Data Governance at Scale: Building Compliance Infrastructure for Insurance

Executive Summary

A top-10 global insurance group faced critical data governance challenges:

Fragmented data governance across 12 countries with inconsistent standards
500+ unmanaged data sources with no lineage or ownership
CPG 235 compliance gaps putting them at regulatory risk
Data quality issues impacting actuarial models and pricing accuracy

We implemented a global, scalable data governance operating model that delivered:

100% CPG 235 compliance in 18 months
95% data source registration and documentation
$8M annual savings from improved data quality and reduced manual controls
60% faster regulatory reporting and audit cycles

The Challenge

The insurance group operated as a federation of regional businesses with minimal central governance:

Governance Landscape

12 regional data governance offices with different standards
No enterprise data dictionary or metadata repository
Manual data quality processes requiring 50+ FTE annually
300+ active regulatory inquiries annually related to data management
Inconsistent approach to data access controls and privacy

Regulatory Risk

CPG 235 (APRA guidance on prudential data management) requiring comprehensive data governance
Growing GDPR complexity across European operations
Increasing regulatory scrutiny of actuarial data quality
Audit findings accumulating year-over-year

Business Impact

Pricing models challenged for accuracy by regulators
Merger & acquisition activities delayed due to data quality concerns
Digital initiatives hampered by inability to reliably access clean data
3+ weeks of manual effort for monthly regulatory reporting

Our Approach

Phase 1: Discovery & Design (Months 1-3)

Comprehensive assessment of current state and design of target governance model:

Current State Assessment:

Conducted interviews with 50+ stakeholders across regions
Catalogued 500+ data sources and their characteristics
Mapped data flows and identified quality hotspots
Assessed regulatory compliance gaps against CPG 235

Target State Design:

ISO 42001 and DAMA-DMBOK aligned governance operating model
Centralized metadata repository with distributed governance
Data governance council structure spanning regions
Clear data ownership, stewardship, and accountability model

Key Finding: 70% of regulatory reporting data had no documented lineage or validation rules.

Phase 2: Governance Operating Model (Months 4-6)

Established the organizational and process foundation:

Governance Structure:

Enterprise Data Governance Council with executive representation
12 regional data governance committees
Functional data stewards for core domains (actuarial, claims, customer)
Data quality working groups by business function

Policies & Standards:

Master data governance policy covering all regions
Data classification and handling standards aligned with privacy regulations
Data quality standards and acceptable thresholds by domain
Data access and security standards across jurisdictions

Tools & Infrastructure:

Implemented Alation for metadata management and data lineage
Built data quality monitoring with Great Expectations
Created data governance dashboard for council visibility
Established data governance request and approval workflow in ServiceNow

Phase 3: Data Cataloguing & Lineage (Months 7-12)

Comprehensive documentation of enterprise data landscape:

Data Source Inventory:

500+ data sources mapped and categorized
Data lineage established from source through to reporting
Data dictionary created for all critical data domains
Metadata enriched with ownership, quality metrics, and usage

Data Quality Baseline:

Quality assessment against standards for each data source
Quality issues prioritized by business impact and regulatory risk
Quality improvement plans developed for high-risk areas
Automated quality monitoring implemented

Regulatory Alignment:

Mapped all CPG 235 requirements to specific data governance controls
Created compliance evidence repository
Established audit trail for all data governance decisions
Prepared comprehensive governance documentation for regulatory submission

Phase 4: Implementation & Automation (Months 13-18)

Operationalized governance at scale:

Process Automation:

Automated data quality checks at source systems
Real-time alerts for data quality anomalies
Automated lineage tracking using data tagging
Governance workflow automation for approvals

Capability Building:

Data stewardship training for 100+ staff across regions
Data quality management workshops for 50+ technical staff
Governance operating model training for executives (25+ leaders)
Regional data governance coordinators trained and empowered

Continuous Improvement:

Monthly governance council meetings tracking metrics
Quarterly governance effectiveness assessments
Data governance community of practice established
Regular updates to standards based on lessons learned

Technology Stack

Metadata Management: Alation (Data Lake & Lineage)
Data Quality: Great Expectations, Soda
Data Governance Tools: Collibra, ServiceNow
Monitoring: Datadog, custom dashboards
Data Warehouse: Snowflake
BI Platform: Tableau with governed data access

Results & Outcomes

Governance Outcomes

Metric	Before	After	Improvement
Data Sources Catalogued	60%	95%	+35pp
Data Lineage Coverage	10%	85%	+75pp
CPG 235 Compliance	45%	100%	+55pp
Data Governance Maturity	Level 1	Level 3	2 levels

Operational Outcomes

Metric	Before	After	Improvement
Regulatory Reporting Time	3+ weeks	3 days	90% reduction
Audit Preparation Time	4-6 weeks	5 days	85% reduction
Data Quality Issues (critical)	45/month	3/month	93% reduction
Manual Data Verification	50 FTE	15 FTE	70% reduction

Financial Outcomes

Cost Savings: $8M annually from reduced manual controls and improved efficiency
Risk Reduction: $2M+ avoided regulatory penalties through proactive CPG 235 compliance
Revenue Impact: $12M from improved pricing accuracy enabled by trusted data

Compliance Outcomes

100% CPG 235 compliance verified in external audit
Zero audit findings on data management in last regulatory examination
Zero data privacy incidents in GDPR-regulated regions
Regulatory confidence: Reduced inquiry volume by 60%

Key Success Factors

Executive Commitment: CFO and CRO co-sponsored governance transformation
Phased Rollout: Regional pilots before global rollout reduced implementation risk
Governance First: Process and standards established before tool implementation
Capability Investment: 200+ staff trained on governance and tools
Regulatory Proactivity: Continuous communication with APRA and other regulators

Implementation Timeline

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3
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Month 1-3: Discovery & Design
Month 4-6: Operating Model & Policies
Month 7-9: Data Cataloguing (Phase 1)
Month 10-12: Data Cataloguing (Phase 2) + Automation
Month 13-15: Regional Rollout & Capability
Month 16-18: Stabilization & Continuous Improvement

Lessons Learned

What Worked Well

Starting with operating model and policies before tools prevented technology-led failures
Regional pilot with strong executive sponsor accelerated adoption
Clear ownership and accountability made governance “sticky”
Automated quality checks achieved scale without proportional cost

Challenges & Solutions

Challenge: Resistance from regional offices wanting to maintain autonomy
- Solution: Balanced central governance with local execution; regional data stewards retained authority over local implementation
Challenge: Legacy systems without modern APIs for metadata extraction
- Solution: Built custom adapters and manual processes for legacy systems, automated where possible
Challenge: Competing definitions of key business terms
- Solution: Business glossary workshops mediated by neutral facilitator; version control for definitions

Industry Benchmarking

The insurance group’s governance maturity now exceeds industry norms:

Data Source Coverage: 95% vs 60% industry average
CPG 235 Compliance: 100% vs 70% industry average
Regulatory Audit Findings: 0 vs 3-4 per exam industry average
Data-Driven Decision Making: 65% of decisions vs 40% industry average

Recommendations for Financial Services

Start with governance, not technology. Clear policies and standards are more important than tool selection.
Balance centralization with distribution. Enterprise standards with regional flexibility ensures adoption and relevance.
Automate quality monitoring. Manual data quality is unscalable; invest in automated checks and alerts.
Measure governance value. Connect data governance improvements to business outcomes (cost, risk, revenue).
Build sustainable capability. Train and empower internal teams rather than creating consulting dependency.

Conclusion

This transformation demonstrates that global data governance is achievable—even with legacy systems, multiple jurisdictions, and complex regulatory requirements. By establishing clear governance foundations, investing in capability, and taking a phased approach, the insurance group moved from reactive compliance to proactive data stewardship.

The result: a trusted, governed data ecosystem that powers better decisions, reduces risk, and exceeds regulatory expectations.

Client: Top-10 Global Insurance Group
Duration: 18 months
Geographic Scope: 12 countries, 500+ data sources
Team Size: 8 Etherion staff + 30+ client governance staff
Key Frameworks: ISO 42001, DAMA-DMBOK, CPG 235
Outcome: 100% compliance + $8M annual savings