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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

Loading high-quality data

When users interact with data, the data must be of good quality for it to be effective. For example, salespeople may go to look at a Contact record in Salesforce to make a call to try and up-sell or cross-sell to that customer. If users are presented with multiple Contact records for the same person (duplicates), and those records are at different levels of completeness, they may end up very annoyed. Due to this, those users may not have all of the information for a Contact or, worse, are still jumping between records to deduce the correct phone number to call or email address to use.

As mentioned previously, practical steps can be taken as part of a data migration process. When preparing data before loading it, or once the data has been loaded into the target system, cleansing, de-duplication, and (optionally) enrichment must take place. When thinking about the quantity of data as well as its quality, it may make sense for some enterprises to perform the...

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