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Data as an Asset
A Data Management Strategy for Executives
Passage 1 — Chapter 8, "Data Quality: Can the Board Trust Its Own Numbers": what bad data actually costs a leader
Business folklore has long carried a caustic image: a machine that hands back garbage on the way out once it has been fed garbage on the way in — the old saying that poor input inevitably produces poor output, which this book already invoked earlier to explain why data quality went neglected for so long. It is worth taking that image seriously here and translating it into terms a leader can act on: exactly how much the input garbage costs a company, and by what test to recognize the moment a figure on a board slide becomes too risky to trust.
Passage 2 — Chapter 9, "Analytics for Business: Data Warehousing and Metadata": a scene that stays with the reader
Picture a board meeting: a single figure sits on the screen — quarterly revenue, say, or the share of customers who defected to a competitor — and one of the directors simply asks where that number came from. The pause that falls over the room while someone runs off to fetch an explanation from whoever put the report together says more about the state of trust in a company's analytics than any presentation ever could.
Passage 3 — Chapter 11, "Data Management Maturity Assessment": maturity as something measured, not graded
The practical conclusion for a leader is this: a maturity rung is not a verdict of "good" or "bad" — it measures how controlled and predictable a company's data work actually is. The higher the rung, the fewer surprises, and the more accurately a leader can forecast the consequences of decisions built on that data.
Passage 4 — Chapter 13, "Managing Organizational Change in Data Management": the book's closing argument
Taken together, the material across all thirteen chapters gives a leader a coherent view of data — from understanding it as a business asset to a concrete toolkit for leading organizational change. The architectural choices, technological infrastructure, and quality-control procedures examined earlier remain unrealized potential until someone at the most senior level of management personally takes on the work of carrying people through resistance and turning that potential into the company's everyday working habit.
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About
About the Book
Overview
Executives make data-driven decisions every day, yet they rarely have the vocabulary to ask their technical team the right question about that data: whether to approve the budget for a data warehouse, whether a number on a board slide can be trusted, or who should be made accountable for a particular body of information. This book closes exactly that gap — it presents data management as a full business function that company leadership owns, not something delegated entirely to the IT department.
The book's approach draws on the systematized practices and recommendations of DAMA International, the international professional association, translated into the language of management decisions, risk, and budget — without technical jargon and without descending into code or specific platforms. Each chapter gives an executive not just an understanding of the topic but a concrete toolkit: checklists to run through before a budget decision, questions to put to a team before launching an initiative, and criteria for telling a well-founded investment from an expensive indulgence.
What the Book Covers
The book is organized into 3 parts and 13 chapters.
Part I. Data Management as a Business Function (Chapters 1–3) — why data is a business asset rather than a technical expense, which ethical boundaries deserve attention, and how Data Governance, the coordinating structure that aligns the rest of the function, actually works.
Part II. Technology Infrastructure Through an Executive's Eyes (Chapters 4–9) — data architecture, security, storage and integration, reference and master data, data quality, data warehousing and business intelligence: the vocabulary and the checklist of questions needed to talk with a technical team without descending into implementation.
Part III. Data as a Competitive Advantage (Chapters 10–13) — big data and data science, assessing an organization's data management maturity, organizational structure and roles, and the change management without which no technology investment ever really takes hold.
Who This Book Is For
CEOs, COOs, CFOs, CROs/CCOs, business-unit and product leaders, current and aspiring chief data officers, and HR and transformation leaders. No technical background is required — the book deliberately stays at the management level rather than technical implementation, which is the subject of a separate, companion book written for data architects and engineers.
What Sets This Book Apart
- Executive language without diluting the substance. Every chapter gives a leader the vocabulary needed to talk with a technical team — concrete questions, evaluation criteria, checklists — rather than a retelling of technical documentation or a vendor's marketing promises.
- Consistent terminology throughout. All thirteen chapters are built on a single, shared set of concepts, and the book closes with a glossary that consolidates them: each term, defined once, is then used consistently, without drifting between chapters.
- Practical tools in every chapter. Prerequisite checklists ahead of a budget decision, a list of questions to put to a team before launching an initiative, tables of investment-evaluation criteria — a leader comes away not just understanding the topic but holding a tool ready to use the very next day.
Author
About the Author
Andrii Bogdanovych
Andrii BOGDANOVYCH combines senior public-service management experience with engineering expertise in artificial intelligence — a combination rare in the Ukrainian market, and one that directly shapes this book's approach: discussing AI governance in language equally accessible to public-sector officials, corporate boards, and technical teams.
Deputy Head for Digital Development, Digital Transformation, and Digitalization (CDTO) of the State Energy Supervision Inspectorate of Ukraine (since 2022); he previously held the equivalent position at the State Ecological Inspectorate of Ukraine, and the position of Deputy Head of the Kherson Regional State Administration — in both roles leading digitalization, cybersecurity, and critical-infrastructure protection efforts, respectively at the level of a central executive authority and at the regional level.
He is the author of four training programs: "AI Management and Governance in the Organization: NIST AI RMF 1.0 and ISO/IEC 42001," data governance for executives, data governance for technical practitioners, and building organizational cybersecurity under NIST CSF 2.0 and ISO/IEC 27001.
A practicing Python/AI developer, he designs RAG systems and autonomous agentic solutions built on LLM APIs, publishes and maintains open-source libraries on PyPI, and administers his own server infrastructure. He holds a Master's degree in Public Administration (Taras Shevchenko National University of Kyiv), a Master's degree in Law (Academy of Advocacy of Ukraine), and a Bachelor's degree in Computer Science (Vadym Hetman Kyiv National Economic University).
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