Full Description
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 Products once launched
Who This Book is for
Data 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.
Contents
Part I: Fundamentals for Data Product Management.- Chapter 1: Data Product Management Introduction.- Chapter 2: Data Analytical Architecture - Traditional Architecture.- Chapter 3 : Data Analytical Architecture - Big Data Architecture.- Chapter 4: Consolidating the Analytics Journey Knowledge.- Part II: Data Product Management in Practice.- Chapter 5: Golden Data Platform to Manage Data as a Product.- Chapter 6: Data Product Management.- Chapter 7: Designing the Product of Data - Understanding Phase.- Chapter 8: Designing the Product of Data - Exploring Phase.- Chapter 9: Designing the Data Product - Materializing Phase.- Chapter 10: Ownership process - Recurrent Cycle - Ongoing.
