A leading property and casualty insurer faced significant challenges ensuring data accuracy, consistency and reliability across its enterprise systems.
By implementing a scalable enterprise data quality and governance framework, the organization reduced manual validation effort, improved reconciliation accuracy and established AI-ready, well-governed data. These advancements delivered measurable cost savings, enabled and enhanced user-acceptance-testing (UAT) capabilities, enhanced trust in enterprise data and created a foundation for future analytics and automation success.
About
Accurate, consistent and reliable data enables insurers to make informed decisions, maintain compliance and prepare for AI-driven transformation. During an engagement with a top property and casualty insurer, deep-rooted data quality and governance issues surfaced that impacted efficiency and business confidence. This case study presents First San Francisco Partners’ (FSFP’s) comprehensive approach to building an integrated enterprise framework that unites data governance, metadata management and data quality improvement.
Key Challenges in Data Quality and Governance for Insurance
An assessment of the insurer’s environment revealed that data quality issues such as missing definitions, inconsistencies and inaccuracies were symptoms of broader governance and metadata gaps:
- Lack of Standardization: Inconsistent data definitions and metrics prevented organization-wide measurement of data quality
- Poor Metadata Management: Limited visibility into data lineage and context hindered accurate reporting and traceability
- Fragmented Data Governance: Missing policies and unclear ownership resulted in inconsistent practices and accountability
- Operational Emphasis in Data Quality Activities: Most data quality initiatives centered on transactional and operational checks, leaving gaps in enterprise-level visibility across historical and cross-domain data
A 360° Framework for Data Quality and Governance Improvement
FSFP’s holistic framework was designed to address data quality, governance and metadata maturity in parallel. Each component reinforces the others, delivering immediate and sustainable value.
Lightweight Data Governance Operating Model/Data Decision Framework
A pragmatic governance model accelerated the organization’s transition from reactive data management to proactive stewardship. Defined ownership and stewardship roles enabled accountability while positioning data as a strategic enterprise asset.
Comprehensive Standards Framework
Reusable standards and frameworks were developed to promote consistency and scalability across data domains:
- RACI matrices defining role accountability and decision responsibilities
- Self-documenting rule naming conventions ensuring clarity and traceability
- Data quality dimensions definitions to classify rules to enable enterprise reporting
- Data classifications structuring information assets
- Data class rule library supporting reusable quality checks
- Standardized data quality templates for both enterprise and departmental deployment
Architecture and Engineering Enablement
Engineering teams embedded within agile delivery teams implemented a reusable reconciliation framework that accelerated defect detection, reduced manual reconciliation and improved confidence in production underwriting data.
Advanced Data Profiling
Python-based profiling libraries were deployed in Snowflake notebooks to identify data anomalies and detect quality issues before they affected downstream analytics. This capability provided early insights and data health visibility across systems.
Collibra Data Quality and Metadata Integration
Enterprise-wide Collibra Data Quality (DQ) capabilities were implemented and integrated with Collibra Data Governance (EDC). Aligning DQ metrics with metadata assets provided full traceability from business terms to datasets, increasing reporting transparency and ensuring that all data quality efforts were connected to business context.
Automated Lineage Stitching in Collibra EDC
Automated lineage stitching was introduced to connect data movement across ingestion, transformation and consumption layers. Metadata extracts from Databricks and Snowflake were programmatically integrated into Collibra EDC, eliminating manual lineage mapping. This automation improved lineage accuracy, enhanced audit readiness and provided clear visualization of data dependencies across the insurance ecosystem.
Metadata Tool Health Check
An in-depth metadata tool assessment identified configuration gaps and optimization opportunities. Recommendations improved visibility, user adoption and metadata quality across the enterprise ecosystem.
Benefits of Implementing an Enterprise Data Quality Framework
The integrated approach produced measurable business and operational benefits:
- Improved Data Quality: Accuracy, consistency, and transparency were strengthened across critical systems, supporting smoother migrations and dependable reporting
- Enhanced Metadata Management: Increased visibility into data lineage reduced investigation time during issue triage
- Accelerated Governance Adoption: The lightweight model simplified implementation and promoted alignment with enterprise policies
- AI Readiness: A governed, reliable data foundation enabled the insurer to pursue AI-driven initiatives in claims and policy management with confidence
Cost Savings and Efficiency Gains
The enterprise framework achieved both direct and long-term efficiency improvements:
- Reduced Manual Effort: Automated data quality checks and triage reporting minimized repetitive manual work
- Reusable Templates and Patterns: Standardized patterns accelerated delivery and reduced redundancy across domains
- Optimized Resource Utilization: Improved governance of Databricks and Snowflake workloads reduced compute and storage costs
- Fewer Errors and Less Rework: Strong data standards decreased rework and improved data reliability
- Scalable and Flexible Design: The architecture supports future business growth with minimal reconfiguration
Results and Next Steps
The unified framework delivered sustainable improvements in data accuracy, governance maturity, and operational efficiency. Expansion into additional business domains and the use of semantic intelligence for rule automation represent the next stage of the insurer’s data quality evolution.
Conclusion
A well-structured data governance and quality program can transform fragmented data practices into an enterprise-wide foundation of trust. By aligning people, processes, and technology, this insurer gained immediate business value and long-term readiness for AI and advanced analytics.
The experience demonstrates that with the right governance model, metadata integration, and automation, even complex data ecosystems can become fully transparent and confidently managed.
