Executive Data Quality Playbook - by Sheik Isaacs (Paperback)

Book Synopsis

IBM research reveals that poor data quality costs the U.S. economy $3.1 trillion annually. Yet for most executives, this is an invisible problem--data looks reasonable, dashboards are polished, and trends appear logical. Until a quarterly projection is built on corrupted revenue data, a $50 million acquisition is approved on overstated retention numbers, or a client relationship collapses because portfolio performance figures were systematically wrong for three months.

The Executive Data Quality Playbook solves this problem at its source: the executive level. Unlike IT-focused data quality guides that address schema validation and field-level cleansing, this book operates entirely from the C-suite perspective. Every chapter is designed to answer the question that actually matters to leaders: "Can I trust this data to make a decision that will define my organization's future?" The book introduces the Decision-Grade Data Classification System--a four-tier framework that matches data quality requirements to decision stakes, from Operational decisions (85% confidence threshold) to Bet-the-Company decisions (99.5%+ confidence threshold). This framework becomes the organizational standard for knowing which data to trust, when to trust it, and what to do when that trust breaks down.

Executives receive the 24-Hour Data Quality Emergency Protocol, Executive Data Quality Dashboard templates, RACI accountability matrices, stakeholder trust-rebuilding roadmaps, vendor assessment frameworks, and technology ROI calculators. Each tool is field-tested--developed through 23+ years of managing high-stakes data quality at scale in one of the world's most complex financial institutions.

The book targets a gap that no existing publication fills: practical, executive-level data quality management that requires no technical background, delivers immediate results, and builds sustainable organizational capability. For any leader who has ever made a strategic decision and quietly wondered if the data was actually telling the truth, this book is the system they have been missing.

What You Will Learn:

How to assess, contain, and communicate any data quality crisis using a proven 24-Hour Emergency Protocol -- turning a potential career-defining disaster into a managed, recoverable situation.

How to build an Executive Data Quality Dashboard that delivers early-warning signals before bad data corrupts a strategic decision -- giving you real-time visibility into the trustworthiness of your most critical information.

How to classify data by decision-grade confidence requirements -- applying the right quality standard to the right decision, so quality-control effort lands where the stakes are highest instead of being spread thin across everything.

How to protect the integrity of the KPIs your board, investors, and leadership team rely on -- so the metrics that tell your company's story can withstand hard scrutiny.

How to future-proof your organization against emerging risks like AI-generated data and automation errors -- keeping your decisions trustworthy as your tools and business evolve.

Who This Book is for:

This book is for leaders who make decisions on data they can't personally verify and want to trust it with their eyes open -- no technical background required. It speaks directly to C-Suite Executives (CEOs, CFOs, COOs, Board and Audit Committee Members, Senior Managers and VPs, Finance, Risk, and Compliance Leaders and Data Governance and Quality Professionals.

From the Back Cover

Executives don't run on data. They run on trust in data -- trust that the revenue figure is real, that the retention number holds, that the dashboard reflects what's actually happening. Yet most leaders are effectively flying blind, relying on numbers they have no practical way to question, produced by processes they never see. The Executive Data Quality Playbook closes that gap where it starts: at the leadership level. Most data quality books are written for the people who clean the data; this one is written from your chair, and every chapter answers the only question that matters to a decision-maker -- can I trust this number enough to bet on it?

At its core is the Decision-Grade Data Classification System, a simple four-tier framework that matches how clean your data needs to be to how much is riding on the decision, from routine operational calls to bet-the-company moves. From there the playbook moves through the three stages every leader recognizes: responding when a key metric turns out to be wrong, putting durable controls around the numbers that matter most, and turning trustworthy data into a lasting advantage -- from rebuilding stakeholder trust to vetting vendor data to future-proofing against emerging risks like AI-generated data and automation errors. Every chapter follows the same rhythm: a real-world scenario, a plain-English framework, and ready-to-use checklists and templates.

Every tool is field-tested, developed over more than two decades managing high-stakes data quality inside one of the world's most complex financial institutions. No technical background is required, and no existing book fills this gap: practical, executive-level data quality you can act on without an engineering degree. By the last page, you'll understand what happens behind your numbers, know where they tend to break, and be able to challenge any figure with genuine confidence -- because you've tested it, not because you had no choice.

What You Will Learn:

• How to assess, contain, and communicate any data quality crisis using a proven 24-Hour Emergency Protocol -- turning a potential career-defining disaster into a managed, recoverable situation.

• How to build an Executive Data Quality Dashboard that delivers early-warning signals before bad data corrupts a strategic decision -- giving you real-time visibility into the trustworthiness of your most critical information.

• How to classify data by decision-grade confidence requirements -- applying the right quality standard to the right decision, so quality-control effort lands where the stakes are highest instead of being spread thin across everything.

• How to protect the integrity of the KPIs your board, investors, and leadership team rely on -- so the metrics that tell your company's story can withstand hard scrutiny.

• How to future-proof your organization against emerging risks like AI-generated data and automation errors -- keeping your decisions trustworthy as your tools and business evolve.

About the Author

Dr. Sheik M. Isaacs is an enterprise data quality and AI governance executive with 23+ years of leadership in regulated financial services. His data quality experience spans three distinct enterprise roles at a global financial institution.

As Director of Data Governance & AI Transformation, he led the design and deployment of data quality controls across the enterprise--embedding governance standards into high-impact business processes and mitigating data risk at scale. As a Data Tracing Quality Manager, he directed end-to-end data quality operations including data profiling, source comparison, and quality assessment across critical management and regulatory reports. And as a Data Analytics Manager in Internal Audit, he led the Business Intelligence and Analytics team to build enterprise risk-monitoring dashboards, producing data quality reporting delivered directly to board audit committees and senior leadership.

Complementing his corporate leadership with graduate-level teaching and curriculum design, Dr. Isaacs brings his deep practitioner expertise to graduate classrooms--designing and teaching courses in AI Ethics & Privacy, Data Analytics, Data Governance, and AI-Optimized Business Operations. An expert in agentic AI architectures and advanced prompt engineering, he actively deploys frontier AI platforms to drive measurable productivity gains across complex governance workflows.

He holds a Doctor of Business Administration from Walden University, five CIMP certifications spanning Data Quality, Data Governance, Data Modeling, Metadata Management, and Master Data Management, a Lean Six Sigma Black Belt, and a Certified Scrum Master (CSM) and Certified Product Owner (CPO) designation.