Every leader signs off on data investments. Almost none were ever taught how to evaluate one. You approve the budget for a data warehouse. You sit through a board meeting where a director asks where a number came from, and no one in the room can answer with certainty. You're told to appoint someone "accountable for data" - but accountable for what, exactly, and measured how? Data as an Asset closes that gap. It is not a technical manual and it will not teach you SQL, cloud architecture, or a specific vendor's platform. It teaches something rarer: how to run data as a business function you personally own, with the same rigor you already bring to finance, legal, or operations. The book translates the systematized practices of the international data management profession into the language executives actually use - risk, budget, accountability, competitive advantage - stripped of engineering jargon. Every chapter closes with a concrete toolkit: a checklist to run before a budget decision, questions to put to a technical team before you approve a project, and criteria for telling a well-founded investment from an expensive indulgence. What's inside Thirteen chapters organized into three parts: Part I - Data as a Business Function. Why data belongs on the same balance sheet as any other asset, where the ethical boundaries of data use sit, and how data governance actually coordinates a function that otherwise fragments across departments. Part II - Technology Through an Executive's Eyes. Architecture, security, storage and integration, master data, data quality, and the analytics pipeline that turns raw numbers into a board-ready figure - enough vocabulary to ask a sharp question without pretending to be the engineer in the room. Part III - Data as Competitive Advantage. Big data and data science without the hype, how to honestly assess your own organization's data maturity, how to structure roles and accountability, and the organizational change work that decides whether any of the above actually survives contact with your company. Who this book is for
- CEOs, COOs, CFOs, CROs, and CCOs who sign off on data-related budgets and risk
- Business-unit and product leaders who depend on data they don't control
- Current and aspiring Chief Data Officers building the function from the top down
- HR and transformation leaders driving the change management data initiatives require
No technical background is assumed or required.
Why this book is different- Built for decisions, not definitions. Every chapter hands you a checklist, a set of questions, or evaluation criteria you can use the next morning - not a glossary of terms to memorize.
- One consistent vocabulary, cover to cover. Thirteen chapters share a single set of concepts, tied together by a consolidated glossary, so terminology never drifts between chapters the way it does in most management books.
- Written by someone who has sat on both sides of the table. The author pairs senior public-sector digital-transformation leadership with hands-on AI engineering experience - a combination that shaped a book equally legible to a board member and a data architect.
If you've ever approved a data project you couldn't fully evaluate, or hesitated before trusting a number on a slide, this book gives you the standing to ask the right question - and the authority to demand a real answer.
