Introduction:

Without a robust Data Governance Framework, organizations struggle with data silos, inconsistent reporting, security risks, and regulatory penalties. A well-defined governance strategy provides clarity on data ownership, standardization, security controls, and compliance requirements.

This course introduces modern data governance methodologies that address evolving challenges such as hybrid cloud governance, AI/ML data governance, self-service analytics, and automation-driven governance frameworks. Participants will gain practical insights into establishing governance policies that support data-driven innovation, operational efficiency, and regulatory compliance.

Objectives:

By the end of this course, participants will be able to:

1. Understand the Core Principles of Data Governance – Define key governance components, policies, and roles.
2. Develop and Implement Data Governance Frameworks – Align governance structures with business and compliance requirements.
3. Establish Data Ownership and Stewardship Roles – Assign responsibilities for data quality, access, and compliance.
4. Ensure Regulatory Compliance – Align governance policies with GDPR, CCPA, HIPAA, ISO 27001, and other global standards.
5. Implement Data Quality and Metadata Management – Improve data integrity and lineage tracking.
6. Leverage Data Governance Tools and Technologies – Utilize platforms like Collibra, Alation, and Informatica for automation.
7. Build a Sustainable and Scalable Data Governance Strategy – Enable governance at scale with cloud, AI, and big data ecosystems.

Who Should Attend?

This course is designed for professionals responsible for implementing, managing, and enforcing data governance policies and compliance strategies, including:

• Chief Data Officers (CDOs) and Data Governance Leaders
• Data Stewards and Data Owners
• Data Architects and Engineers
• IT and Compliance Managers
• Business Intelligence (BI) and Analytics Professionals
• Risk and Regulatory Officers
• Enterprise Data Management Teams

Training Agenda:

Day 1: Fundamentals of Data Governance Frameworks

• Morning Session:

  • Introduction to Data Governance: Definitions, Objectives, and Business Value
  • Key Components of a Data Governance Framework
  • Common Challenges in Data Governance Implementation
  • Case Study: Data Governance Failures and Lessons Learned

• Afternoon Session:

  • Data Governance Models: Centralized, Federated, and Hybrid Approaches
  • Establishing Data Ownership, Roles, and Responsibilities
  • Hands-on Exercise: Defining a Governance Structure for an Organization

Day 2: Developing Data Governance Policies and Standards

• Morning Session:

  • Data Policies, Procedures, and Standards: Defining Business and IT Rules
  • Data Classification and Sensitivity Levels
  • Metadata Management and Data Lineage Tracking

• Afternoon Session:

  • Regulatory Compliance: GDPR, CCPA, HIPAA, SOX, ISO 27001
  • Data Access and Security Governance: Role-Based Access Control (RBAC), Encryption, and Data Masking
  • Hands-on Exercise: Creating a Data Policy and Access Control Matrix

Day 3: Data Quality Management and Governance Implementation

• Morning Session:

  • Data Quality Dimensions: Accuracy, Completeness, Consistency, Timeliness
  • Implementing Data Quality Metrics and KPIs
  • Data Governance in a Self-Service Analytics Environment

• Afternoon Session:

  • Data Governance Implementation Roadmap: Steps for Deployment
  • Change Management and Stakeholder Buy-in Strategies
  • Hands-on Exercise: Designing a Data Quality Monitoring Framework

Day 4: Tools, Technologies, and Automation in Data Governance

• Morning Session:

  • Data Governance Technology Landscape: Key Features and Capabilities
  • Implementing Governance with Data Cataloging and Metadata Management Tools (Collibra, Alation, Informatica)
  • Automating Data Governance Processes with AI and Machine Learning

• Afternoon Session:

  • Data Governance in Cloud and Hybrid Environments (AWS, Azure, Google Cloud)
  • Case Study: Implementing an Automated Data Governance Program
  • Hands-on Exercise: Configuring a Data Governance Tool for an Enterprise Scenario

Day 5: Scaling and Sustaining Data Governance Programs

• Morning Session:

  • Measuring Data Governance Success: KPIs, Scorecards, and Reporting
  • Continuous Improvement: Governance Maturity Models and Frameworks (DAMA-DMBOK, CMMI)
  • Creating a Governance Center of Excellence (CoE)

• Afternoon Session:

  • Emerging Trends in Data Governance: AI-Driven Governance, Decentralized Governance Models, and Blockchain for Data Auditing
  • Capstone Project: Developing a Data Governance Implementation Strategy
  • Course Wrap-Up, Q&A, and Certification of Completion