Data Product Thinking
Couldn't load pickup availability
ISBN: 9789378546662
eISBN: 9789378540578
Authors: Andrei Zaichikov, Elizabeth Antoine
Rights: Worldwide
Edition: 2027
Pages: 260
Dimension: 7.5*9.25 Inches
Book Type: Paperback
- Description
- Table of Contents
- About the Authors
Data Product Thinking is transforming modern enterprise strategy by turning raw datasets into reliable, self-service assets that drive real business value. It bridges technical data management and strategic business execution, giving you a clear blueprint for building, governing, and scaling high-impact data products.
This book provides a complete roadmap across four core phases. You will start by mastering data governance, business impact, and product characteristics across transactional, analytical, document, and third-party environments. Next, it covers key quality properties—existence, accuracy, completeness, timeliness, trustworthiness, and usability—while managing the lifecycle from creation to decommissioning. You will examine consumption formats for structured data, semi-structured documents, media, and AI models, alongside privilege management, backward compatibility, data culture, data economics, and agile adoption across governance, domains, and individual products. Finally, you will unlock data products for AI models, generative AI, quantum computing, low-code platforms, and autonomous networks.
By the end of this book, you will have the skills to design, govern, and deploy scalable data products, empowering you to lead enterprise data transformation.
WHAT YOU WILL LEARN
● Implement enterprise data governance frameworks.
● Treat raw enterprise datasets as actionable assets.
● Map decentralized data products across transactional, analytical, and third-party systems.
● Enforce quality dimensions including existence, accuracy, timeliness, and data trustworthiness.
● Drive enterprise transformation using agile frameworks across domains and products.
WHO THIS BOOK IS FOR
This book is for technology executives, enterprise and data architects, data engineers, product owners, and data governance leaders in large, complex organizations. Some familiarity with enterprise data governance and concepts such as data mesh or data fabric is helpful for students but not required.
1. Introduction to Data Products and Data Governance
2. Impact of Data Products
3. Characteristics of Data Products
4. Types of Data Products
5. Ecosystem of Data Products
6. Data Products Lifecycle
7. Consumption of Data Products
8. Managing Data Products
9. Transforming Enterprises to Embrace Data Products
10. Adoption of Data Products in Enterprise
11. Data Products for AI
12. Future of Data Products
Andrei Zaichikov is a technology leader and enterprise data architecture expert with extensive experience designing and delivering large-scale data and cloud solutions. Throughout his career, he has contributed to some of the industry's most ambitious IT transformation initiatives, helping organizations modernize their data platforms and adopt scalable, data-driven architectures. Andrei's professional interests span data engineering, cloud computing, distributed systems, artificial intelligence, and enterprise architecture. He is an active writer and speaker, regularly sharing practical insights on modern topics of data and artificial intelligence. Drawing on years of hands-on experience solving complex engineering challenges, Andrei combines strategic thinking with deep technical expertise to help organizations turn data into a competitive advantage. His work emphasizes pragmatic design, continuous improvement, and the effective application of emerging technologies to real-world business problems.
Elizabeth Antoine is a data and AI executive and non-executive director based in Australia. Over 17 years, including senior roles at Microsoft and in the global resources sector she has helped large, complex organisations turn data and AI into trusted strategic capabilities: from foundational platforms and enterprise knowledge systems to the governance, operating models, and controls needed to scale AI safely. She brings a board-level lens to questions too often treated as purely technical: how organisations make better decisions, manage risk, and create value as data, AI, and agentic systems become embedded in the way work gets done. A keynote speaker at industry events including the Telstra AI roadshow and WiDS Stanford, Liz holds an Executive MBA (AGSM, UNSW), a Master by Research, and is a graduate of the AICD Company Directors Course. She writes for leaders navigating the shift from data as a by-product of operations to data as a governed product and strategic asset.