Data Governance and Data Quality — Framework
A two-day course on the Data Governance framework (DAMA-DMBOK) and data stewardship practices, building an organization's ability to ensure data quality under GDPR, AI Act, and DORA regulatory requirements — for organizations where generic data management is not enough without formal governance.
Governance as an answer to three regulations at once
GDPR, AI Act, and DORA place separate but related requirements on organizations regarding data quality and provenance — from personal-data minimization, through AI training-data quality, to ICT risk management in finance. The course shows how a single formal Data Governance program based on DAMA-DMBOK addresses all three requirements at once, instead of building separate, duplicative processes.
Data stewardship as an accountability model
A framework without people accountable for maintaining it becomes dead documentation. The course emphasizes practically implementing data owner and data steward roles — with clearly assigned responsibility for the quality of specific data sets, not an abstract policy on paper.
From data quality to auditable compliance
The final module ties technical data-quality practices (profiling, validation, monitoring) to the audit requirements of three regulations — so the organization can demonstrate compliance during a review, not just declare good practices.
Benefits
- Apply the DAMA-DMBOK framework to build a formal Data Governance program
- Implement data stewardship roles and a data-quality accountability model
- Design data-quality metrics and processes aligned with regulatory requirements
- Map GDPR, AI Act, and DORA requirements to concrete data governance practices
Who is this training for?
Prerequisites
- Basic familiarity with an organization's data structures (databases, warehouses, source systems)
- General GDPR awareness helps but is not required
Training program
The Data Governance framework — DAMA-DMBOK
- DAMA-DMBOK's 11 knowledge areas and how they apply in an organization
- Structuring a Data Governance program: roles, committees, policies
- Data stewardship — the roles of data owners and stewards
- The data catalog and business glossary as the foundation of governance
Data quality — metrics and processes
- Data quality dimensions: completeness, accuracy, consistency, timeliness
- Data profiling — identifying quality issues in source data
- Validation rules and data cleansing processes
- Monitoring data quality over time — dashboards and alerts
Data lineage and metadata management
- Tracking data lineage from source to report
- Managing technical, business, and operational metadata
- Classifying sensitive data and mapping personal data flows
- Tools supporting governance: data catalogs, metadata systems
Data governance under regulation — GDPR, AI Act, DORA
- GDPR: data minimization principles, the processing register, DPIAs
- AI Act: training-data quality requirements for high-risk AI systems
- DORA: data risk management in financial services and ICT third-party risk
- Workshop: building a data governance compliance map for a sample organization
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from the generic Data Management course in our catalog?
The existing course covers the full data lifecycle — acquisition, processing, analysis, and visualization — with an emphasis on analytics tools. This course focuses exclusively on formal governance: the DAMA-DMBOK framework, data stewardship roles, data-quality processes, and maps them directly to GDPR, AI Act, and DORA regulatory requirements. It is not a data-analytics course but a course on building an organizational data-accountability program.
Is DAMA-DMBOK a certification that this course prepares you for?
The course introduces the DAMA-DMBOK (Data Management Body of Knowledge) framework as a methodology for structuring a governance program, but it is not formal preparation for the CDMP (Certified Data Management Professional) certification offered by DAMA International — that is a separate, longer certification path.
Why does the AI Act require data governance if it targets AI systems, not data itself?
The AI Act places direct requirements on the quality and provenance of training, validation, and test data for high-risk AI systems — errors in training data translate into regulatory risk for the entire AI system. Without formal data governance (lineage, quality documentation), an organization cannot demonstrate its AI system's compliance with AI Act requirements during an audit.
Where should an organization with no formal practices start building a Data Governance program?
The course recommends starting with a small, high-value data domain (e.g., customer or financial data) rather than trying to cover governance for the whole organization at once — with a clearly assigned data owner, a basic data catalog, and simple quality metrics. The final workshop shows how to build such a starting map step by step.
Request a quote
Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
Learn moreTrusted by
We train teams at Poland's largest companies
Interested in this training?
Contact us - we'll prepare an offer tailored to your organization's needs.