returns. The result is a scalable, AI-ready data foundation that supports current analytical requirements and is built to accommodate future ML and advanced analytics use cases.
Key Benefits
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Faster Insights:
65% improvement in time-to-insight through curated Gold-layer data marts accessible to business teams without IT dependency.
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Complete PII Protection:
100% PII coverage with automated governance, masking, and lineage ensures full regulatory compliance across all data states.
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Performance Gains:
3x faster query execution and 60% faster batch processing delivered immediate productivity benefits for analytics and reporting teams.
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Business Self-Service:
Reduced dependency on IT for reporting and analytics by making trusted, governed data accessible directly to business consumers.
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AI-Ready Foundation:
Scalable Databricks Lakehouse architecture provides the data infrastructure required to support future ML models and advanced analytics use cases.