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Implementing Microsoft Dynamics 365 for Finance and Operations

You're reading from   Implementing Microsoft Dynamics 365 for Finance and Operations Implement methodology, integration, data migration, and more

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787283336
Length 562 pages
Edition 1st Edition
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Authors (3):
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JJ Yadav JJ Yadav
Author Profile Icon JJ Yadav
JJ Yadav
Rahul Mohta Rahul Mohta
Author Profile Icon Rahul Mohta
Rahul Mohta
Yogesh Kasat Yogesh Kasat
Author Profile Icon Yogesh Kasat
Yogesh Kasat
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Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to Microsoft Dynamics 365 FREE CHAPTER 2. Implementation Methodology and Tools 3. Architecture and Deployment 4. Project Initiation and Kickoff 5. Requirements, Business Process Analysis, and Traceability 6. Configuration and Data Management 7. Functional and Technical Design 8. Integration Planning and Design 9. Building Customizations 10. Analytics, Business Intelligence, and Reporting 11. Testing and Training 12. Go Live 13. Post Go Live Support 14. Update, Upgrade, and Migration

Data migration management 


Similar to configuration management, data migration is an important in a project. By data migration, we mean the data that you need to migrate from the legacy system, such as master data and opening balances. Data is needed for any kind of task and has multiple attributes. Hence, to ensure that it is managed and utilized well, we need to undergo a systematic approach. In the context of implementing a Dynamics 365 solution, we are focusing on overall data management and would like to share a visual for an end-to-end understanding of all the activities involved in planning for data:

The full suite of activities involved in data migration is as follows:

  • Planning for data migration: Involves planning the scope, tools, techniques, volume, and environment
  • Data extraction and cleansing: Cleansing is a business function; however, milestones and dates must be well planned and baked in the data migration plan
  • Data transformation: Seek and agree on the transformation requirements...
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