Data Product Management in the AI Age

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 LearnExplore the evolution of data architecture and strategiesUnderstand the fundamentals of data product managementDifferentiate between Data Product and Data as a ProductKnow what the Golden Data Platform is and how to use itUtilize the Data Product Management Canvas effectivelyReorient your data strategy with product management principlesExamine the concepts of products and data architecture evolution in relation to leadershipEvaluate how to solve data architecture without bias by technologyDesign and implement Data as a Product and Data Products through a projectMaintain and enhance Data Products once launchedWho This Book is forData leaders and managers responsible for designing and delivering data products, as well as product managers who want to collaborate more effectively with data teams. Analytics engineers, data engineers, data scientists, and machine learning engineers will find practical guidance for building impactful data solutions. Developers and data professionals aiming to move into leadership or product roles will also find valuable insights throughout.