Data Products, том 1: От проектов к продуктам: ответственность, управление и бизнес-ценность: От проектов к продуктам: ответственность, управление и бизнес-ценность: От проектов к продуктам:

Turn your data assets into data products people actually adopt, trust, and use, instead of building another “Ferrari data platform” that looks impressive but never leaves the garage. Most organizations don’t fail at data because of technology. They fail because no one truly owns the data, governance slows teams down, and “value” is never defined in business terms. From data as a byproduct to data as a product. Using a clear, business-first approach, you’ll learn: What a data product really is (and what it isn’t) What a data product really is (and what it isn’t) The full data product spectrum from clean, documented datasets to analytics, algorithmic, and AI-powered products The full data product spectrum from clean, documented datasets to analytics, algorithmic, and AI-powered products How to match ambition to organizational maturity instead of over-engineering too early How to match ambition to organizational maturity instead of over-engineering too early Organize for adoption, not just delivery. You’ll discover how to: Set up ownership models that actually work (business, technical, and product) Set up ownership models that actually work (business, technical, and product) Design team structures with or without data mesh Design team structures with or without data mesh Escape the backlog trap and reduce “shadow Excel” Escape the backlog trap and reduce “shadow Excel” Increase adoption and delivery speed without turning governance into a bottleneck Increase adoption and delivery speed without turning governance into a bottleneck Governance that enables, not constrains. A core focus of this book is making governance invisible yet effective. You’ll learn: How to apply federated governance with a “shift-left” mindset How to apply federated governance with a “shift-left” mindset Practical frameworks for data quality, standards, templates, and automated controls Practical frameworks for data quality, standards, templates, and automated controls How to use policy-as-code and data classification to build trust by design How to use policy-as-code and data classification to build trust by design Why governance becomes non-negotiable as AI raises the stakes around risk and accountability Why governance becomes non-negotiable as AI raises the stakes around risk and accountability Speak the language leaders care about. Finally, you’ll learn how to connect data product work to: Business value and ROI Business value and ROI Cost reduction and revenue growth Cost reduction and revenue growth Risk management and regulatory readiness Risk management and regulatory readiness You’ll also walk away with a concrete 90-day action plan to build traction fast. From projects to products, from tech-first to value-first. Practical, honest, and refreshingly grounded in impact, this is a playbook for teams who want data products people actually use. Winfried Adalbert Etzel, Data Governance Thinker, Writer, Host, Strategist, and Enthusiast “Data Products Volume 1” is exactly the book the data community has been waiting for and Amy Raygada is exactly the person to write it. This book doesn’t just give you an overview of data products and their concepts - it gives you truly practical guidance on how to actually get started, including a 90 day roadmap that turns ideas into action. As the first part of a trilogy, it sets a great foundation for what’s to come. This is a must-read for the thinkers AND the doers in the data community who want to stop talking about data products and start generating value with them. I’m so happy that others can now learn from Amy too! Tiankai Feng, Author of “Humanizing Data Strategy” & “Humanizing AI Strategy” All good things in the data space begin and end with customers. So too data products. Well done, Amy Raygada! Tom Redman, The Data Doc Based on concrete, real enterprise experience, Amy Raygada delivers a solid blueprint for building data products in the AI era. Ole Olesen-Bagneux, Author, VP & Chief Evangelist, PhD Amy Raygada delivers a practical, no-fluff guide that reframes “data work” into real products people actually adopt—grounded in user needs, clear ownership, and measurable business impact. Her frameworks make it easy to move from abstract strategy to day-to-day execution, without losing sight of governance, quality, and trust. The writing is sharp, relatable, and packed with lessons that will save teams from building “Ferraris nobody drives.” Jessica Talisman, Founder, Ontology Pipeline and Contextually LLC