“An outstanding companion for any data professional seeking to deepen their expertise and grow in the field of Data Product Management.” (Eduardo Juremeira, Data Engineering Manager, Adyen)

“This book is a valuable resource for data professionals, offering clear concepts and practical tools.” (Luis Oliveira, Analytics Engineering Tech Manager, Nubank)

A practical guide to mastering data product management—bridging strategy, architecture, and execution for the AI era.

We have firmly entered the age of artificial intelligence (AI). Data, the backbone of this technology, is more crucial than ever. If the algorithm is the brain, data is the content that feeds it. This book argues that to succeed in this era, organizations should adopt a holistic approach to data—one that uses product management principles to inform how data is sourced, designed, managed, maintained, optimized, and leveraged.

This book is divided into two sections, moving from fundamentals to practical applications. In the first part, you’ll learn about the concepts that underlie product theory, the data architecture journey, and the essential knowledge needed to manage data products. The second part focuses on putting everything into practice, with particular attention to designing solutions, ongoing maintenance, and optimization.

Additionally, the book introduces the Golden Data Platform and the Data Product Management Canvas, important tools and frameworks coined by author Jessika Milhomem. These resources will help you begin transforming your organization’s data strategy, empowering you to stay ahead of the competition and thrive in the AI age.

What You Will Learn

• Explore the evolution of data architecture and strategies

• Understand the fundamentals of data product management

• Differentiate between Data Product and Data as a Product

• Know what the Golden Data Platform is and how to use it

• Utilize the Data Product Management Canvas effectively

• Reorient your data strategy with product management principles

• Examine the concepts of products and data architecture evolution in relation to leadership

• Evaluate how to solve data architecture without bias by technology

• Design and implement Data as a Product and Data Products through a project

•  Maintain and enhance Data P...