Data Governance

Enterprise Data Governance at CIBC: A Light-Touch Framework for a Multi-Domain Bank

Perspective: Senior Manager of Data Architecture designing a bank-wide data governance framework for CIBC (Toronto, Canada), spanning retail, corporate, wealth, treasury, and capital markets systems.

Executive Summary

Canadian Imperial Bank of Commerce (CIBC) is a diversified financial institution with multiple lines of business (LoBs): retail banking, commercial/corporate lending, capital markets, wealth management, and treasury/finance. Each LoB has its own application architectures—core banking, loan origination, payment rails, trading platforms, risk engines, customer relationship management, data warehouses, and reporting stacks.

The challenge is to set up data governance (DG) that is effective but not oppressive—a “light-touch” framework. Too often, governance programs collapse under their own weight: endless approvals, rigid policies, and disconnected councils. CIBC needs a federated, business-aligned, agile model that enforces trust, compliance, and reusability, but also enables innovation.

1. Why Data Governance Matters in a Multi-Line Bank

Business drivers

  1. Regulatory compliance
    • OSFI guidelines (E-21, B-20, Basel III, IFRS 9/17) demand accurate, auditable data.
    • Privacy laws (PIPEDA, GDPR, CCPA) require demonstrable consent and usage controls.
  2. Risk management
    • Incorrect risk-weighted asset (RWA) calculations or liquidity reports can trigger fines.
    • Fraud detection requires clean, timely, cross-domain data.
  3. Operational efficiency
    • Multiple customer masters, inconsistent account hierarchies, and product codes lead to reconciliation headaches.
    • Duplicate efforts in retail vs wealth vs capital markets.
  4. Business growth
    • Personalization, omni-channel engagement, and cross-sell need unified and trusted customer/product data.
    • Data-driven partnerships (open banking, APIs, fintech) require controlled, high-quality exposure.

2. Governance in a Complex Architecture

CIBC’s ecosystem contains:

  • Retail banking systems: Core deposits, loans, mortgages, payments.
  • Corporate/commercial banking: Syndicated loans, trade finance, treasury services.
  • Capital markets: Trading platforms, risk/pricing engines, market data feeds.
  • Wealth management: Portfolio accounting, advisor platforms, CRM.
  • Enterprise functions: HR, Finance, Risk, Compliance.
  • Data platforms: Data warehouses, data lakes, streaming hubs, MDM solutions, analytics sandboxes.

Each line of business has its own architecture, terminology, and priorities. A “big-bang” centralized governance model will not work. Instead, the goal is federated governance: shared standards and policies, enforced through lightweight processes, but with ownership and stewardship embedded in each LoB.

3. Guiding Principles for Light-Touch Governance

  1. Federated ownership: Data domains stay close to LoBs, but adhere to shared standards.
  2. Policy as enablement, not policing: Governance provides guardrails, not roadblocks.
  3. Automation first: Use tools (catalogs, lineage, quality checks) to reduce manual bureaucracy.
  4. Transparency: Make lineage, quality, and ownership visible to all.
  5. Minimum viable governance: Start with a small set of high-value policies and expand iteratively.
  6. Regulatory alignment by design: Policies mapped to OSFI, PIPEDA, Basel, etc. from the outset.
  7. Business-outcome driven: Always tie DG to tangible outcomes—fewer reconciliations, faster onboarding, better compliance reporting.

4. The Core Framework Components

A practical DG framework for CIBC included the following:

4.1 Data Domains and Ownership

  • Defined domains aligned to business areas: Customer, Product, Account, Transaction, Market Data, Risk, Finance, HR.
  • Assign Data Owners (senior executives accountable for data quality/compliance) and Data Stewards (LoB SMEs handling operational governance).

4.2 Data Policies

We Started small:

  • Data quality: Accuracy, completeness, timeliness, uniqueness.
  • Privacy & security: Classification, encryption, access control, consent.
  • Lifecycle: Retention, archival, deletion.
  • Reference & master data: Common definitions and identifiers.

4.3 Standards and Metadata

  • Canonical definitions for “customer,” “account,” “transaction.”
  • Metadata catalog with lineage and business glossary.
  • Reference data standards (ISO country codes, product hierarchies).

4.4 Processes & Workflows

  • Data issue management (raise, assign, remediate).
  • Change requests for new data elements.
  • Impact analysis via lineage before changes.
  • Exception handling with escalation paths.

4.5 Technology Enablers

  • Data catalog (that was evaluated was IBM MQ, This was pre – Azure purview, Alation, Collibra Days.
  • Lineage tools integrated with ETL and reporting.
  • DQ monitors: automated rules, dashboards.
  • MDM hubs for key domains.
  • Privacy/consent management integrated with access controls.

5. Step-by-Step Implementation Roadmap

Step 1: Established Governance Charter

  • Define vision and scope: “Enable trusted, secure, and reusable data across all LoBs.”
  • Secure executive sponsorship from CIO, CDO, CRO, CFO.
  • Form Data Governance Council with LoB representation.

Step 2: Defined Domains and Owners

  • Identify high-value data domains (customer, account, product, transaction, risk, finance).
  • Appoint Data Owners (VP/MD level) and Data Stewards (operational SMEs).

Step 3: Started with a Pilot Domain

  • Choose a cross-cutting domain with pain points: Customer data.
  • Document sources, definitions, quality issues.
  • Stand up a metadata catalog and quality rules.
  • Demonstrate value with fewer duplicates and faster onboarding.

Step 4: Built the Central Governance Office

  • Lean central team (CDO office): framework, tooling, training, policy enforcement.
  • Provide templates, glossaries, DQ rule libraries.
  • Act as enabler, not bottleneck.

Step 5: Created Federated Operating Model

  • Embed stewards in each LoB.
  • LoBs apply policies locally but report into the council.
  • Council approves cross-domain standards.

Step 6: Rolled Out Policies Gradually

  • Wave 1: Quality, privacy, metadata.
  • Wave 2: Reference data harmonization.
  • Wave 3: Advanced lineage, lifecycle.
  • Wave 4: Integration with AI/analytics governance (bias, model explainability).

Step 7: Automated and Embed in Architecture

  • Integrate catalog with ETL/ELT pipelines.
  • Automate lineage extraction.
  • Deploy real-time DQ checks on feeds.
  • Integrate privacy tagging into data platforms.

Step 8: Monitored & Reported Values

  • Metrics:
    • Duplicate rate reduction.
    • Time to reconcile finance reports.
    • Regulatory report error rates.
    • Data issue resolution time.
  • Publish dashboards for transparency.

6. Key Risks

  1. Overly bureaucratic governance
    • Endless approvals slow delivery.
    • Solution: Light-touch, automate wherever possible.
  2. Unclear ownership
    • Nobody accountable for data quality.
    • Solution: Name Data Owners per domain.
  3. Focus on tools, not process
    • Buying catalog/MDM without defining roles.
    • Solution: Framework first, tools later.
  4. Scope creep
    • Trying to govern every field in every system.
    • Solution: Prioritize high-value domains and attributes.
  5. Cultural resistance
    • Business sees DG as IT’s problem.
    • Solution: Tie DG to business outcomes (faster onboarding, fewer fines).

7. Light-Touch Design Patterns

  • Policy libraries, not approvals: Teams adopt policies without central sign-off for every change.
  • Embedded stewardship: Stewards are business-side, not central IT.
  • Self-service catalog: Users browse metadata without gatekeeping.
  • Automated quality checks: Alerts rather than manual reports.
  • Federated council: Representatives make collective decisions; central team facilitates.

8. Operating Model for CIBC

Central Data Governance Office (CDGO)

  • Owns framework, tools, training.
  • Ensures regulatory mapping.
  • Runs council and dashboards.

Data Governance Council

  • Senior leaders from each LoB.
  • Approves cross-domain standards.
  • Prioritizes issues and investment.

Domain Councils

  • Domain-specific forums (e.g., Customer, Transaction, Risk).
  • Review definitions, approve DQ rules.

Data Stewards

  • Embedded in LoBs.
  • Resolve issues, maintain glossaries, validate merges.

9. Phased Value Delivery(Took 7 Years)

Year 1 (Foundation)

  • Establish charter, owners, catalog, quality rules.
  • Pilot on customer data.

Year 2 (Expansion)

  • Add product, account, and transaction domains.
  • Integrate reference data.
  • Launch data issue management.

Year 3 (Maturity)

  • Expand to risk/finance domains.
  • Automate lineage.
  • Integrate with AI/ML governance.

Year 4 (Optimization)

  • Enterprise-wide adoption.
  • Continuous improvement and regulatory reporting automation.

11. What Success Looks Like

  • Trusted customer view across retail, wealth, and corporate.
  • Accurate regulatory reports with faster turnaround.
  • Reduced reconciliation effort in finance and risk.
  • Empowered analysts with searchable, trusted catalog.
  • Regulators satisfied with demonstrable governance controls.
  • Business growth through cross-sell and digital engagement.

12. Closing Thoughts

CIBC cannot impose a heavy, centralized data governance bureaucracy across retail, corporate, and capital markets—it would be resisted and ignored. Instead, the winning strategy is federated, light-touch governance:

  • Policies as guardrails, not gates.
  • Federated ownership with domain-level accountability.
  • Automation to reduce manual overhead.
  • Iterative expansion focused on high-value domains first.

When done well, data governance becomes invisible but powerful: it enables accurate reporting, faster onboarding, regulatory compliance, and cross-bank innovation. It turns data from a liability into a shared, trusted asset across all lines of business.

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