15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Governance Professionals
- Data Quality Analysts
- Compliance Officers
- Data Managers
- Database Administrators
- Business Analysts
- Anyone needing data governance and quality management skills
Session Objectives
- Understand the fundamentals of data governance and quality management.
- Master data profiling and data quality assessment techniques.
- Utilize data cleansing and data standardization methods.
- Implement data governance policies and procedures.
- Design and build data quality monitoring and reporting systems.
- Optimize data quality for business intelligence and analytics.
- Troubleshoot and address common data quality issues.
- Implement data security and access control measures.
- Integrate data governance and quality management with real-world applications.
- Understand how to handle data privacy and ensure data integrity.
- Explore advanced data governance and quality management techniques (e.g., data lineage, metadata management).
- Apply real world use cases for data governance and quality management.
- Leverage data governance and quality management tools for efficient implementation.
About the Course
Build an unshakeable foundation of data trust and regulatory compliance with our Data Governance and Quality Management Training Course. Tailored for data governance leads, quality analysts, and compliance officers, this practical program equips you to design and implement rigorous quality controls across modern enterprise data pipelines. Guided by governance experts, you will master data profiling techniques, automated data cleansing workflows, schema validation, and regulatory compliance frameworks—enabling your enterprise to eliminate data decay, enforce stewardship standards, and deliver audit-ready data for strategic analytics.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data governance and quality management.
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Workshop 1.2:
Overview of data profiling, data cleansing, and governance policies.
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Workshop 1.3:
Setting up a data governance and quality management environment.
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Workshop 1.4:
Introduction to data governance and quality management tools.
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Workshop 1.5:
Best practices for data governance and quality.
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Workshop 2.1:
Mastering data profiling and data quality assessment techniques.
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Workshop 2.2:
Utilizing statistical analysis and data visualization.
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Workshop 2.3:
Designing and building data quality metrics.
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Workshop 2.4:
Optimizing data profiling for data integrity.
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Workshop 2.5:
Best practices for data profiling.
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Workshop 3.1:
Utilizing data cleansing and data standardization methods.
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Workshop 3.2:
Implementing data normalization and deduplication.
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Workshop 3.3:
Designing and building data cleansing workflows.
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Workshop 3.4:
Optimizing data standardization for consistency.
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Workshop 3.5:
Best practices for data cleansing.
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Workshop 4.1:
Implementing data governance policies and procedures.
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Workshop 4.2:
Utilizing data governance frameworks (DAMA-DMBOK, COBIT).
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Workshop 4.3:
Designing and building data governance roles and responsibilities.
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Workshop 4.4:
Optimizing policies for effective data management.
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Workshop 4.5:
Best practices for data governance policies.
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Workshop 5.1:
Designing and building data quality monitoring and reporting systems.
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Workshop 5.2:
Utilizing data quality dashboards and alerts.
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Workshop 5.3:
Implementing data quality audits and reviews.
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Workshop 5.4:
Optimizing monitoring for proactive data quality management.
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Workshop 5.5:
Best practices for monitoring.
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Workshop 6.1:
Optimizing data quality for business intelligence and analytics.
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Workshop 6.2:
Utilizing data quality metrics for reporting and analysis.
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Workshop 6.3:
Implementing data quality improvements for decision-making.
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Workshop 6.4:
Designing scalable data quality solutions.
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Workshop 6.5:
Best practices for business intelligence.
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Workshop 7.1:
Debugging common data quality issues.
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Workshop 7.2:
Analyzing data quality errors and inconsistencies.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common data quality challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data security and access control measures.
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Workshop 8.2:
Utilizing data encryption and masking techniques.
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Workshop 8.3:
Designing and building access control policies.
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Workshop 8.4:
Optimizing security for data protection.
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Workshop 8.5:
Best practices for data security.
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Workshop 9.1:
Integrating data governance and quality management with real-world applications.
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Workshop 9.2:
Utilizing data quality tools and APIs.
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Workshop 9.3:
Implementing data governance for specific business domains.
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Workshop 9.4:
Optimizing integration for data-driven insights.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle data privacy and ensure data integrity.
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Workshop 10.2:
Utilizing data anonymization and pseudonymization techniques.
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Workshop 10.3:
Designing and building data integrity checks.
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Workshop 10.4:
Optimizing data handling for privacy and accuracy.
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Workshop 10.5:
Best practices for data privacy.
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Workshop 11.1:
Exploring advanced data governance and quality management techniques (data lineage, metadata management).
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Workshop 11.2:
Utilizing data lineage for data traceability.
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Workshop 11.3:
Implementing metadata management for data discovery.
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Workshop 11.4:
Designing and building advanced data governance solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing data governance for financial services compliance.
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Workshop 12.2:
Utilizing data quality management for healthcare data.
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Workshop 12.3:
Implementing data governance for e-commerce customer data.
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Workshop 12.4:
Utilizing data quality for supply chain optimization.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data governance and quality management tools (Informatica, Collibra, Talend).
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Workshop 13.2:
Implementing data quality workflows with tools.
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Workshop 13.3:
Designing and building data governance dashboards.
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Workshop 13.4:
Optimizing tool usage for efficient implementation.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing data quality performance measurement.
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Workshop 14.2:
Utilizing data quality KPIs and metrics.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing measurement for data quality effectiveness.
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Workshop 14.5:
Best practices for performance measurement.
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Workshop 15.1:
Emerging trends in data governance and quality management.
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Workshop 15.2:
Utilizing AI for data quality automation.
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Workshop 15.3:
Implementing data governance in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
