Data Governance and Quality Topic Stream
Guidance on data governance and quality practices for U.S. banking institutions, addressing regulatory expectations, audit readiness, AI data requirements, and operational controls.
Information Briefs
What audit teams must be able to evidence as banks move from pilots to governed intelligence
Baseline Documentation for Regulators. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Practical bank data quality baseline guidance for 2026, including BCBS 239 lineage, rules, ownership, evidence artifacts, and AI-ready reporting controls.
Reporting Consistency Baseline: Closing. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
How leaders define metric rules and reconciliation controls to keep reporting consistent across products, functions, and regulatory views.
Data Capability Baseline for Banks: Lineage. Defines capability gaps, readiness signals, and concrete actions that turn strategy into executable change.
Regulatory, risk, and control scoping as a gating factor for credible transformation governance and measurable progress
Baseline metric language that gives executives an objective starting point, improves investment discipline, and supports progress tracking over time
Transformation Risk Management Frameworks. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Enterprise KPI Standardization as a. Shows how governance design and decision rights accelerate execution while preserving accountability and control.
Fixing Inconsistent Metrics Across the Bank. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Data Quality Program as a Feasibility Test. Provides practical guidance to prioritize investments, manage execution risk, and improve transformation outcomes.
AI Ambition Checks: Data Readiness. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Data Lineage Tooling as a Feasibility Test. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
AI Model Risk Management Capabilities. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Data Governance Operating Model as a. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Why Data Quality Breaks in Banking and What It. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Data Governance Gaps That Undermine Analytics. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Master Data Management Roadmap for Banks. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.
Open Banking Data Access Governance: The. Clarifies control priorities, resilience requirements, and practical risk-reduction actions for banking leaders.