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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

Raw data deserves appreciation too

Raw data also deserves appreciation. It really does.

When considering the aforementioned three pivotal layers where data products excel, we’re significantly impacting business users and their productivity. Positively, of course. However, the story here isn’t solely about data products.

Underlying these glamorous products are the original datasets or raw data assets.

This operational data, captured daily in key business-facing and partner-facing systems, often arrives from various internal and external sources. Whether coming from an internal CRM system, co-selling activities, or partner-driven business, this raw data, despite its varying quality, might contain tons of hidden insights.

While data products are a prime focus for business users, their foundation is always raw data. Modern data architecture often features embedded data products, adopting a microservice-like approach where data products are nested within each other...

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