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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

Where’s My Data and Who’s in Charge?

This first chapter delves into common business challenges stemming from the necessity of maintaining data quality and effective data management within a company. Often, there is either a complete absence of these practices or, at the very least, a partial implementation. This challenge typically impacts the end users of data the most, including sales, marketing, pre-sales, go-to-market, and finance teams. Meanwhile, the IT team tends to lag behind and might not even fully comprehend the issue until they are confronted with urgent user concerns.

This was notably the case for Microsoft in its early years of data enablement. The IT division had limited exposure to data-related problems as its focus was primarily on engineering and developing long-term solutions within a specific ecosystem. Consequently, there was no single global team responsible for overseeing data, ensuring data quality, and managing data across key Tier 1 Line of...

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