Making Data Governance Work, by Yvette M Desmarais
Unlock the power of practical, risk-aware, and value-driven data governance to transform your organization’s data chaos into strategic clarity.
Business Value
Business Priorities
Data Privacy and Sensitivity
External Presentation
Complexity
Data Quality Issue Volume
Lack of Knowledge
Purchased Data
Shared Data
AI/ML Use
Organization-Specific Risks and Opportunities
Data Stewardship
Master Data Management
Data Quality
Data Cataloging and Metadata
Data Privacy
Entitlements and Access
Privacy Data Classification
Data Products
Other Considerations
People
Time and Complexity
Project Variety
Recommended Approach
Data Governance Steering Committee
Data Governance Council
Data Governance Office
Data Stewardship Teams
Other Roles
Approach
Data Profiling
Data Quality Standards
Data Quality Improvement
Monitoring Data Quality Improvements
Evaluate Results and Select Data Quality Targets
Discover Data
Describe Data
Manage Data
Democratizing Data
Business Metadata
Technical Metadata
Governance Metadata
Process Metadata – Data Lineage
Process Metadata – Data Observability
Data Synchronization
Key MDM Capabilities
Planning for MDM Implementation
Types of AI/ML Models
Governing AI/ML
Inventory AI/ML Models within the Organization
Risk Management for Artificial Intelligence
Cataloging AI/Models
Challenges of Governing AI/ML
Measuring Data Quality for AI/ML
Data Governance and Artificial Intelligence
Data Access Risks
Sensitive Data Discovery
Data Access Control Models
Data Anonymization
Data Access Monitoring and Reporting
Changing Course
Next Steps
Making Data Governance Work is your hands-on guide to mastering the complexities of modern data environments. Whether you’re launching a new data governance initiative or reviving one that’s lost steam, this book equips you with actionable strategies to prioritize, organize, and implement effective governance programs that actually stick. Designed for professionals from diverse backgrounds—project managers, data analysts, compliance officers, engineers—this book meets you where you are and helps you turn theory into measurable impact.
You’ll learn how to build a data governance roadmap grounded in risk assessment, business value, and organizational priorities. Discover how to analyze your data environment, identify your highest-risk data sets, and map them to the governance functions most urgently needed. If you’ve ever asked, “Where do we even start with data governance?”, this book has the answer—backed by proven frameworks, templates, and real-world advice.
Inside, you’ll find comprehensive coverage of all the major data governance functions—data quality, data stewardship, metadata management, master data management (MDM), regulatory compliance, privacy, entitlements, AI/ML governance, and more. You’ll also gain insight into how governance roles are structured across Data Governance Offices, Steering Committees, and Stewardship Teams, helping you clarify responsibilities and build collaboration across your enterprise.
This is not another theoretical overview. It’s a tactical toolkit for real-world implementation. Through carefully crafted checklists and matrices, you’ll learn how to evaluate systems, inventory data sets, classify risks and sensitivities (e.g., PII, PHI, GDPR, HIPAA), and assign the right governance practices to the right places—without boiling the ocean or reinventing the wheel.
For organizations adopting emerging technologies, this book also dives deep into data governance for artificial intelligence and machine learning. You’ll learn how to catalog and monitor AI/ML models, manage training data risk, and maintain transparency in automated decision-making—all while aligning with current regulations like CCPA, CPRA, and FERPA.
With dedicated chapters on data observability, privacy classification, data lineage, and access control, this guide helps you build a sustainable governance program that adapts as your business evolves. You’ll also get expert guidance on handling shared data, purchased data, and complex, integrated data repositories, so you’re prepared for today’s hybrid, multi-source data realities.
One of the greatest strengths of this book is its prioritization matrix approach, helping you focus on high-value, high-risk areas while leveraging existing assets. Instead of starting from scratch, you’ll learn how to build on what your organization already knows and owns—saving time, reducing duplication, and driving adoption.
Whether you’re in healthcare, finance, tech, education, or government, Making Data Governance Work gives you the tools to succeed. It acknowledges the frustration, the scope creep, and the political navigation—and still delivers a clear path forward, rooted in experience and practicality.
If you’re looking for a field guide to enterprise data governance—a resource filled with insights on data catalogs, business glossaries, data standards, and building a data-driven culture—this book will become your go-to reference for years to come.
Yvette M. Desmarais is a seasoned data professional with a 40-year career spanning diverse roles across the data landscape. She has worked with organizations such as CVS Health, Hewlett-Packard, Quest Diagnostics, and State Street Corporation, bringing deep expertise in management accounting, data repository implementations, project and program management, business analysis, and reporting. For the past decade, she has focused on data governance, championing best practices and practical solutions to help organizations unlock the true value of their data. Making Data Governance Work draws on her rich experience to provide real-world insights into building effective, sustainable data governance programs.
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