Data Governance Fundamentals for Practitioners and Leaders

Name: Data Governance Fundamentals for Practitioners and Leaders
Rating: 4.8 (52 reviews)

Leverage your Data without Risking your Business

Highest Rated

Created byJacob Bushong

Last updated 2/2026

English

English [Auto],

What you'll learn

Data Governance Strategies for Success
Data Governance Vocabulary
Data Governance processes and procedures
Data Classification
Data Retention
Date Protection
Date Privacy
Sustainable Data Governance
Define clear data ownership and accountability
Define governance operating models at scale
Manage data quality as a governance outcome
Govern access, use, and sharing
Align governance with security, privacy, and ethics
Support analytics, AI, and cloud platforms
Measure governance effectiveness

Course content

4 sections • 22 lectures • 2h 41m total length

Course Overview3:37
Discover how data governance works in real organizations, why ownership and usage matter, and how governance acts as a practical business capability.
Course Intro - How to use this course.3:45
Learn how data governance becomes a practical business capability that clarifies ownership, decision making, risk management, and trust across data use, access, quality, security, and lifecycle.
What is Data Governance, and Why Does it Matter?7:43
Data governance defines structures, roles, and decision rights to ensure data is used responsibly for business goals. Differentiate governance from data management and security, framing decisions as a people-centered process.
Quick Check-In0:45
The quick check-in invites learners to share feedback and leave reviews. Feedback helps improve the course and guides improvements to data governance fundamentals.

Business Drivers for Data Governance8:05
Explore the real forces driving data governance, trust, scale, and advanced analytics, through decision rights, ownership, and accountability that support risk decisions and align external expectations.
Core Principles8:08
Learn the core principles of data governance—accountability, clear decision rights, and transparency—and how to embed them in day-to-day operations with risk-based, lifecycle-aware, and consistency-minded practices.
Data Ownership and Stewardship7:16
Clarify data ownership and data stewardship roles to empower accountable decisions, align data with business objectives, and ensure governance through clear handoffs and collaboration.
Operating Models11:05
Explore centralized, federated, and hybrid governance operating models and how to choose based on size, culture, and complexity, with guardrails, cross-domain coordination, and clear escalation paths.
Roles, RACI, and Escalation Paths8:14
Define clear roles and decision rights in governance, apply RACI to key moments like data definitions and access, and design tiered escalation to prevent deadlocks.
Data Classification and Sensitivity Models8:04
Understand data classification as a risk-based decision framework guiding access and protection. Explore tiered sensitivity models, ownership, handling rules, and retention to reduce misclassification and enforce governance.
Data Lifecycle Management8:46
Define and govern the full data lifecycle from creation to retirement to prevent hidden risks and misaligned ownership. Ensure transformation, sharing, retention, and archival stay purposeful and accountable across time.
Quiz 1

Data Quality Management8:21
Reframe data quality as a governance outcome by defining ownership, standards, and accountability, and align monitoring and remediation with defined thresholds to prevent bad data upstream.
Policies and Standards9:27
Define and design data governance policies and standards that remove ambiguity, align ownership and accountability, and enable consistent decisions across classification, metadata, retention, and data sharing.
Access, Use, and Sharing8:24
enforce data access, use, and sharing controls as governance mechanisms balancing enablement with accountability. establish leased privilege and approval paths to prevent drift and align with ownership, lifecycle, and purpose.
Privacy, Ethics, and Regulations8:34
Align privacy, ethics, and regulations with data governance to treat privacy decisions as governance decisions, defining purpose, ownership, and responsible data use beyond legal compliance.
Data Governance and Information Security Alignment8:05
Align data governance and information security to define ownership, intent, and protections. Turn governance decisions into precise security controls, guided by data classification and lifecycle considerations.

Analytics, AI, and Cloud8:15
Explore how analytics, AI, and cloud amplify data governance needs, highlighting ownership, lineage, explainability, and risk-based controls to enable trusted, scalable insights.
Measuring Effectiveness7:25
Measure data governance effectiveness by shifting from activity to outcome metrics, capturing trust, risk management, and faster, better decisions.
Scaling and Sustaining a Program8:04
Scale and sustain data governance by shifting from centralized to federated models, embedding clear ownership, repeatable decision flows, and adaptive, outcome-focused practices that evolve with the organization.
Continuous Improvement8:30
Explore how data governance maturity measures decision quality and behavior, and learn to improve governance incrementally with clear ownership, decision paths, and continuous feedback.
Creating a program that lasts8:06
Sequence ownership, quality, classification, and access to design a survivable data governance program that endures change, treats governance as infrastructure, and remains useful under real-world conditions.
Final Quiz
Bringing it all Together3:13
Data governance clarifies ownership, classification, lifecycle, quality, and access to enable faster, more confident data use, guiding decisions that scale over time.

Requirements

No previous experience is required, although some understanding of IT concepts will help.

Description

Data governance is often misunderstood, overcomplicated, or treated as a compliance exercise. In reality, effective data governance is about how organizations make decisions about data—who owns it, how it’s used, how risk is managed, and how governance survives as the organization grows.

This course provides a practical, real-world understanding of data governance as a business capability, not a collection of tools or policies. Instead of focusing on abstract frameworks or vendor-specific solutions, you’ll learn how governance actually works inside organizations, why it often fails, and how to design governance that people rely on instead of work around.

Throughout the course, we explore data governance from multiple angles, including ownership and accountability, data lifecycle management, quality, access and sharing, privacy and ethics, security alignment, analytics, AI, and cloud platforms. Each topic is explained in the context of real organizational decision-making, with a focus on clarity, sustainability, and scale.

By the end of the course, you’ll understand how the individual components of data governance fit together into a cohesive system—and how to design and evolve governance programs that survive change, growth, and shifting priorities.

This course is ideal for professionals working with data, analytics, security, risk, compliance, or technology leadership who want a grounded, practical understanding of data governance without unnecessary theory or fluff.

Who this course is for:

Data governance practitioners, leaders, and their coworkers that work closely with DG Teams.

Data Governance Fundamentals for Practitioners and Leaders

What you'll learn

Explore related topics

Course content

Part 1 - Introduction to Data Governance4 lectures • 16min

Part 2 - Roles, Models, and the Data Lifecycle7 lectures • 1hr

Part 3 - Policies, Controls, and Alignment5 lectures • 43min

Platforms, Sustainability, and Continuous Improvement6 lectures • 44min

Requirements

Description

Who this course is for: