Data Governance Training – Effective Management of Data Quality and Security
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedData Governance Training – Effective Management of Data Quality and Security
The Data Governance training is an intensive 2–3 day workshop (80% practice, 20% theory) that prepares participants to effectively implement and maintain data governance programs in organizations, especially in the context of increasing use of artificial intelligence. You will learn comprehensive frameworks, roles, and processes of Data Governance that ensure data quality, security, compliance, and effective utilization for digital transformation, including training AI models. You will discover how to define data policies, monitor quality, manage access, and integrate governance practices with modern technologies, including cloud and AI solutions. Participants will become familiar with the latest legal regulations, such as the Artificial Intelligence Act and the Data Act, and learn how to design Data Governance systems that meet legal and ethical requirements in the context of AI and IoT.
Who is this training for?
Chief Data Officers and data management leaders, Data Stewards, and Data Owners across business domains-
Directors, managers, and specialists from companies offering IT services or data-based solutions, including Big Data and AI
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Data architects and compliance specialists
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Managers, specialists, and consultants in digital transformation, data analytics, AI, and Data Science
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Directors, managers, and specialists from IoT device manufacturers, vendors, and service providers handling IoT data
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Legal counsels, attorneys, and in-house lawyers supporting AI and digital transformation projects
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Data quality and information security specialists
What You Will Learn
- Define and implement effective Data Governance frameworks and strategies
- Build roles and organizational structures that support data management
- Achieve and maintain high data quality using tools and metrics
- Understand legal requirements and practices ensuring compliance and data security, with deep knowledge of regulations (GDPR, AI Act, Data Act) and their impact on AI and digitalization projects
- Master practical skills in managing the data lifecycle, intellectual property, data flows between entities, and protecting the interests of organizations and users
- Apply modern technologies for automating monitoring and data management
Training Program
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Day 1: Fundamentals of Data Governance and Legal Frameworks
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Module 1: Introduction to Data Governance
- Definition of Data Governance, goals, and business benefits
- Key components and pillars of Data Governance
- Roles and responsibilities:
- Chief Data Officer (CDO)
- Data Steward
- Data Owner
- Personal, non-personal, and machine data in governance
- Overview of legal regulations on data protection and data sharing
- The role of Data Governance in training AI models
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Module 2: Policies, Ethics, and Operational Models
- Creating data management policies and decision-making processes
- Data stewardship, incident handling, and conflict resolution
- Assessing organizational maturity
- Building Data Governance roadmaps
- Ethical principles and human rights in Data Governance
- Algorithmic discrimination and AI ethics violation examples
- Ethical guidelines and codes
- Methodologies for ensuring ethical and legal compliance
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Day 2: Data Quality, Compliance, New Regulations, and Intellectual Property
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Module 3: Data Quality Management
- Data quality standards and monitoring
- Incident alerting mechanisms
- Data quality improvement techniques:
- Profiling
- Validation
- Cleansing
- Automation of data quality processes
- Integration with data pipelines
- Data quality criteria for AI:
- Selection and adequacy
- Accuracy
- Representativeness
- Completeness
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Module 4: Compliance, Security, and New Legal Acts
- Overview of regulations:
- GDPR
- AI Act
- Data Act
- Compliance management and data security policies
- Personal data protection and privacy practices
- New obligations for IoT manufacturers and data-based services
- User rights: access, portability, and data sharing
- Data-sharing agreements and best practices
- Overview of regulations:
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Module 5: Copyrighted Data and Text & Data Mining
- Copyright protection of works
- Public domain and open licenses
- Text & Data Mining (TDM) exception for AI
- Legal risks related to copyrighted data acquisition
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Day 3: Technologies, Implementations, Non-personal Data, and Databases
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Module 6: Data Governance Technologies and Implementations
- Automated data discovery and classification tools
- Dashboards for data quality and governance monitoring
- Integration with analytics, cloud (multi-cloud), and AI platforms
- Implementation principles:
- Design
- Execution
- Maintenance
- Building data communities and skills
- Data literacy programs
- Change management and governance culture
- Case studies and group workshops on real datasets
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Module 7: Use, Exchange, and Protection of Data
- Processing personal data for AI:
- Legal bases
- Information duties
- Data minimization
- Data Protection Impact Assessments (DPIA)
- AI-based automated decision-making
- Database protection:
- Copyright
- Sui generis rights
- Open Database License
- Use and commercialization of non-personal and machine data
- Free B2B data flows, agreements, and market practices
- Processing personal data for AI:
