By Published On: May 30, 2026Categories:

Data Governance & AI Governance: Build Trusted Data and Production-Ready AI

This course shows how to design practical data governance and AI governance frameworks with clear roles, controls, quality standards, and ready-to-use templates. You will learn how to connect data ownership, privacy, metadata, and decision rights to real-world AI and analytics use cases.

  • Build a data governance operating model with clear roles and responsibilities
  • Create policies, standards, controls, and accountability frameworks
  • Govern AI, ML, and GenAI with practical risk and control thinking

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Governance

Operating Model

AI

Risk and Controls

Templates

Ready To Use Artifacts

Trusted data foundations

AI governance readiness

Operating model clarity

Templates you can adapt

What Our Students Say

Trusted by learners who want practical governance outcomes, not buzzwords.

Excellent course structure. The topics are very practical and useful for AI governance work.

– Willian

This course is vital to AI production. I’m ready to learn about Data Governance and AI Governance.

– Milagros

Essential for AI in production, great intro to data governance.

– Marc

Your 4-Step Governance Learning Journey

1. Frame The Problem

Understand the difference between governance ambitions and practical implementation needs.

2. Define Controls

Create policies, standards, and accountability structures that people can actually use.

3. Build Templates

Use practical templates to accelerate adoption across teams and projects.

4. Govern AI

Apply the same discipline to AI, ML, and GenAI initiatives.

In Short

This course teaches practical data governance and AI governance for organizations that want trusted data, clear accountability, compliant processes, and production-ready AI. You will learn how to design operating models, policies, controls, quality frameworks, ownership structures, and governance templates that can be adapted to real business environments.

Who This Is For

  • Data governance professionals and BI analysts
  • Analytics leads and data owners
  • Consultants and managers shaping governance programs
  • Anyone preparing data and AI initiatives for production

What You Will Learn

  • Data governance operating model design
  • Roles, responsibilities, and decision rights
  • Policies, standards, and controls
  • Metadata management, cataloging, and data quality
  • AI, ML, and GenAI governance fundamentals
  • Implementation templates and adoption artifacts

Data Governance vs AI Governance

Data governance defines how data is owned, documented, secured, measured, and used. AI governance defines how AI systems are designed, reviewed, monitored, and controlled. AI governance depends on strong data governance because AI systems need trusted, well-managed data.

Templates And Deliverables Included

The course includes practical templates and frameworks you can adapt for real governance work.

  • Governance operating model template
  • Roles and responsibilities structure
  • Policy and standards examples
  • Data quality framework
  • AI governance checklist
  • Adoption and communication artifacts

Perfect Companion Courses

Microsoft Purview

Apply catalog, glossary, and data-quality capabilities in a practical governance platform.

Copilot In Microsoft Fabric & Power BI

Put governed data into AI-assisted analytics and reporting workflows.

Frequently Asked Questions

Data governance defines how data is owned, documented, secured, measured, and used. AI governance defines how AI systems are designed, reviewed, monitored, and controlled. AI governance depends on strong data governance because AI systems need trusted, well-managed data.

No. A basic understanding of business data, reporting, analytics, or organizational processes is enough. The course focuses on practical governance design, not coding.

The course includes governance templates for operating models, roles and responsibilities, policies, controls, data quality, and AI governance implementation.

Yes. The course covers governance foundations that are important for generative AI, including accountability, data quality, privacy, access, controls, and responsible use.

The course is useful for data governance professionals, BI analysts, analytics leads, data owners, consultants, and managers who need practical governance structures for data and AI.

Ready To Build Trusted Data and AI Governance?

Start learning practical governance workflows for data, AI, and production readiness today.