Assure data quality across regulated enterprises

Regulatory foundations

GDPR Article 5 accuracy principle. Controllers must maintain procedures to rectify or erase inaccurate personal data without delay. This includes verifying inputs at collection, orchestrating master data management (MDM) workflows, and providing self-service portals for data subjects to request corrections.^{Regulation (EU) 2016/679} Supervisory authorities have issued fines where organisations failed to update records promptly or relied on stale datasets for automated decision-making.

CSRD Articles 19a and 29a. Companies must disclose sustainability information under European Sustainability Reporting Standards (ESRS) and set up internal control and risk management systems over reporting processes. Member State transposition laws and ESMA enforcement priorities emphasise data quality, traceability, and external assurance readiness.^{Directive (EU) 2022/2464}

Information Quality Act (Section 515) and OMB guidelines. Agencies must publish information quality guidelines, establish administrative mechanisms allowing affected persons to seek correction, and report annually on complaints and resolutions.^{OMB M-02-24} These obligations extend to scientific, statistical, and financial data disseminated to the public.

OMB Circular A-123. Agency management must design, implement, and operate an effective system of internal control over operations, reporting, and compliance. Appendix A requires assessments of data reliability supporting performance and financial reporting, linking directly to DQA maturity.^{OMB Circular A-123}

BCBS 239 Principles 3, 4, and 5. Large banks must ensure accuracy, integrity, and completeness of risk data, supported by reconciliation processes, data architecture documentation, and automated controls.^{BCBS 239} Supervisors have issued remediation mandates where manual spreadsheets or fragmented systems undermined aggregated capital or liquidity reporting.

ESMA EMIR data quality guidelines. Trade repositories and reporting counterparties must implement quality assurance programs addressing validation, reconciliation, and error correction, with escalation obligations to national competent authorities.^{ESMA Guidelines on EMIR data quality}

Sector regulations. U.S. Sarbanes-Oxley Act Section 404 requires management assessment and auditor attestation on internal controls over financial reporting.^{Public Law 107-204} The EU Emissions Trading System monitoring and reporting regulation (EU) 2018/2066 imposes data quality control plans for greenhouse-gas inventories. Health-sector regulators (for example, the U.S. Food and Drug Administration’s Quality System Regulation) require master data integrity for product traceability.

Standards and frameworks

ISO 8000-61:2016. Defines a process reference model for data quality management, including governance, planning, control, assurance, and improvement processes.^{ISO 8000-61:2016} Use it to structure policies, assign responsibilities, and integrate quality planning into data lifecycle management.

ISO/IEC 25012:2008. Provides a data quality model covering inherent characteristics (accuracy, completeness, consistency) and system-dependent characteristics (accessibility, confidentiality, recoverability).^{ISO/IEC 25012:2008} Align metrics and monitoring dashboards with these characteristics to provide traceable evidence to auditors.

DAMA-DMBOK and EDM Council DCAM/CDMC. Industry frameworks detail data ownership models, quality rules, and stewardship processes. Map their artifacts to ISO 8000 processes and regulatory controls to avoid duplication.

ISAE 3000 (Revised). Sets assurance requirements for non-financial reporting, including evaluating the suitability of criteria, testing evidence, and forming conclusions.^{ISAE 3000 (Revised)} Preparing for limited and reasonable assurance requires rigorous documentation of data lineage, transformation logic, and control testing results.

NIST Framework for Improving Critical Infrastructure Cybersecurity. The Identify and Protect functions include data management expectations that reinforce quality controls, particularly around asset inventories and data classification. Align DQA metrics with cyber resilience metrics to provide a unified risk view.

Governance and organisational design

Establish a Data Quality Council chaired by the Chief Data Officer with representation from compliance, finance, risk, sustainability, and IT. The council should approve data standards, remediation priorities, and assurance schedules. Embed domain data owners who report quality metrics and coordinate remediation with stewards and engineering teams.

Define RACI matrices for data creation, transformation, storage, and consumption. Ensure product teams understand their obligation to implement validation and reconciliation controls before publishing data to enterprise platforms. Align governance charters with ISO 8000-61 process groups and OMB Circular A-123 control environment requirements.

Integrate DQA governance with privacy, security, and model risk committees. For example, escalate AI training data quality issues to AI governance forums to satisfy EU AI Act Article 10 requirements for data governance, representativeness, and bias mitigation.

Data domain prioritisation

Segment data domains by regulatory impact, financial materiality, and operational criticality. Typical high-priority domains include customer master, supplier master, product master, financial ledger, emissions inventory, clinical trial data, and safety incident records. Map each domain to applicable regulations (GDPR, CSRD, SEC climate proposals, EMIR Refit) and standards (ISO/IEC 25012 dimensions).

Develop quality scorecards for each domain with metrics covering completeness, accuracy, timeliness, uniqueness, consistency, and validity. Use thresholds aligned with regulatory expectations; for example, BCBS 239 expects automated reconciliation between risk and accounting systems with documented tolerance levels.

Implement critical data element (CDE) inventories with metadata describing owner, steward, system of record, validation rules, and downstream consumers. Link CDEs to control libraries and assurance plans to ensure coverage is verifiable.

Data lineage and documentation

Lineage documentation underpins auditability and regulatory confidence. Capture end-to-end flows for each CDE, including source systems, transformation logic, enrichment rules, calculation engines, and reporting outputs. Use automated lineage extraction from ETL/ELT tools, orchestration platforms, and analytics workspaces to maintain up-to-date maps.

Annotate lineage with control checkpoints: validation rules, reconciliations, manual adjustments, and approval steps. Tie each checkpoint to control IDs in the DQA library so auditors can trace evidence quickly. Where manual processes remain, record responsible roles, timestamps, and supporting documentation.

Provide user-friendly lineage visualisations for business owners and regulators. Offer filtered views highlighting regulatory submissions (for example, CSRD disclosures, EMIR reports, ESG scorecards) and underlying data sources. Integrate lineage with metadata catalogues and issue logs so stakeholders can assess the impact of quality incidents, system changes, or policy updates. Ensure documentation adheres to retention policies and is version-controlled to evidence historical states during regulatory reviews.

Tooling architecture

Deploy data quality platforms that support rule management, profiling, anomaly detection, and remediation workflows. Integrate with metadata catalogues to reuse business glossaries and lineage. Use API-first designs so validation services can be embedded in data ingestion pipelines, streaming platforms, and application integrations.

Adopt master data management (MDM) tools to enforce golden records and survivorship rules. Ensure MDM workflows capture audit trails of manual overrides, match-merge outcomes, and steward approvals, satisfying GDPR accountability and CSRD assurance evidence needs.

Introduce data contracts between producers and consumers that define schema, semantics, freshness, and quality expectations. Automate contract testing in CI/CD pipelines so schema changes or degradation in completeness trigger build failures and stakeholder notifications. Document contract breaches and resolutions for inclusion in regulator-facing evidence packs.

Implement observability tooling for data pipelines, capturing freshness, volume, schema drift, and distribution changes. Align alerts with operational run-books so incidents trigger triage, business impact assessment, and regulator communication when required.

Control library

Develop a control framework aligned to ISO 8000-61 processes:

• Planning controls. Annual data quality risk assessments, documented objectives, and control design reviews.
• Control controls. Automated validation rules, referential integrity checks, duplication detection, range checks, and manual review workflows.
• Assurance controls. Independent testing by quality assurance teams, sample-based data audits, and control effectiveness certifications.
• Improvement controls. Root-cause analysis, corrective action plans, and change management reviews.

Map each control to regulatory requirements. For example, link customer data validation to GDPR accuracy, sustainability data reconciliation to CSRD ESRS E1 metrics, and risk data reconciliations to BCBS 239 Principle 4. Record control owners, frequency, evidence requirements, and tooling dependencies.

Assurance programme

Design a three-lines-of-defence assurance model:

1. First line. Data producers and stewards execute controls, document remediation, and maintain operational dashboards.
2. Second line. Risk and compliance teams review control design, monitor key risk indicators (KRIs), and coordinate regulator responses.
3. Third line. Internal audit performs independent testing, aligning audit plans with regulatory focus areas and ISAE 3000 readiness assessments.

Schedule quarterly quality reviews for high-risk domains, semi-annual management attestations, and annual independent assurance. Coordinate with external auditors to provide evidence packages for CSRD limited assurance or SOX 404 attestations. Maintain issue logs with root cause, remediation owner, due date, and status, enabling transparent tracking.

Metrics and KPIs

Define metrics aligned with ISO/IEC 25012 characteristics and regulatory expectations:

• Accuracy rate. Percentage of sampled records passing validation rules; target thresholds based on regulatory tolerance (for example, 99.5% for financial reporting).
• Completeness. Share of mandatory fields populated for CDEs. Track at domain and source-system levels.
• Timeliness. Average delay between data capture and availability for reporting; monitor against statutory deadlines (for example, EMIR T+1 trade reporting).
• Consistency. Alignment between datasets (for example, emissions totals vs. sub-ledger detail).
• Issue resolution cycle time. Days to close data quality incidents.
• Control effectiveness. Pass rate of control testing, audit findings, and remediation completion.

Visualise metrics in executive dashboards linked to enterprise performance management systems. Provide drill-down capability for regulators and auditors to review evidence.

Integration with adjacent programmes

Coordinate DQA with privacy, security, AI governance, and sustainability initiatives. For example, align GDPR rectification workflows with incident response playbooks, ensuring inaccurate data discovered during breach investigations is corrected promptly. Integrate AI training data quality checks with EU AI Act risk management procedures highlighted in Zeph Tech’s March 2024 briefing.

Align sustainability data pipelines with EU ETS monitoring plans and U.S. EPA reporting requirements, sharing control evidence with environmental health and safety teams. Use shared metadata catalogs to ensure consistent definitions across ESG, finance, and operational reporting.

Collaborate with model risk management teams to ensure data quality metrics feed into model validation reports, satisfying regulatory expectations for explainability and bias mitigation.

18-month roadmap

1. Months 0–6: Establish foundations. Perform maturity assessment, inventory CDEs, define governance charters, implement baseline validation rules, and launch remediation sprints for high-risk data sets. Update policies to reference GDPR accuracy, CSRD, and BCBS 239 requirements.
2. Months 6–12: Industrialise controls. Deploy data quality tooling, integrate with metadata catalogs, roll out automated monitoring, and implement stewardship scorecards. Formalise issue management workflows and align with internal audit schedules.
3. Months 12–18: Assure and optimise. Conduct independent assurance engagements, pursue ISO 8000 certification readiness, expand coverage to secondary datasets (for example, supplier ESG data), and integrate predictive analytics to anticipate quality degradation.

Review roadmap quarterly to incorporate regulatory updates such as ESRS revisions, SEC climate reporting rules, or updated OMB guidance.

Maturity model

Benchmark progress using five maturity levels:

• Initial. Quality issues addressed reactively; limited governance.
• Managed. Basic governance in place; manual validation; fragmented tooling.
• Defined. Enterprise standards, stewardship roles, and automated controls across key domains.
• Quantitatively managed. Metrics embedded in executive dashboards; predictive monitoring; integration with risk management.
• Optimised. Continuous improvement, certification readiness, external assurance integration, and documented contributions to industry standards.

Assess maturity by domain and regulatory requirement. Prioritise investments to elevate lagging areas before assurance deadlines.

Case studies

Banking. Institutions implementing BCBS 239 have built federated data architectures, automated reconciliation, and data lineage tooling. Supervisors such as the European Central Bank have issued remediation programmes requiring integrated data governance offices, quality rules, and board reporting.

Energy and emissions. Shipping companies preparing for EU ETS phase-in have created emissions data hubs with sensor validation, manual override approval workflows, and audit trails for verifiers. Zeph Tech’s September 2025 briefing highlights the importance of ISO 14064-1 alignment and integrated quality controls.

Public sector. U.S. federal agencies maintain Information Quality Act portals allowing public correction requests, track complaints, and publish responses in annual reports. Agencies adopting data governance boards have reduced complaint resolution times and improved transparency.

Healthcare. Clinical research sponsors follow Good Clinical Practice (ICH E6) and FDA 21 CFR Part 11 to ensure data integrity. DQA programmes integrate electronic data capture (EDC) validation, audit trails, and centralised monitoring to support regulatory submissions.

Risk management

Catalog data quality risks such as inaccurate customer records, incomplete emissions data, inconsistent trade reporting, and delayed AI training data refresh. Map risks to likelihood, impact, and existing controls. Align with enterprise risk frameworks (COSO ERM or ISO 31000) and ensure risk appetite statements reference data quality tolerance.

Establish KRIs such as number of regulatory findings, audit issues related to data quality, or breach of service-level agreements. Escalate breaches to governance forums and board committees. Document remediation and lessons learned.

Incorporate scenario planning to test resilience. For example, simulate failure of a key source system feeding sustainability reporting and evaluate contingency controls, including manual backup processes and data recovery times.

Change management and culture

Develop training programmes covering regulatory obligations, quality standards, tooling usage, and reporting expectations. Provide specialised training for stewards, data engineers, analysts, and assurance teams. Incorporate DQA objectives into performance management and incentive plans.

Run communication campaigns emphasising the cost of poor data quality—regulatory penalties, audit findings, reputational damage—and highlighting success stories. Offer helpdesk support and office hours to accelerate adoption of new tooling and processes.

Celebrate improvements through dashboards, awards, or leadership recognition to reinforce the culture of data stewardship.

Future outlook

Track regulatory developments: the European Commission is preparing assurance standards for sustainability data (ISAs for sustainability reporting) and delegated acts specifying ESRS digital taxonomy requirements. The U.S. SEC climate disclosure rule, once final, will require granular emissions data supported by internal control evaluations. AI regulations worldwide (EU AI Act, Colorado SB24-205) will mandate dataset documentation, bias assessments, and human oversight.

Monitor standard updates: ISO 8000 working groups are refining guidance on reference data quality and supplier data exchanges; ISO/IEC JTC 1/SC 42 is advancing AI data quality standards. ISAE 3000 limited assurance requirements may transition to reasonable assurance in future CSRD phases, increasing evidence expectations.

Adopt emerging technologies cautiously: leverage machine learning for anomaly detection, synthetic data for testing (with clear segregation from production), and distributed ledger technology for immutable audit trails where appropriate. Validate tools against regulatory expectations before deployment, and document governance decisions to evidence responsible adoption.

Appendix: artefact templates

• Data quality policy. References legal obligations, governance structures, and escalation paths.
• CDE inventory. Metadata schema capturing owner, steward, system of record, validation rules, and downstream consumers.
• Validation rule library. Catalogue of automated checks with business rationale, thresholds, and evidence requirements.
• Issue log. Tracker capturing priority, impact, root cause, remediation actions, and status.
• Assurance evidence pack. Template grouping control matrices, test results, exceptions, and management responses for internal and external auditors.

Store artefacts in a controlled repository with versioning, access control, and retention policies aligned with regulatory requirements.