Description
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 Prod
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.
Jessika Milhomem is a seasoned expert with over 18 years of experience in Data Product Management, Data Governance, Analytics, Data Engineering, and Machine Learning Engineering. She has led strategic initiatives across a wide range of industries including digital banking (Nubank, PagSeguro), fashion, telecommunications, education, automotive, and consumer goods delivering impactful, data-driven solutions.
Jessika holds a degree in Databases, a postgraduate specialization in Business Intelligence with an emphasis in Enterprise Performance Management from FIAP, and an MBA in Big Data Analytics from FIA. Her passion lies in bridging business needs with the power of data and collaborative team dynamics to drive innovation and long-term value.
As a leader, Jessika empowers teams to both consume and produce data products, championing a data as a product mindset that strengthens decision-making and fuels customer-centric innovation.
You can learn about her work at https://jessikamilhomem.com.
