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Salesforce Data Architect Certification Guide

You're reading from   Salesforce Data Architect Certification Guide Comprehensive coverage of the Salesforce Data Architect exam content to help you pass on the first attempt

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781801813556
Length 254 pages
Edition 1st Edition
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Author (1):
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Aaron Allport Aaron Allport
Author Profile Icon Aaron Allport
Aaron Allport
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Table of Contents (23) Chapters Close

Preface 1. Section 1: Salesforce Data Architect Theory
2. Chapter 1: Introducing the Salesforce Data Architect Journey FREE CHAPTER 3. Chapter 2: Data Modeling and Database Design 4. Chapter 3: Master Data Management 5. Chapter 4: Salesforce Data Management 6. Chapter 5: Data Governance 7. Chapter 6: Understanding Large Data Volumes 8. Chapter 7: Data Migration 9. Section 2: Salesforce Data Architect Design
10. Chapter 8: Accounts and Contacts 11. Chapter 9: Data APIs and Apex 12. Chapter 10: Tuning Performance 13. Chapter 11: Backup and Restore 14. Chapter 12: Territory Management 15. Section 3: Applying What We've Learned – Practice Questions and Revision Aids
16. Chapter 13: Practice Exam Questions 17. Chapter 14: Cheat Sheets 18. Chapter 15: Further Resources 19. Chapter 16: How to Take the Exam 20. Chapter 17: Answers to Practice Questions 21. Index 22. Other Books You May Enjoy

Consolidating data attributes from multiple sources

When designing an MDM strategy, we need to determine what data attributes constitute the golden record, along with the source of the data attributes. Where multiple sources may contain the same attribute, it may be necessary to implement a cleansing and de-duplication process in order to ensure that the same data attribute values are preserved throughout the enterprise. Depending on the MDM implementation method being used, it may be necessary to allow any source system to facilitate the update of a data attribute to the golden record, which then invokes a process to push that update down to all source systems for that data attribute. Therefore, when implementing the golden record for the first time, data quality and classification are crucial, and so time should be given to a data cleansing, matching, and de-duplication strategy as part of the initial golden record population. It may then be wise to consider locking down the source...

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