Data Governance for AI-Enabled Organizations
Course 1292
3 DAY COURSE

Data is a strategic organizational asset, but its value depends on how effectively it is collected, managed, governed, protected, and used. This course introduces the foundations of data governance and data management while helping you understand how to assess and improve your organization’s data maturity.

You will explore how organizations create business value from data, establish governance responsibilities, manage modern data platforms and cloud pipelines, apply governance to machine learning and generative AI, and address the challenges of sovereign AI. Through practical examples and a continuing case study, you will learn how effective governance supports reliable data, informed decisions, organizational objectives, and responsible innovation.

Data Governance for AI-Enabled Organizations Benefits

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

  • Explain where organizational data comes from and why data volumes continue to increase
  • Distinguish data, information, knowledge, and insight
  • Explain why organizations collect data and treat it as a strategic asset
  • Recognize how data quality affects business performance, automation, and decision-making
  • Identify common causes of poor-quality data and data-related failures
  • Evaluate an organization’s current data maturity level
  • Identify key standards and practices for organizational data governance
  • Explain why organizations collect, manage, share, and protect data
  • Recognize the principles, roles, and components of a data governance program
  • Understand the fundamentals of modern data platforms and data management
  • Apply governance principles to organizational data stores and cloud data pipelines
  • Recognize governance considerations for machine learning and generative AI
  • Explain the emerging importance of sovereign AI
  • Use metrics to monitor and improve data management and governance practices

• Training Prerequisites

  No previous data governance or data management experience is required. Familiarity with basic organizational processes, data use, or technology concepts is helpful but not necessary.

  The revised course is positioned as an introductory course and does not identify a required technical prerequisite.

• Certification Information

  Learning Tree End of Course Exam included

• Who Should Attend

  This course is designed for professionals who participate in, oversee, or are affected by the collection, management, governance, and use of organizational data. It is appropriate for business and technology professionals who need a practical understanding of data governance, data maturity, data quality, cloud data practices, and responsible AI governance.

  Suggested roles include:

  • Data Governance Professionals
  • Data Owners and Data Stewards
  • Business Analysts
  • Data Analysts
  • Product Managers
  • Project and Program Managers
  • Information and Records Management Professionals
  • Risk and Compliance Professionals
  • Privacy and Security Professionals
  • Data Architects and Data Engineers
  • AI and Machine Learning Professionals
  • Technology and Business Leaders

Data Governance for AI-Enabled Organizations Training Outline

Chapter 1: Understanding Business Value Through Data

Explores how organizations turn raw data into information, knowledge, insight, and measurable business value. Participants examine why organizations collect data, how data supports better decisions and operational improvement, and why data should be treated as a strategic asset. The chapter also introduces data quality challenges, common causes of data failure, and a practical case study used throughout the course.

Chapter 2: Data Governance Fundamentals

Introduces the purpose, principles, standards, roles, responsibilities, and organizational components of data governance.

Chapter 3: Data Platforms

Examines the technology platforms used to collect, store, integrate, process, and make organizational data available.

Chapter 4: Data Management

Introduces the practices used to manage data throughout its lifecycle, including quality, metadata, architecture, access, storage, and maintenance.

Chapter 5: Governance and Data Stores

Explores how governance policies and controls apply to databases, data warehouses, data lakes, and other organizational data stores.

Chapter 6: Governance of Cloud Pipelines

Examines governance across cloud-based data movement and processing, including accountability, quality, security, lineage, and operational controls.

Chapter 7: Machine Learning and Generative AI

Introduces governance considerations for machine learning and generative AI, including data quality, bias, transparency, trust, oversight, and responsible use.

Chapter 8: Sovereign AI

Explores sovereign AI and the organizational, legal, geographic, infrastructure, security, and data-control considerations associated with AI systems.

Chapter 9: Course Summary

Review the relationship between business value, data management, governance, cloud platforms, and artificial intelligence

• Reinforce the role of data quality, accountability, standards, and measurement
• Identify practical next steps for evaluating and improving organizational data maturity