Insurance Case Study
Overcoming Data Challenges in Insurance
How a Fortune 500 P&C insurer rebuilt trust in analytics by integrating 15+ fragmented systems, standardizing quality, and achieving consistent KPIs across the enterprise.
Discuss Your ChallengeProblem Statement
Fragmented Data Landscape
Insurance organizations rely on dozens of operational systems—each with its own history and taxonomy. The result is a fragmented data landscape that undermines analytics and slows decision-making.
Data Silos Everywhere
Legacy policy, claims, and billing systems created conflicting answers and stalled delivery timelines.
Inconsistent Formats & Quality
Different schemas, missing history, and manual extracts eroded trust in KPI reporting.
Complex Integrations
Changing regulations and vendor upgrades made point-to-point data flows brittle and expensive.
Client Situation
Our client—one of the nation's leading Fortune 500 insurers—managed multiple policy and claims platforms with legacy integrations that never fully reconciled. Analysts sourced answers manually, executives received conflicting reports, and data trust eroded across the enterprise.
Business Impact
Critical data trust issues threatened decision-making capabilities and regulatory compliance across the organization.
Our Task
Restoring Data Trust
Seisiun Analytics was engaged to build an integrated enterprise data warehouse that would reconcile legacy systems, ensure quality, and deliver a compliant audit trail—without slowing business operations.
Why Datavault 2.0
Scalability
Purpose-built to handle enterprise-scale insurance data volumes without sacrificing performance.
Flexibility
Accommodates new source systems quickly—no disruptive rewrites when business lines evolve.
Auditability
Provides end-to-end lineage and a compliant audit trail to satisfy regulatory scrutiny.
We paired Datavault 2.0 with a Medallion architecture so the insurer could preserve raw fidelity, standardize their core entities, and present curated marts ready for analytics and AI.
Action
The Medallion Architecture
Each layer plays a role in restoring data trust—from raw ingestion to curated analytics.
Bronze Layer
Ingest raw policy, claims, billing, and external datasets in their native structures to preserve fidelity.
Silver Layer
Standardize, cleanse, and harmonize core entities so analytics teams can trust their joins.
Gold Layer
Publish enriched, ready-to-consume marts for reporting, decision support, and AI readiness.
Platinum Layer
Advanced AI models that dynamically adjust recommendations and communications based on real-time customer interactions and predictive analytics.
Bronze Layer
Ingest raw policy, claims, billing, and external datasets in their native structures to preserve fidelity.
Silver Layer
Standardize, cleanse, and harmonize core entities so analytics teams can trust their joins.
Gold Layer
Publish enriched, ready-to-consume marts for reporting, decision support, and AI readiness.
Platinum Layer
Advanced AI models that dynamically adjust recommendations and communications based on real-time customer interactions and predictive analytics.
Key Client Organization Statistics
The scale of the insurer’s footprint demanded a solution built for growth, resilience, and governance.
Policy Holders
Vehicles Insured
Largest Claims Table
Organization Database Size
Source Systems Processed
External Data Sources
Source System Tables
Execution
Delivery Timeline
Experience-led execution guided the insurer from data chaos to trusted analytics in twelve months.
Month 0-3
- Staged over 300 source system tables across policy, claims, billing, and customer servicing.
- Adopted core/spoke delivery model with 12 spoke teams driving functional-area builds.
- Produced 12 concept models and one enterprise core model to standardize shared entities.
Month 3-6
- Constructed raw datavault with 12 functional schemas and orchestrated nightly loads.
- Loaded 30 reference tables to support business rule lookups and regulatory reporting.
- Developed 20 business rules and 3 star schemas (Claims, Policy, Customer Service).
Month 6-12
- Expanded spoke deliverables into governed gold marts and self-serve analytics products.
- Codified enablement playbooks and embedded coaches to mature data stewardship.
- Stabilized SLA-driven operations with proactive monitoring and automated lineage checks.
Result
Business Impact
By addressing silos, standardizing quality, and enabling governed adoption, Seisiun restored trust in the insurer's analytics foundation.
Improved Data Integration
Unified datavault architecture now reconciles policy, claims, and external data in one trusted hub.
Efficient Processing
Automated pipelines reduce manual effort and shorten data availability windows for analysts.
Enhanced Quality & Trust
Standardized cleansing and lineage restore confidence in KPIs and cross-functional reporting.
Better Decisions
Leadership acts quickly with consistent insights, supporting underwriting, claims, and customer care.
Conclusion
Combining Datavault 2.0 with the Medallion architecture gave the insurer a resilient foundation for trusted analytics. Silos were dismantled, data quality was restored, and teams now deliver insights with speed and confidence.
With experience as the guiding principle, Seisiun helped the organization move from data complexity to business clarity—minus the labor.
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