Governance, Risk & Compliance
Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation
This five-day programme prepares data professionals to build a governed data capability that an organisation can rely on, covering data governance foundations and frameworks, data quality assessment and improvement, metadata, lineage and catalogues, master data, reference data and architecture, and implementation, maturity and adoption. Participants develop skills in governance operating model design, data quality assessment, metadata and lineage management, and implementation planning.
Introduction
Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation Training Course prepares data professionals to build a governed data capability that an organisation can rely on. Participants work through data governance concepts and operating models, the DAMA-DMBOK knowledge areas, data quality dimensions, assessment, and remediation, metadata management, data lineage, and catalogue practice, master and reference data management, and implementation planning, maturity assessment, and change adoption as one connected discipline. The programme links the data an organisation holds with the decisions and reporting that data either supports or undermines. As a result, learners gain a practical method for data governance and management. Furthermore, the course strengthens governance design, data quality judgement, and implementation discipline.
Data Governance & Data Management Course Objectives
- Explain data governance concepts, drivers, and operating models.
- Apply the DAMA-DMBOK data management knowledge areas.
- Apply data governance structures, stewardship, and ownership models.
- Apply data quality dimensions: accuracy, completeness, timeliness, consistency.
- Apply data profiling, quality assessment, and remediation methods.
- Apply metadata management, data lineage, and data catalogue practice.
- Apply business glossary and data dictionary development.
- Apply master data and reference data management techniques.
- Apply data security, privacy, and regulatory compliance requirements.
- Apply implementation roadmaps, maturity assessment, and change adoption.
Course Methodology
- Interactive sessions built on data governance and management cases.
- Workshops on operating models, quality assessment, and metadata design.
- Group exercises on profiling, remediation, and catalogue construction.
- Hands-on practice with scorecards, glossaries, and governance charters.
- Simulation of a data governance council and a quality escalation.
- Facilitator coaching and peer review on personal implementation plans.
Who Should Take This Course
- Data governance managers and data stewards.
- Chief data officers and data management leaders.
- Data quality analysts and data architects.
- Business intelligence and analytics professionals.
- Compliance, privacy, and information security officers.
- Consultants advising on data governance and management programmes.
Data Governance & Data Management Course Outlines
Data Governance Foundations and Frameworks
- • Data governance concepts, scope, and business drivers.
- • The cost of poor data: decisions, compliance, and trust.
- • DAMA-DMBOK knowledge areas and their relationships.
- • Data governance operating models: centralised, federated, hybrid.
- • Roles: data owner, steward, custodian, and council.
- • Data governance policy, principles, and charters.
- • Case discussion: three reports, three numbers, one truth.
Data Quality Assessment and Improvement
- • Data quality dimensions defined and measured.
- • Data quality frameworks and rule design.
- • Data profiling techniques and assessment methods.
- • Root cause analysis for data defects.
- • Data cleansing, correction, and remediation workflows.
- • Data quality scorecards, KPIs, and monitoring.
- • Workshop: assessing data quality for a critical dataset.
Metadata, Lineage, and Catalogues
- • Metadata types: technical, business, and operational.
- • Metadata management architecture and repositories.
- • Data lineage, provenance, and impact analysis.
- • Business glossary and data dictionary development.
- • Data catalogue design, search, and stewardship.
- • Data classification and sensitivity labelling.
- • Exercise: building a business glossary and lineage map.
Master Data, Reference Data, and Architecture
- • Master data management concepts and domain models.
- • Reference data management and code sets.
- • Data integration patterns and golden record creation.
- • Data lifecycle: creation, storage, archival, and disposal.
- • Data architecture principles and platform considerations.
- • Data security, privacy, and regulatory compliance duties.
- • Simulation: resolving a master data conflict across systems.
Implementation, Maturity, and Adoption
- • Building an implementation roadmap and milestones.
- • Quick wins, pilots, and scaling strategies.
- • Data governance metrics and value demonstration.
- • Data governance maturity models and assessment.
- • Data culture, literacy, and change adoption.
- • Governance council operation and continuous improvement.
- • Capstone: presenting a data governance and management implementation plan.
Conclusion
By successfully completing Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation, participants will gain a practical command of data governance frameworks and operating models, data quality assessment and improvement, metadata, lineage and catalogue practice, master and reference data management, and implementation, maturity and change adoption. They will acquire skills in governance operating model design, data quality assessment, metadata and lineage management, and implementation planning that move an organisation from inconsistent data to trusted data. Gentex Training Center designs the programme to help professionals govern and manage data with confidence.
FAQs for the Data Governance & Data Management course
— What is the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course about?
It prepares data professionals to build a governed data capability that an organisation can rely on. The course spans data governance foundations and frameworks, data quality assessment and improvement, metadata, lineage and catalogues, master data, reference data and architecture, and implementation, maturity and adoption.
— What are the key benefits of the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
You gain a practical method for data governance and management that links the data an organisation holds with the decisions and reporting that data either supports or undermines. The course improves operating model design and stewardship practice, strengthens data quality assessment and remediation, sharpens metadata, lineage and catalogue practice, and gives you roadmap, maturity and adoption practice that makes governance stick.
— What skills will I gain from the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
You will develop four core skills: data governance framework and operating model design, data quality assessment and remediation, metadata, lineage and catalogue management with master data practice, and implementation roadmapping with maturity assessment and change adoption.
— What tools, methods, or standards are covered in the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
The course covers data governance concepts, scope and business drivers, the cost of poor data across decisions, compliance and trust, the DAMA-DMBOK knowledge areas and their relationships, data governance operating models of centralised, federated and hybrid, roles of data owner, steward, custodian and council, data governance policy, principles and charters, data quality dimensions defined and measured, data quality frameworks and rule design, data profiling techniques and assessment methods, root cause analysis for data defects, data cleansing, correction and remediation workflows, data quality scorecards, KPIs and monitoring, metadata types of technical, business and operational, metadata management architecture and repositories, data lineage, provenance and impact analysis, business glossary and data dictionary development, data catalogue design, search and stewardship, data classification and sensitivity labelling, master data management concepts and domain models, reference data management and code sets, data integration patterns and golden record creation, the data lifecycle of creation, storage, archival and disposal, data architecture principles and platform considerations, data security, privacy and regulatory compliance duties, building an implementation roadmap and milestones, quick wins, pilots and scaling strategies, data governance metrics and value demonstration, data governance maturity models and assessment, data culture, literacy and change adoption, and governance council operation and continuous improvement.
— How is the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course applied in real-world practice?
Participants apply it to real work such as designing a data governance operating model, assessing data quality for a critical dataset, profiling data and tracing defect root causes, building a business glossary and lineage map, constructing a data catalogue with classification labels, resolving a master data conflict across systems, and presenting a data governance implementation roadmap. The same methods support privacy compliance reviews and governance council operation.
Contact our team and let us help you find the right option.
Introduction
Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation Training Course prepares data professionals to build a governed data capability that an organisation can rely on. Participants work through data governance concepts and operating models, the DAMA-DMBOK knowledge areas, data quality dimensions, assessment, and remediation, metadata management, data lineage, and catalogue practice, master and reference data management, and implementation planning, maturity assessment, and change adoption as one connected discipline. The programme links the data an organisation holds with the decisions and reporting that data either supports or undermines. As a result, learners gain a practical method for data governance and management. Furthermore, the course strengthens governance design, data quality judgement, and implementation discipline.
Data Governance & Data Management Course Objectives
- Explain data governance concepts, drivers, and operating models.
- Apply the DAMA-DMBOK data management knowledge areas.
- Apply data governance structures, stewardship, and ownership models.
- Apply data quality dimensions: accuracy, completeness, timeliness, consistency.
- Apply data profiling, quality assessment, and remediation methods.
- Apply metadata management, data lineage, and data catalogue practice.
- Apply business glossary and data dictionary development.
- Apply master data and reference data management techniques.
- Apply data security, privacy, and regulatory compliance requirements.
- Apply implementation roadmaps, maturity assessment, and change adoption.
Course Methodology
- Interactive sessions built on data governance and management cases.
- Workshops on operating models, quality assessment, and metadata design.
- Group exercises on profiling, remediation, and catalogue construction.
- Hands-on practice with scorecards, glossaries, and governance charters.
- Simulation of a data governance council and a quality escalation.
- Facilitator coaching and peer review on personal implementation plans.
Who Should Take This Course
- Data governance managers and data stewards.
- Chief data officers and data management leaders.
- Data quality analysts and data architects.
- Business intelligence and analytics professionals.
- Compliance, privacy, and information security officers.
- Consultants advising on data governance and management programmes.
Data Governance & Data Management Course Outlines
Data Governance Foundations and Frameworks
- • Data governance concepts, scope, and business drivers.
- • The cost of poor data: decisions, compliance, and trust.
- • DAMA-DMBOK knowledge areas and their relationships.
- • Data governance operating models: centralised, federated, hybrid.
- • Roles: data owner, steward, custodian, and council.
- • Data governance policy, principles, and charters.
- • Case discussion: three reports, three numbers, one truth.
Data Quality Assessment and Improvement
- • Data quality dimensions defined and measured.
- • Data quality frameworks and rule design.
- • Data profiling techniques and assessment methods.
- • Root cause analysis for data defects.
- • Data cleansing, correction, and remediation workflows.
- • Data quality scorecards, KPIs, and monitoring.
- • Workshop: assessing data quality for a critical dataset.
Metadata, Lineage, and Catalogues
- • Metadata types: technical, business, and operational.
- • Metadata management architecture and repositories.
- • Data lineage, provenance, and impact analysis.
- • Business glossary and data dictionary development.
- • Data catalogue design, search, and stewardship.
- • Data classification and sensitivity labelling.
- • Exercise: building a business glossary and lineage map.
Master Data, Reference Data, and Architecture
- • Master data management concepts and domain models.
- • Reference data management and code sets.
- • Data integration patterns and golden record creation.
- • Data lifecycle: creation, storage, archival, and disposal.
- • Data architecture principles and platform considerations.
- • Data security, privacy, and regulatory compliance duties.
- • Simulation: resolving a master data conflict across systems.
Implementation, Maturity, and Adoption
- • Building an implementation roadmap and milestones.
- • Quick wins, pilots, and scaling strategies.
- • Data governance metrics and value demonstration.
- • Data governance maturity models and assessment.
- • Data culture, literacy, and change adoption.
- • Governance council operation and continuous improvement.
- • Capstone: presenting a data governance and management implementation plan.
Conclusion
By successfully completing Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation, participants will gain a practical command of data governance frameworks and operating models, data quality assessment and improvement, metadata, lineage and catalogue practice, master and reference data management, and implementation, maturity and change adoption. They will acquire skills in governance operating model design, data quality assessment, metadata and lineage management, and implementation planning that move an organisation from inconsistent data to trusted data. Gentex Training Center designs the programme to help professionals govern and manage data with confidence.
FAQs for the Data Governance & Data Management course
— What is the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course about?
It prepares data professionals to build a governed data capability that an organisation can rely on. The course spans data governance foundations and frameworks, data quality assessment and improvement, metadata, lineage and catalogues, master data, reference data and architecture, and implementation, maturity and adoption.
— What are the key benefits of the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
You gain a practical method for data governance and management that links the data an organisation holds with the decisions and reporting that data either supports or undermines. The course improves operating model design and stewardship practice, strengthens data quality assessment and remediation, sharpens metadata, lineage and catalogue practice, and gives you roadmap, maturity and adoption practice that makes governance stick.
— What skills will I gain from the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
You will develop four core skills: data governance framework and operating model design, data quality assessment and remediation, metadata, lineage and catalogue management with master data practice, and implementation roadmapping with maturity assessment and change adoption.
— What tools, methods, or standards are covered in the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course?
The course covers data governance concepts, scope and business drivers, the cost of poor data across decisions, compliance and trust, the DAMA-DMBOK knowledge areas and their relationships, data governance operating models of centralised, federated and hybrid, roles of data owner, steward, custodian and council, data governance policy, principles and charters, data quality dimensions defined and measured, data quality frameworks and rule design, data profiling techniques and assessment methods, root cause analysis for data defects, data cleansing, correction and remediation workflows, data quality scorecards, KPIs and monitoring, metadata types of technical, business and operational, metadata management architecture and repositories, data lineage, provenance and impact analysis, business glossary and data dictionary development, data catalogue design, search and stewardship, data classification and sensitivity labelling, master data management concepts and domain models, reference data management and code sets, data integration patterns and golden record creation, the data lifecycle of creation, storage, archival and disposal, data architecture principles and platform considerations, data security, privacy and regulatory compliance duties, building an implementation roadmap and milestones, quick wins, pilots and scaling strategies, data governance metrics and value demonstration, data governance maturity models and assessment, data culture, literacy and change adoption, and governance council operation and continuous improvement.
— How is the "Data Governance & Data Management: Frameworks, Quality, Metadata & Implementation" course applied in real-world practice?
Participants apply it to real work such as designing a data governance operating model, assessing data quality for a critical dataset, profiling data and tracing defect root causes, building a business glossary and lineage map, constructing a data catalogue with classification labels, resolving a master data conflict across systems, and presenting a data governance implementation roadmap. The same methods support privacy compliance reviews and governance council operation.
Partners in Learning
Our Partners in Learning include leading companies and organizations that trust Gentex Training Center for high-quality training courses and professional development programs. Together, we deliver impactful training programs that support skills growth, leadership development, and organizational excellence.
.png)
