Data and Information Governance Certification Course

Why Select this Training Course?

In an era steeped in data-centric operations, the Data and Information Governance Certification Course is pivotal for professionals who seek to ensure that their organisations can not only harness the power of their data responsibly but can also protect and manage it in accordance with the highest standards of compliance and efficiency.

How does effective data governance benefit an organisation?

Robust data governance frameworks increase the reliability and quality of data for decision-making, ensuring compliance with regulations and enhancing operational performance.

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Is this course adaptable?

Yes, the course content is regularly updated to reflect the latest data regulation trends, giving attendees the most relevant and current best practices in the field.

Who Should Attend?

Ideal for:

• Data Managers and Analysts
• Compliance Officers
• IT Governance Professionals
• Risk Managers
• Information Officers
• Policy Makers
• Corporate Governance Executives
• Privacy Officers
• Legal Professionals

What are the Course Objectives?

Participants will:

• Develop a comprehensive data governance strategy.
• Understand key legal and compliance requirements affecting data governance.
• Establish frameworks for data quality and lifecycle management.
• Address and mitigate risks associated with data management.
• Facilitate collaboration across departments for data governance initiatives.

How will this course be presented?

Expect:

• Case-study-driven workshops.
• Interactive group exercises.
• Scenario-based role play.
• Practical implementation tips and strategies.
• Guide to current and emerging technologies affecting data governance.

What are the Topics Covered in this Course?

Module 1: Advanced Compliance Management

• Advanced techniques for regulatory compliance monitoring.
• Automated compliance systems and governance dashboards.
• International compliance standards and frameworks.
• Compliance management as an aspect of governance.
• Advanced reporting and documentation for regulatory bodies.

Module 2: Legal Compliance and Ethical Considerations

• Data protection laws and regulations.
• Consequences of non-compliance.
• Navigating cross-border data transfer and privacy concerns.
• Implementing ethical data practices.
• Case studies of legal and ethical data governance.

Module 3: Data Governance Frameworks and Standards

• Overview of industry-standard frameworks (e.g., DAMA DMBOK, COBIT).
• Designing a tailored data governance framework.
• Implementing a data governance maturity model.
• Benchmarks and metrics for evaluating data governance.
• Integrating data governance with corporate governance structures.
• Governance frameworks for different types of data (structured, unstructured).
• Change management in data governance frameworks.
• Continuous improvement and iteration of data governance strategy.

Module 4: Data Quality Management

• Defining and measuring data quality.
• Tools and techniques for data quality improvement.
• Data cleansing and de-duplication strategies.
• Data profiling and metadata management.
• Establishing a data quality management plan.
• Resolving data quality issues.
• Maintaining data quality in big data environments.
• Monitoring and reporting on data quality.

Module 5: Risk Management in Data Governance

• Assessing risks associated with data management.
• Mitigating data security risks.
• Developing a risk-based approach to data governance.

Module 6: Data Lifecycle Management

• The data lifecycle from creation to retirement.
• Information asset registers and inventories.
• Data storage, archiving, and destruction best practices.
• Data ownership and stewardship throughout the lifecycle.
• Managing data access and permission controls.
• Leveraging technology for data lifecycle management.
• Auditing data lifecycle processes.
• Ensuring compliance in each stage of the data lifecycle.
• Disaster recovery planning and business continuity.
• Environmental considerations and sustainability in data lifecycle management.

Module 7: Data Security and Privacy

• The importance of data privacy and how it intersects with security.
• Encryption, anonymisation, and pseudonymisation of data.
• Data breach prevention and management.
• The role of cybersecurity in data governance.
• Privacy by Design and by Default principles.
• Data privacy impact assessments (DPIA).
• Implementing and managing access controls.
• Training for data privacy and security awareness.

Module 8: Data Architecture and Integration

• Aligning data architecture with governance objectives.
• Data warehousing and business intelligence governance.
• Integrating disparate data sources.
• Managing data in hybrid and multi-cloud environments.
• Considerations for data architecture in digital transformation.
• The role of APIs in data integration and governance.
• Leveraging data lakes for governance.
• Master Data Management (MDM) strategies.

Module 9: Technology Trends Impacting Data Governance

• Artificial intelligence and machine learning in data management.
• Blockchain for secure and transparent data handling.
• Developments in cloud computing affecting governance.
• Internet of Things (IoT) and governance of generated data.
• The impact of big data technologies on governance strategies.
• Utilising data governance tools and software solutions.
• Data governance implications of advanced analytics and business intelligence outputs.
• Regulatory technology (RegTech) for automated compliance.
• Ethical considerations and societal impact of emerging technologies.
• Strategies for adopting new technologies while maintaining governance controls.

Module 10: Mastering Data Governance in Practice

• Case studies illustrating successful data governance initiatives.
• Developing a communicative and transparent governance culture.
• Real-life challenges and solutions in data governance.
• Practical steps for implementing a data governance program.
• Aligning data governance with business strategy for added value.

Module 11: Data Governance in Specific Sectors

• Sector-specific data governance challenges and opportunities.
• Comparing governance requirements across industries.
• Best practices for sector-specific data governance.
• Regulatory nuances and compliance in different sectors.
• Sector-driven data governance frameworks.

Module 12: Data Governance Project Management

• Project planning and management for data governance initiatives.
• Resource allocation and budgeting for data governance projects.
• Monitoring and controlling data governance activities.
• Risk management within data governance projects.
• Ensuring stakeholder engagement and cross-department collaboration.

Module 13: Establishing Data Stewardship and Ownership

• Data stewardship roles and responsibilities.
• Assigning data ownership across organizations.
• Balancing data ownership with organizational sharing requirements.
• Creating data stewardship communities of practice.
• Training and developing data stewards.

Module 14: Performance Measurement in Data Governance

• Key Performance Indicators (KPIs) for data governance.
• Aligning KPIs with business outcomes.
• Developing a data governance scorecard.
• Quantitative and qualitative measures of governance performance.
• Continuous monitoring and reporting mechanisms.

Module 15: Developing Policies and Standards for Data Governance

• Drafting comprehensive data governance policies.
• Standards development and implementation.
• Policy and standards alignment with external regulations.
• Updating governance policies to reflect technology advancements.
• Engaging stakeholders in policy development and adherence.

Module 16: Designing a Data Governance Organisation

• Organisational structures for effective data governance.
• Roles of committees and working groups in governance.
• Frameworks for governance operations within an organisation.
• Managing the cultural change accompanying governance programs.
• The relationship between data governance and corporate governance.

Module 17: The Role of Data Governance in Business Transformation

• Data governance in digital transformation initiatives.
• Leveraging data governance for competitive advantage.
• Business process re-engineering through robust data governance.
• Case studies of transformation driven by data governance.
• Future-proofing an organisation through adaptive governance models.

Module 18: Data Ethics and Cultural Considerations

• Cultivating an ethical culture around data use.
• Addressing cultural challenges in implementing data governance.
• Balancing data innovation with ethical restrictions.
• Case studies of ethical dilemmas in data governance.
• Strategies for fostering ethical data use globally.