Data Governance & AI Governance - The Complete Guide

Packt via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

16 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Master the core concepts of data governance and AI governance with practical, real-world applications. Understand key roles, frameworks, and strategies. Perfect for professionals looking to advance their governance skills in the digital age. In this course, you’ll explore the foundational principles of data and AI governance, focusing on key frameworks, governance structures, and the roles responsible for ensuring compliance. With practical applications tied to real-world case studies, you will gain essential tools to navigate the complex world of data management and AI ethics. Throughout the course, we will guide you through the lifecycle of data governance, covering everything from metadata management to security controls, privacy regulations, and AI integration. You will learn how to apply industry-recognized frameworks to effectively manage governance across a variety of sectors. This course is ideal for professionals in data management, security, and AI development. Prerequisites include basic familiarity with data systems and a desire to enhance your governance knowledge in today’s fast-paced technological landscape. This course is for IT professionals, data managers, AI practitioners, and anyone interested in mastering the principles of data and AI governance. Basic knowledge of data systems is helpful but not required. This course takes a hands-on approach, guiding learners through practical examples and exercises. With real-world case studies, you will apply governance principles to scenarios and exercises. By the end of the course, you will confidently manage data governance and AI governance processes. This course is based on Data Governance & AI Governance - The Complete Guide, by Dr. Florian Detzel. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Welcome
- This module introduces learners to the course structure, learning objectives, and the EasyCar case study, providing a foundation for the learning journey. It helps students understand the course flow and what to expect throughout the modules.
Foundations of Data Governance
- 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.
Operating Model & Roles (Decision Rights & RACI)
- 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.
Policies, Standards, and Controls
- 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.
Metadata, Catalog, and Business Glossary
- 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.
Data Quality & Master Data
- 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.
Privacy & Security
- 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.
Lifecycle and Lineage
- 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.
AI Governance Fundamentals
- 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.
Responsible AI
- 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.
Machine Learning 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.
GenAI & Large Language Models (LLM)
- 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.
AI Data Quality
- 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.
Monitoring, Incidents, and Responsible Operations
- 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.
Tooling Architecture
- 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.
Conclusion
- 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.