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F&O Implementation Best Practices, Patterns and Anti-Patterns

Welcome to the Dynamics 365 F&O Implementation Best Practices, Patterns and Anti-Patterns

A curated collection of Implementation best practices for Data Migration, Integration and Reporting that could be critical in making your Dynamics 365 Finance and Operations implementation project a success.

Data Migration Best Practices and Patterns

Scenario 01 - Loading large volume of data using Data Management Framework

Scenario 02 - Loading large volume of data in batch mode (Import in batch)

Scenario 03 - Data loading slows down after importing large volume of data

Scenario 04 - The business has high volume of data

Scenario 05 - Customer plans to use ‘Dual Write (DW)’ infrastructure to integrate data

Scenario 06 - customer needs to have as much historical sales data as possible

Scenario 07 - Master data lying in multiple disparate systems partly due to M&A

Scenario 08 - Importing large volume of GL Journals

Scenario 09 - Data Migrations from legacy systems involving multiple legal entitites

Integration Best Practices and Patterns

Scenario 01 - High volume, batched, file-based GL transactions

Scenario 02 - sales orders from 3rd party system(s)e

Scenario 03 - propagate information about order confirmations to multiple outbound systems

Scenario 04 - credit check / limit assignment is done with external service call

Scenario 05 - High volume/availability e-Commerce pricing

Scenario 06 - Automation of vendor invoices (inbound and outbound)

Scenario 07 - Complex batch scheduling

Reporting Best Practices and Patterns

Scenario 01 - F&O Implementation project reporting requirements

Scenario 02 - Multiple users need different filters and columns for same report to analyze

Scenario 03 - For Month-end closing Business needs to extract and merge data from multiple reports

Scenario 04 - Financial Statements and Reports

Scenario 05 - For Month-end closing Business needs to extract and merge data from multiple reports

Scenario 06 - Reports that require data mash-up between various on-prem and cloud apps