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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

Taking a look at a typical enterprise—”Data Mess”

In this chapter, we will explore Microsoft’s data platforms and data governance.

At the time of writing, it’s been over three years since we began operating on a Data Mesh platform and implementing a global data governance initiative. This initiative isn’t seen as restrictive or policing; rather, it acts as an enabler of AI and insights.

However, let’s consider a typical enterprise scenario – and Microsoft was very much a conventional enterprise of its time, from the early 2000s up until 2020. Data was scattered across multiple locations, business groups, and IT groups, and lacked real ownership or oversight.

As previously mentioned, we embarked on our data journey somewhat late, opting for a full-fledged data architecture-driven approach for platform creation. We initially used expanded data capabilities and SQL solutions for Data Quality (DQ) and management but lacked...

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