Data Quality ROI: A Playbook for Business-Driven Data Quality, by Gaurav Patole
Turn data quality into a business advantage that delivers measurable results.
What is data quality?
Types of data quality checks
Data quality-related terminologies:
Why data quality matters
The perception problem
So, why does data quality matter to the business?
Who is “the business” in data quality?
Law of Data Inaction
Business stakeholders are human, too!
How does data quality impact business success?
Why inaction feels easier than action
The Business Awakening Curve
Federated or fragmented governance?
The Law of Unified Data Governance Force
The Core-Hub Approach
How to avoid data quality becoming a silo?
Why data quality acceleration remains low
Achieving meaningful acceleration with the fit-for-purpose mindset
Acceleration must start at the top
Who should own data quality?
Data quality ownership often fails due to disconnect
Data quality is always contextual
Law of action versus reaction
Why doesn’t the business get it?
The juggernaut of data quality dashboards
Similar missteps with data quality dimensions
Why data quality needs its own storytelling
From failures to business readiness scores
Cataloging your business metadata is critical
Business accountability for data quality
What gives data “mass”?
Embedding DQ into everyday tasks
Conclusion
Educate, don’t dictate!
Get everyone in a room!
Catch the no. 2’s in the value chain!
Actively seek and act on feedback
Invest in PR!
Data quality for AI
AI for data quality
Can gamification drive data quality engagement?
Why tools come second
Strategic approach to tool selection
Poor data quality delays transformation, undermines analytics, and erodes trust in business decisions. Data Quality ROI shows business leaders, data professionals, and executives how to reframe data quality as a strategic asset that fuels revenue growth, risk reduction, and customer satisfaction.
Drawing on years of leading data governance and quality programs, author Gaurav Patole introduces a clear, Newton-inspired framework for understanding how data flows, breaks, and improves inside organizations. Through relatable stories and four simple laws, he explains why data quality programs often stall and how to build lasting change.
This book goes beyond cleansing and dashboards to focus on ownership, communication, and culture. Readers will discover how to overcome organizational inaction, break down governance silos, and make data quality everyone’s responsibility. Each chapter connects practical steps to real business outcomes, ensuring data quality conversations resonate across both business and technology teams.
This book delivers a rare combination of deep expertise and practical frameworks that any organization—regardless of maturity—can use to take control of its data, to adopt a Fit-for-Purpose mindset focused exclusively on the data that drives decisions, revenue, and risk mitigation.
Patrick Attallah, Chief Data Officer at NXP Semiconductors
The core idea in this book—that data quality is fundamentally a communication challenge—is a powerful one. It provides a useful framework for data teams who are struggling to translate their efforts into a language that resonates with business stakeholders and fosters a culture of shared data accountability.
Dr. Sebastian Wernicke, Bestselling Author of ‘Data Inspired’, Partner- Oxera Consulting
Having worked alongside Gaurav through some of the most demanding data transformation programs, I’ve seen his brilliance in action, leading and delivering enterprise-scale data governance and quality initiatives with a level of impact that’s truly unprecedented. This book reflects that same rare mix of vision, depth, and execution.
Subeer Sehgal, Head, AI & Data Governance Strategy at Fractal
This book that you hold in your hands is a work of leadership that will create clarity and generate energy for data quality as an organization-wide team sport.
Karthik Ravindran, GM, Worldwide Data Platforms, Microsoft Corporation
Gaurav Patole is a seasoned Data Governance and Data Quality professional who champions a simple yet powerful philosophy: data problems should make sense to everyone. With a decade of experience at companies including Infosys, Cognizant, BCG, and Thoughtworks, he specializes in translating complex data governance and data quality concepts into practical and understandable solutions. Gaurav holds an engineering degree in IT and a Master’s in Management Studies, and his conference presentations on making governance and quality relevant to business audiences are well-regarded within the community. Mentored by senior leaders across the industry, he has led cross-functional programs, advised organisations on data strategy and tooling, and regularly publishes practical guidance through articles and social posts. This book is his practical playbook for making data quality a measurable business asset.
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