Name: Data Governance and Data Quality for Regulatory Reporting
Availability: InStock

Data Governance and Data Quality for Regulatory Reporting

A practical course for finance professionals who work with supervisory reporting, risk data, and structured regulatory submissions. The agenda is aligned with the kinds of governance, architecture, control, and reporting discipline expected in modern regulatory reporting environments, including the emphasis on accuracy, integrity, completeness, timeliness, and adaptability found in BCBS 239 and recent ECB supervisory guidance, as well as the structured reporting frameworks maintained by the EBA.

What will you learn?

You will learn how to build stronger data governance and data quality practices around regulatory reporting processes, datasets, and controls. You will connect business ownership, data lineage, validation, remediation, and reporting workflows into a more consistent operating model. You will also strengthen your ability to support auditability, supervisory dialogue, and management confidence in reported numbers.

Understand the governance and data quality foundations of effective regulatory reporting
Strengthen ownership, lineage, controls, and validation across reporting datasets
Improve the reliability of supervisory, risk, and compliance related submissions
Build a more sustainable reporting model for business, control, and regulatory stakeholders

Requirements:

Suitable for professionals in finance, risk, regulatory reporting, compliance, controls, and data functions
Basic familiarity with reporting processes, data flows, or control environments is helpful
No specialist technical background required

Course Outline*:

*We customize the course outline and content to your specific needs and relevant use cases.

Module 1: Regulatory reporting data governance fundamentals

Why data governance matters in supervisory and regulatory reporting
Typical reporting data flows from source systems to submitted outputs
Roles and responsibilities across business, finance, risk, IT, and controls
Common governance weaknesses that undermine reporting quality

Module 2: Data ownership, lineage, and reporting accountability

Defining ownership for data elements, rules, controls, and submissions
Tracing lineage from source, transformation, and aggregation to final report
Clarifying accountability across handoffs, reconciliations, and signoff points
Building transparency for audit, review, and supervisory scrutiny

Module 3: Core dimensions of data quality in reporting

Accuracy, completeness, consistency, timeliness, and traceability in practice
Typical data quality issues in regulatory and risk datasets
Distinguishing business rule failures from source data defects and process weaknesses
Structuring practical quality checks that support reliable reporting

Module 4: Controls, validation, and issue management

Preventive and detective controls in reporting processes
Validation logic, thresholds, exception handling, and escalation
Organizing issue logging, root cause analysis, and remediation tracking
Linking data quality problems to governance action and process improvement

Module 5: Reporting architecture and control points

Understanding how source systems, marts, calculation layers, and submission outputs interact
Identifying where key quality risks arise in aggregation and transformation
Designing practical control points across the reporting chain
Aligning architecture choices with governance and reporting discipline

Module 6: Data quality for supervisory, risk, and structured submissions

Handling structured submissions, validation rules, and reporting taxonomies
Strengthening consistency across finance, risk, and prudential reporting views
Managing reporting cutoffs, period end pressures, and late adjustments
Improving readiness for recurring and ad hoc supervisory requests

Module 7: Management reporting, evidence, and auditability

Building management visibility over data quality and reporting health
Creating evidence trails that support internal review and audit
Documenting definitions, assumptions, rule changes, and signoff decisions
Supporting confidence in reported numbers across governance forums

Module 8: Practical roadmap for stronger data governance

Prioritizing governance and quality improvements by business impact and regulatory risk
Balancing short term reporting fixes with longer term operating model changes
Strengthening coordination between reporting teams, data teams, and control functions
Building a practical checklist for future regulatory reporting readiness

Hands-on learning with expert instructors at your location for organizations.

3.072€*

Level:

intermediate

Duration:

Hours (days:

)

Training customized to your needs

Immersive hands-on experience in a dedicated setting

*Price can range depending on number of participants, change of outline, location etc.

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Master new skills guided by experienced instructors from anywhere.

2.037€*

Level:

intermediate

Duration:

Hours (days:

)

Training customized to your needs

Reduced training costs

*Price can range depending on number of participants, change of outline, location etc.

Get Quote

Data Governance and Data Quality for Regulatory Reporting

What will you learn?

Requirements:

Course Outline*:

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Data Governance and Data Quality for Regulatory Reporting

What will you learn?

Requirements:

Course Outline*:

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