Making Data Governance Work

Unlock the power of practical, risk-aware, and value-driven data governance to transform your organization's data chaos into strategic clarity.

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.