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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

Case study – the creation of the Unified Support service

To illustrate and reflect on previous learnings, our approach almost always involved nurturing new services or ideas through data operations, gathering invaluable insights directly from our incredible professionals.

This involved a much-needed dynamic collaboration between business experts and data team members before any subsequent automation or engineering took place. This path often stands in contrast to the more conventional IT-driven or engineering-driven methods, where project managers collect requirements and software engineers construct solutions. We’ve emphasized this a few times already, and the reason we’re going back to this notion again is the crucial importance of succeeding in a fast, ambiguous, and uncertain environment. The ability to quickly experiment with data and change direction at no cost to the infrastructure is commonly underestimated.

We found that, when working with data,...

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