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Managing Data as a Product

You're reading from   Managing Data as a Product Design and build data-product-centered socio-technical architectures

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835468531
Length 368 pages
Edition 1st Edition
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Author (1):
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Andrea Gioia Andrea Gioia
Author Profile Icon Andrea Gioia
Andrea Gioia
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Data Products and the Power of Modular Architectures
2. Chapter 1: From Data as a Byproduct to Data as a Product FREE CHAPTER 3. Chapter 2: Data Products 4. Chapter 3: Data Product-Centered Architectures 5. Part 2: Managing the Data Product Lifecycle
6. Chapter 4: Identifying Data Products and Prioritizing Developments 7. Chapter 5: Designing and Implementing Data Products 8. Chapter 6: Operating Data Products in Production 9. Chapter 7: Automating Data Product Lifecycle Management 10. Part 3: Designing a Successful Data Product Strategy
11. Chapter 8: Moving through the Adoption Journey 12. Chapter 9: Team Topologies and Data Ownership at Scale 13. Chapter 10: Distributed Data Modeling 14. Chapter 11: Building an AI-Ready Information Architecture 15. Chapter 12: Bringing It All Together 16. Index 17. Other Books You May Enjoy

Delving into the bootstrap phase

The bootstrap phase aims to lay the foundation for the adoption journey. In this section, we will see what the main activities that characterize this phase from both an organizational and operational point of view are.

Setting the foundation

The bootstrap phase begins by establishing organizational structures necessary to execute the operating model defined during the assessment phase. The operating model dictates decisions about activities throughout the bootstrap phase and beyond, guiding prioritization, budgeting, and the ongoing monitoring of identified tasks. Consequently, it is crucial to reorganize roles and management responsibilities within the data management function (System 2-5) to ensure alignment with the chosen operating model before moving forward.

As discussed in Chapter 3, Data Product-Centered Architectures, the data management function to adopt the data as a product paradigm needs to implement the following four key capabilities...

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