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Data Management Strategy at Microsoft

You're reading from   Data Management Strategy at Microsoft Best practices from a tech giant's decade-long data transformation journey

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835469187
Length 270 pages
Edition 1st Edition
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Author (1):
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Aleksejs Plotnikovs Aleksejs Plotnikovs
Author Profile Icon Aleksejs Plotnikovs
Aleksejs Plotnikovs
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Thinking Local, Acting Global FREE CHAPTER
2. Chapter 1: Where’s My Data and Who’s in Charge? 3. Chapter 2: We Make Data Business Ready 4. Chapter 3: Thousands to One – From Locally Siloed to Globally Centralized Processes 5. Chapter 4: “Reactive! Proactive? Predictive” 6. Part 2: Build Insights to Global Capabilities
7. Chapter 5: Mastering Your Data Domains and Business Ownership 8. Chapter 6: Navigating the Strategic Data Dilemma 9. Chapter 7: Unique Data IP Is Your Magic 10. Chapter 8: The Pareto Principle in Action 11. Part 3: Intelligent Future
12. Chapter 9: Data Mastering and MDM 13. Chapter 10: Data Mesh and Data Governance 14. Chapter 11: Data Assets or Data Products? 15. Chapter 12: Data Value, Literacy, and Culture 16. Chapter 13: Getting Ready for GenAI 17. Index 18. Other Books You May Enjoy

Data Assets or Data Products?

New paradigms arrive every day and we are happily jumping on exploring, experimenting, and learning how to best use them.

The notion of data products was originally driven by data science, yet as the thinking besides data productization matured, it made additional thoughts and diversions. Aside from the general direction to treat data as a product – meaning taking the utmost care of it, developing a roadmap of usage and deployment, elevating the value and adoption to align with discrete business goals, and addressing the scalability, constant upgrades, modernization needs, and much more. However, it also required the creation of a distinct differentiation between the core (“raw data”) data assets and data product approach.

This is nothing new for the industry – a decade ago, we had already a similar notion of data warehousing coming over with massive data extraction and transformation techniques, positing itself as the...

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