This module provides an in-depth overview of data governance, covering its importance, key concepts, and practical applications. Learners will explore the data lifecycle, frameworks, and the evolving role of AI in governance. The module also includes hands-on exercises to develop a governance charter and assess key performance indicators.

This module explores the key components of an operating model, including decision rights, roles, and governance structures. Learners will gain insight into how to design, implement, and measure effective data governance frameworks. It covers practical tools like RACI, governance scorecards, and implementation roadmaps.

This module explores the foundational elements of data governance, including how to define scope boundaries, create effective policies, and implement standards and controls. Learners will gain an understanding of traceability, risk acceptance, and how to map governance frameworks to real-world scenarios. Practical exercises will help apply these concepts in a structured and measurable way.

This module explores the essential concepts of metadata, catalogs, and business glossaries, focusing on their roles in data governance. Learners will gain an understanding of metadata lifecycle management, classification standards, and how to apply these tools to improve data quality and organizational alignment.

This module explores the key concepts of data quality and master data governance, including defining scope boundaries, measuring data quality dimensions, designing rules and SLAs, and managing data ownership and workflows. It also covers the remediation lifecycle, MDM, monitoring, and reporting practices. Learners will gain practical knowledge to implement effective data governance strategies.

This module covers essential concepts in data governance, including data classification, access control, compliance standards, and practical implementation of privacy and security measures. Learners will gain an understanding of how to manage data throughout its lifecycle and apply best practices for regulatory compliance.

This module explores the stages and control points of the data lifecycle, along with the fundamentals of data lineage. Learners will gain an understanding of how to capture, use, and integrate lineage information for effective data governance and auditing.

This module provides a comprehensive overview of AI governance, covering its scope, importance, core principles, key roles, and practical frameworks. Learners will gain an understanding of how to establish and maintain responsible AI systems through structured governance processes.

This module explores the principles of responsible AI, including defining its scope, identifying risks, and implementing ethical frameworks. Learners will gain practical skills in conducting impact assessments, documenting AI models, and maintaining transparency and oversight throughout the AI lifecycle.

This module explores the key phases and best practices involved in managing machine learning models throughout their lifecycle. Learners will gain an understanding of how to ensure reproducibility, traceability, and governance in ML workflows. The content also covers evaluation, monitoring, and the role of model registries in maintaining operational control.

This module explores the key considerations for governing generative AI systems, including data management, prompt and context control, output safety, logging practices, and incident handling. Learners will gain an understanding of how to implement effective governance frameworks and evaluate the ethical and security implications of AI systems.

This module explores the critical aspects of AI data quality, including scope boundaries, dataset representativeness, labeling governance, fairness checks, and monitoring strategies. Learners will gain practical insights into ensuring ethical, reliable, and robust AI systems throughout their lifecycle.

This module covers essential aspects of monitoring, incident management, and responsible operations in data governance. Learners will gain practical skills in defining scope, categorizing incidents, creating response plans, and ensuring audit readiness. It emphasizes strategies for continuous improvement and operational efficiency.

This module explores the design and implementation of tooling architectures for AI governance, covering scope boundaries, capability mapping, reference architectures, integration patterns, and decision frameworks for building or buying governance solutions. Learners will gain skills in aligning tooling with best practices and operationalizing governance strategies.

This module provides a concise summary of the key concepts covered throughout the course, reinforcing essential knowledge and reflecting on the learning journey. Learners will gain clarity on the main takeaways and be encouraged to appreciate their progress.