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.