Governance, Risk & Compliance
Data Governance, Quality & Compliance Management
This five-day programme prepares professionals to manage data as a governed, quality-assured, and compliant asset, covering data governance foundations, data quality management, metadata, lineage and control documentation, data compliance and regulatory obligations, and monitoring, audit and governance reporting. Participants develop skills in governance design, data quality management, compliance mapping, and data governance reporting.
Introduction
Data Governance, Quality & Compliance Management Training Course prepares professionals to manage data as a governed, quality-assured, and compliant asset. Participants work through data governance frameworks and operating models, the DAMA-DMBOK knowledge areas, data quality dimensions, assessment, and remediation, regulatory compliance obligations for data including privacy and retention rules, metadata, lineage, and control documentation, and compliance monitoring, audit, and reporting as one connected discipline. The programme links the data an organisation holds with the quality, compliance, and governance discipline that makes it trustworthy and lawful. As a result, learners gain a practical command of data governance, quality, and compliance. Furthermore, the course strengthens governance judgement, quality discipline, and compliance literacy.
Data Governance, Quality & Compliance 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 data quality rules, controls, and monitoring practice.
- Apply regulatory compliance obligations for data handling and retention.
- Apply privacy, consent, and lawful processing requirements to data.
- Apply metadata, lineage, and control documentation for compliance evidence.
- Apply compliance monitoring, audit, and data governance reporting.
Course Methodology
- Interactive sessions built on data governance, quality, and compliance cases.
- Workshops on operating models, quality rules, and compliance mapping.
- Group exercises on profiling, remediation, and control documentation.
- Hands-on practice with scorecards, processing registers, and audit evidence.
- Simulation of a data governance council and a compliance review.
- Facilitator coaching and peer review on personal governance plans.
Who Should Take This Course
- Data governance managers and data stewards.
- Data quality analysts and data architects.
- Compliance, privacy, and regulatory affairs officers.
- Risk managers and internal auditors covering data.
- Business intelligence and analytics professionals.
- Consultants advising on data governance and compliance programmes.
Data Governance, Quality & Compliance Management Course Outlines
Data Governance Foundations
- • 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 Management
- • Data quality dimensions defined and measured.
- • Data quality frameworks, rules, and validation 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: designing data quality rules for a critical dataset.
Metadata, Lineage, and Control Documentation
- • Metadata types: technical, business, and operational.
- • Metadata management architecture and repositories.
- • Data lineage, provenance, and impact analysis.
- • Business glossary and data dictionary development.
- • Control documentation and evidence retention for audit.
- • Data classification and sensitivity labelling.
- • Exercise: building a lineage map and control evidence file.
Data Compliance and Regulatory Obligations
- • Regulatory landscape for data: privacy, retention, and sector rules.
- • Lawful processing, consent, and purpose limitation.
- • Data subject rights and request handling procedures.
- • Records of processing and compliance registers.
- • Data retention schedules, legal hold, and disposal rules.
- • Cross-border transfer and third-party data arrangements.
- • Simulation: running a data compliance review.
Monitoring, Audit, and Governance Reporting
- • Compliance monitoring, testing, and control assurance.
- • Internal audit of data governance and compliance.
- • Metrics, KPIs, and integrated governance reporting.
- • Data governance maturity assessment and roadmap.
- • Data culture, literacy, and change adoption.
- • Continuous improvement and regulatory horizon scanning.
- • Capstone: presenting a data governance, quality and compliance plan.
Conclusion
By successfully completing Data Governance, Quality & Compliance Management, participants will gain a practical command of data governance frameworks and operating models, data quality assessment and remediation, metadata, lineage and control documentation, data compliance and regulatory obligations, and monitoring, audit and governance reporting. They will acquire skills in governance design, data quality management, compliance mapping, and data governance reporting that make data trustworthy, lawful and auditable. Gentex Training Center designs the programme to help professionals govern data quality and compliance with confidence.
FAQs for the Data Governance, Quality & Compliance Management course
— What is the "Data Governance, Quality & Compliance Management" course about?
It prepares professionals to manage data as a governed, quality-assured, and compliant asset. The course spans data governance foundations, data quality management, metadata, lineage and control documentation, data compliance and regulatory obligations, and monitoring, audit and governance reporting.
— What are the key benefits of the "Data Governance, Quality & Compliance Management" course?
You gain a practical command of data governance, quality, and compliance that links the data an organisation holds with the quality, compliance, and governance discipline that makes it trustworthy and lawful. The course improves operating model design and quality rule practice, strengthens profiling and remediation, sharpens compliance mapping and evidence documentation, and gives you monitoring and reporting practice that stands up to audit and regulators.
— What skills will I gain from the "Data Governance, Quality & Compliance Management" course?
You will develop four core skills: data governance framework and operating model design, data quality assessment, rule design and remediation, metadata, lineage and control documentation with compliance mapping, and compliance monitoring with data governance reporting.
— What tools, methods, or standards are covered in the "Data Governance, Quality & Compliance Management" 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, rules and validation 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, control documentation and evidence retention for audit, data classification and sensitivity labelling, the regulatory landscape for data across privacy, retention and sector rules, lawful processing, consent and purpose limitation, data subject rights and request handling procedures, records of processing and compliance registers, data retention schedules, legal hold and disposal rules, cross-border transfer and third-party data arrangements, compliance monitoring, testing and control assurance, internal audit of data governance and compliance, metrics, KPIs and integrated governance reporting, data governance maturity assessment and roadmapping, and data culture, literacy and change adoption.
— How is the "Data Governance, Quality & Compliance Management" course applied in real-world practice?
Participants apply it to real work such as designing a data governance operating model, designing data quality rules for a critical dataset, profiling data and tracing defect root causes, building a lineage map and control evidence file, maintaining a records of processing register, setting retention and disposal rules, running a data compliance review, and presenting a governance, quality and compliance plan. The same methods support internal audit of data and governance maturity improvement.
Contact our team and let us help you find the right option.
Introduction
Data Governance, Quality & Compliance Management Training Course prepares professionals to manage data as a governed, quality-assured, and compliant asset. Participants work through data governance frameworks and operating models, the DAMA-DMBOK knowledge areas, data quality dimensions, assessment, and remediation, regulatory compliance obligations for data including privacy and retention rules, metadata, lineage, and control documentation, and compliance monitoring, audit, and reporting as one connected discipline. The programme links the data an organisation holds with the quality, compliance, and governance discipline that makes it trustworthy and lawful. As a result, learners gain a practical command of data governance, quality, and compliance. Furthermore, the course strengthens governance judgement, quality discipline, and compliance literacy.
Data Governance, Quality & Compliance 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 data quality rules, controls, and monitoring practice.
- Apply regulatory compliance obligations for data handling and retention.
- Apply privacy, consent, and lawful processing requirements to data.
- Apply metadata, lineage, and control documentation for compliance evidence.
- Apply compliance monitoring, audit, and data governance reporting.
Course Methodology
- Interactive sessions built on data governance, quality, and compliance cases.
- Workshops on operating models, quality rules, and compliance mapping.
- Group exercises on profiling, remediation, and control documentation.
- Hands-on practice with scorecards, processing registers, and audit evidence.
- Simulation of a data governance council and a compliance review.
- Facilitator coaching and peer review on personal governance plans.
Who Should Take This Course
- Data governance managers and data stewards.
- Data quality analysts and data architects.
- Compliance, privacy, and regulatory affairs officers.
- Risk managers and internal auditors covering data.
- Business intelligence and analytics professionals.
- Consultants advising on data governance and compliance programmes.
Data Governance, Quality & Compliance Management Course Outlines
Data Governance Foundations
- • 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 Management
- • Data quality dimensions defined and measured.
- • Data quality frameworks, rules, and validation 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: designing data quality rules for a critical dataset.
Metadata, Lineage, and Control Documentation
- • Metadata types: technical, business, and operational.
- • Metadata management architecture and repositories.
- • Data lineage, provenance, and impact analysis.
- • Business glossary and data dictionary development.
- • Control documentation and evidence retention for audit.
- • Data classification and sensitivity labelling.
- • Exercise: building a lineage map and control evidence file.
Data Compliance and Regulatory Obligations
- • Regulatory landscape for data: privacy, retention, and sector rules.
- • Lawful processing, consent, and purpose limitation.
- • Data subject rights and request handling procedures.
- • Records of processing and compliance registers.
- • Data retention schedules, legal hold, and disposal rules.
- • Cross-border transfer and third-party data arrangements.
- • Simulation: running a data compliance review.
Monitoring, Audit, and Governance Reporting
- • Compliance monitoring, testing, and control assurance.
- • Internal audit of data governance and compliance.
- • Metrics, KPIs, and integrated governance reporting.
- • Data governance maturity assessment and roadmap.
- • Data culture, literacy, and change adoption.
- • Continuous improvement and regulatory horizon scanning.
- • Capstone: presenting a data governance, quality and compliance plan.
Conclusion
By successfully completing Data Governance, Quality & Compliance Management, participants will gain a practical command of data governance frameworks and operating models, data quality assessment and remediation, metadata, lineage and control documentation, data compliance and regulatory obligations, and monitoring, audit and governance reporting. They will acquire skills in governance design, data quality management, compliance mapping, and data governance reporting that make data trustworthy, lawful and auditable. Gentex Training Center designs the programme to help professionals govern data quality and compliance with confidence.
FAQs for the Data Governance, Quality & Compliance Management course
— What is the "Data Governance, Quality & Compliance Management" course about?
It prepares professionals to manage data as a governed, quality-assured, and compliant asset. The course spans data governance foundations, data quality management, metadata, lineage and control documentation, data compliance and regulatory obligations, and monitoring, audit and governance reporting.
— What are the key benefits of the "Data Governance, Quality & Compliance Management" course?
You gain a practical command of data governance, quality, and compliance that links the data an organisation holds with the quality, compliance, and governance discipline that makes it trustworthy and lawful. The course improves operating model design and quality rule practice, strengthens profiling and remediation, sharpens compliance mapping and evidence documentation, and gives you monitoring and reporting practice that stands up to audit and regulators.
— What skills will I gain from the "Data Governance, Quality & Compliance Management" course?
You will develop four core skills: data governance framework and operating model design, data quality assessment, rule design and remediation, metadata, lineage and control documentation with compliance mapping, and compliance monitoring with data governance reporting.
— What tools, methods, or standards are covered in the "Data Governance, Quality & Compliance Management" 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, rules and validation 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, control documentation and evidence retention for audit, data classification and sensitivity labelling, the regulatory landscape for data across privacy, retention and sector rules, lawful processing, consent and purpose limitation, data subject rights and request handling procedures, records of processing and compliance registers, data retention schedules, legal hold and disposal rules, cross-border transfer and third-party data arrangements, compliance monitoring, testing and control assurance, internal audit of data governance and compliance, metrics, KPIs and integrated governance reporting, data governance maturity assessment and roadmapping, and data culture, literacy and change adoption.
— How is the "Data Governance, Quality & Compliance Management" course applied in real-world practice?
Participants apply it to real work such as designing a data governance operating model, designing data quality rules for a critical dataset, profiling data and tracing defect root causes, building a lineage map and control evidence file, maintaining a records of processing register, setting retention and disposal rules, running a data compliance review, and presenting a governance, quality and compliance plan. The same methods support internal audit of data and governance maturity improvement.
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)
