Governance, Risk & Compliance
Big Data Governance: Bad Data or Better Data or Best Data
This five-day programme prepares data and governance professionals to move an organisation from poor data to trusted data, covering data governance foundations, data quality assessment and remediation, metadata, lineage and catalogues, master, reference and lifecycle data, and security, privacy, compliance and reporting. Participants develop skills in data quality assessment, governance structuring, metadata and lineage management, and data governance reporting.
Introduction
Big Data Governance: Bad Data or Better Data or Best Data Training Course prepares data and governance professionals to move an organisation from poor data to trusted data. Participants work through data quality dimensions and assessment, data governance structures and stewardship, the DAMA-DMBOK framework and data management practice, metadata, lineage, and data catalogues, master and reference data management, and data security, privacy, and compliance as one connected discipline. The programme links the data an organisation holds with the decisions that data supports or undermines. As a result, learners gain a practical method for big data governance. Furthermore, the course strengthens data quality judgement, governance design, and stewardship discipline.
Big Data Governance Course Objectives
- Explain data governance concepts, drivers, and maturity models.
- Apply the DAMA-DMBOK data management knowledge areas.
- Apply data quality dimensions: accuracy, completeness, timeliness, consistency.
- Apply data quality assessment, profiling, and remediation methods.
- Apply data governance structures, stewardship, and ownership models.
- Apply metadata management, data lineage, and data catalogue practice.
- Apply master data and reference data management techniques.
- Apply data security, privacy, and regulatory compliance requirements.
- Apply data architecture, integration, and lifecycle management practice.
- Apply data governance metrics, reporting, and continuous improvement.
Course Methodology
- Interactive sessions built on data governance and data quality cases.
- Workshops on data quality assessment, stewardship models, and metadata.
- Group exercises on data profiling, remediation, and catalogue design.
- Hands-on practice with data quality scorecards, glossaries, and control logs.
- Simulation of a data governance council and a data quality escalation.
- Facilitator coaching and peer review on personal data governance 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 data quality programmes.
Big Data Governance Course Outlines
Data Governance Foundations
- • Data governance concepts, scope, and business drivers.
- • The cost of bad data: decisions, compliance, and trust.
- • Data governance maturity models and assessment.
- • The DAMA-DMBOK data management knowledge areas.
- • Data governance roles: owners, stewards, and custodians.
- • Data governance policy, principles, and operating model.
- • Case discussion: three reports, three numbers, one truth.
Data Quality Assessment and Remediation
- • Data quality dimensions defined and measured.
- • Data profiling and quality assessment techniques.
- • Root cause analysis for data defects.
- • Data cleansing, correction, and remediation workflows.
- • Data quality rules, validation, and control points.
- • Data quality scorecards and monitoring dashboards.
- • Workshop: assessing data quality for a critical dataset.
Metadata, Lineage, and Catalogues
- • Metadata types: technical, business, and operational.
- • Data lineage and impact analysis practice.
- • Business glossary and data dictionary development.
- • Data catalogue design and searchability.
- • Data classification and sensitivity labelling.
- • Data provenance and auditability requirements.
- • Exercise: building a business glossary and lineage map.
Master, Reference, and Lifecycle Data
- • 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 retention schedules and legal hold requirements.
- • Data architecture principles and platform considerations.
- • Simulation: resolving a master data conflict across systems.
Security, Privacy, Compliance, and Reporting
- • Data security controls, access, and least privilege.
- • Data privacy, consent, and lawful processing.
- • Regulatory compliance for data, including data protection law.
- • Data governance metrics and reporting to leadership.
- • Data culture, literacy, and change management.
- • Continuous improvement and governance operating rhythm.
- • Capstone: presenting a big data governance improvement plan.
Conclusion
By successfully completing Big Data Governance: Bad Data or Better Data or Best Data, participants will gain a practical command of data governance foundations, data quality assessment and remediation, metadata, lineage and catalogue practice, master and reference data management, and data security, privacy, compliance and reporting. They will acquire skills in data quality assessment, governance structuring, metadata and lineage management, and data governance reporting that move an organisation from bad data to trusted data. Gentex Training Center designs the programme to help professionals govern big data with confidence.
FAQs for the Big Data Governance course
— What is the "Big Data Governance: Bad Data or Better Data or Best Data" course about?
It prepares data and governance professionals to move an organisation from poor data to trusted data. The course spans data governance foundations, data quality assessment and remediation, metadata, lineage and catalogues, master, reference and lifecycle data, and security, privacy, compliance and reporting.
— What are the key benefits of the "Big Data Governance: Bad Data or Better Data or Best Data" course?
You gain a practical method for big data governance that links the data an organisation holds with the decisions that data supports or undermines. The course improves data quality assessment and remediation, strengthens governance structuring and stewardship, sharpens metadata, lineage and catalogue practice, and gives you data governance metrics and reporting that turn poor data into trusted data.
— What skills will I gain from the "Big Data Governance: Bad Data or Better Data or Best Data" course?
You will develop four core skills: data governance structuring with DAMA-DMBOK knowledge area application, data quality assessment and remediation, metadata, lineage and catalogue management with master data practice, and data security, privacy and governance reporting.
— What tools, methods, or standards are covered in the "Big Data Governance: Bad Data or Better Data or Best Data" course?
The course covers data governance concepts, scope and business drivers, the cost of bad data across decisions, compliance and trust, data governance maturity models and assessment, the DAMA-DMBOK data management knowledge areas, data governance roles of owners, stewards and custodians, data governance policy, principles and operating model, data quality dimensions defined and measured, data profiling and quality assessment techniques, root cause analysis for data defects, data cleansing, correction and remediation workflows, data quality rules, validation and control points, data quality scorecards and monitoring dashboards, metadata types of technical, business and operational, data lineage and impact analysis practice, business glossary and data dictionary development, data catalogue design and searchability, data classification and sensitivity labelling, data provenance and auditability requirements, 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 retention schedules and legal hold requirements, data architecture principles and platform considerations, data security controls, access and least privilege, data privacy, consent and lawful processing, regulatory compliance for data including data protection law, data governance metrics and reporting to leadership, and data culture, literacy and change management.
— How is the "Big Data Governance: Bad Data or Better Data or Best Data" course applied in real-world practice?
Participants apply it to real work such as assessing data quality for a critical dataset, profiling data and tracing defect root causes, building a business glossary and lineage map, designing a data catalogue and classification scheme, resolving a master data conflict across systems, setting data retention and legal hold rules, and presenting a data governance improvement plan. The same methods support privacy compliance reviews and data quality monitoring.
Contact our team and let us help you find the right option.
Introduction
Big Data Governance: Bad Data or Better Data or Best Data Training Course prepares data and governance professionals to move an organisation from poor data to trusted data. Participants work through data quality dimensions and assessment, data governance structures and stewardship, the DAMA-DMBOK framework and data management practice, metadata, lineage, and data catalogues, master and reference data management, and data security, privacy, and compliance as one connected discipline. The programme links the data an organisation holds with the decisions that data supports or undermines. As a result, learners gain a practical method for big data governance. Furthermore, the course strengthens data quality judgement, governance design, and stewardship discipline.
Big Data Governance Course Objectives
- Explain data governance concepts, drivers, and maturity models.
- Apply the DAMA-DMBOK data management knowledge areas.
- Apply data quality dimensions: accuracy, completeness, timeliness, consistency.
- Apply data quality assessment, profiling, and remediation methods.
- Apply data governance structures, stewardship, and ownership models.
- Apply metadata management, data lineage, and data catalogue practice.
- Apply master data and reference data management techniques.
- Apply data security, privacy, and regulatory compliance requirements.
- Apply data architecture, integration, and lifecycle management practice.
- Apply data governance metrics, reporting, and continuous improvement.
Course Methodology
- Interactive sessions built on data governance and data quality cases.
- Workshops on data quality assessment, stewardship models, and metadata.
- Group exercises on data profiling, remediation, and catalogue design.
- Hands-on practice with data quality scorecards, glossaries, and control logs.
- Simulation of a data governance council and a data quality escalation.
- Facilitator coaching and peer review on personal data governance 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 data quality programmes.
Big Data Governance Course Outlines
Data Governance Foundations
- • Data governance concepts, scope, and business drivers.
- • The cost of bad data: decisions, compliance, and trust.
- • Data governance maturity models and assessment.
- • The DAMA-DMBOK data management knowledge areas.
- • Data governance roles: owners, stewards, and custodians.
- • Data governance policy, principles, and operating model.
- • Case discussion: three reports, three numbers, one truth.
Data Quality Assessment and Remediation
- • Data quality dimensions defined and measured.
- • Data profiling and quality assessment techniques.
- • Root cause analysis for data defects.
- • Data cleansing, correction, and remediation workflows.
- • Data quality rules, validation, and control points.
- • Data quality scorecards and monitoring dashboards.
- • Workshop: assessing data quality for a critical dataset.
Metadata, Lineage, and Catalogues
- • Metadata types: technical, business, and operational.
- • Data lineage and impact analysis practice.
- • Business glossary and data dictionary development.
- • Data catalogue design and searchability.
- • Data classification and sensitivity labelling.
- • Data provenance and auditability requirements.
- • Exercise: building a business glossary and lineage map.
Master, Reference, and Lifecycle Data
- • 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 retention schedules and legal hold requirements.
- • Data architecture principles and platform considerations.
- • Simulation: resolving a master data conflict across systems.
Security, Privacy, Compliance, and Reporting
- • Data security controls, access, and least privilege.
- • Data privacy, consent, and lawful processing.
- • Regulatory compliance for data, including data protection law.
- • Data governance metrics and reporting to leadership.
- • Data culture, literacy, and change management.
- • Continuous improvement and governance operating rhythm.
- • Capstone: presenting a big data governance improvement plan.
Conclusion
By successfully completing Big Data Governance: Bad Data or Better Data or Best Data, participants will gain a practical command of data governance foundations, data quality assessment and remediation, metadata, lineage and catalogue practice, master and reference data management, and data security, privacy, compliance and reporting. They will acquire skills in data quality assessment, governance structuring, metadata and lineage management, and data governance reporting that move an organisation from bad data to trusted data. Gentex Training Center designs the programme to help professionals govern big data with confidence.
FAQs for the Big Data Governance course
— What is the "Big Data Governance: Bad Data or Better Data or Best Data" course about?
It prepares data and governance professionals to move an organisation from poor data to trusted data. The course spans data governance foundations, data quality assessment and remediation, metadata, lineage and catalogues, master, reference and lifecycle data, and security, privacy, compliance and reporting.
— What are the key benefits of the "Big Data Governance: Bad Data or Better Data or Best Data" course?
You gain a practical method for big data governance that links the data an organisation holds with the decisions that data supports or undermines. The course improves data quality assessment and remediation, strengthens governance structuring and stewardship, sharpens metadata, lineage and catalogue practice, and gives you data governance metrics and reporting that turn poor data into trusted data.
— What skills will I gain from the "Big Data Governance: Bad Data or Better Data or Best Data" course?
You will develop four core skills: data governance structuring with DAMA-DMBOK knowledge area application, data quality assessment and remediation, metadata, lineage and catalogue management with master data practice, and data security, privacy and governance reporting.
— What tools, methods, or standards are covered in the "Big Data Governance: Bad Data or Better Data or Best Data" course?
The course covers data governance concepts, scope and business drivers, the cost of bad data across decisions, compliance and trust, data governance maturity models and assessment, the DAMA-DMBOK data management knowledge areas, data governance roles of owners, stewards and custodians, data governance policy, principles and operating model, data quality dimensions defined and measured, data profiling and quality assessment techniques, root cause analysis for data defects, data cleansing, correction and remediation workflows, data quality rules, validation and control points, data quality scorecards and monitoring dashboards, metadata types of technical, business and operational, data lineage and impact analysis practice, business glossary and data dictionary development, data catalogue design and searchability, data classification and sensitivity labelling, data provenance and auditability requirements, 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 retention schedules and legal hold requirements, data architecture principles and platform considerations, data security controls, access and least privilege, data privacy, consent and lawful processing, regulatory compliance for data including data protection law, data governance metrics and reporting to leadership, and data culture, literacy and change management.
— How is the "Big Data Governance: Bad Data or Better Data or Best Data" course applied in real-world practice?
Participants apply it to real work such as assessing data quality for a critical dataset, profiling data and tracing defect root causes, building a business glossary and lineage map, designing a data catalogue and classification scheme, resolving a master data conflict across systems, setting data retention and legal hold rules, and presenting a data governance improvement plan. The same methods support privacy compliance reviews and data quality monitoring.
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)
