Data Cleaning Guidelines for a D365 Finance & Operations Implementation

Dynamics 365 Finance & Operations (D365FO) represents a major leap toward streamlining business processes and enhancing operational efficiency. However, the success of this implementation hinges on the quality of the migrated data. This post offers best practices for data cleansing to ensure a smooth and effective D365FO implementation.

Exclude Obsolete Data

Obsolete data should be excluded in advance of a migration. This includes products, customers, and vendors:

• Obsolete Products: Do not migrate products that are no longer sold or used. Exclude any discontinued or irrelevant products as well.
• Inactive Customers: Customers who have not engaged with the business for a significant period can be excluded.
• Outdated Vendors: Vendors who are no longer in business or have not been transacted with recently should be left out.

Key Business Considerations

There are specific types of data that are key to business operations. These data types should be carefully considered during a data cleansing ahead of a migration.

Identify Essential Data

• Current Inventory: Ensure that all active inventory data is accurate and up-to-date.
• Active Customers: Focus on customers who are currently engaged and have recent transactions.
• Ongoing Orders: Pay special attention to sales orders, purchase orders, and manufacturing orders that are still in progress.

Data Quality and Accuracy

• Updates and Consistency: Make sure all data to be migrated is current, consistent, and free from errors.
• Historical Data: Evaluate the necessity of historical data for compliance or operational insights. Only migrate what’s essential.

Stakeholder Engagement

• Data Requirements: Engage with relevant stakeholders to understand their data requirements and expectations from the new system.
• Verification: Collaborate with stakeholders to verify data mappings and ensure a smooth transition.

Strategy for Obsolete Data

• Archiving: Develop a clear strategy for managing and archiving obsolete data to ensure it’s not migrated.
• Retention Policy: Align your data migration with the company’s data retention policy, ensuring only relevant data is included.

Special Attention Data Categories

Ensure proper validation and cleansing of data to match Dynamics 365 Finance and Operations requirements, including data type, format, and naming conventions.

Work in Progress (WIP)

• Source Data Fields: WIP ID, Description, Quantity, Start Date, Completion Date, Status
• D365FO Mapping: Production Order ID, Description, Planned Quantity, Start Date, End Date, Status

Sales Orders

• Source Data Fields: Order ID, Customer ID, Product(s) Ordered, Quantity, Price, Delivery Date
• D365FO Mapping: Sales Order ID, Customer Account, Product(s) Line Items, Quantity, Unit Price, Delivery Date

Purchase Orders

• Source Data Fields: Order ID, Vendor ID, Product(s) Ordered, Quantity, Price, Delivery Date
• D365FO Mapping: Purchase Order ID, Vendor Account, Product(s) Line Items, Quantity, Unit Price, Expected Receipt Date

Data Prioritization

Before migrating your data, consider the types of data and where they fall in terms of priority. My suggestions are the following:

1. Current Inventory
2. Active Customers
3. Ongoing Orders
4. Vendors

Data Retention Policy

Impact on Migration Process

• Scope Definition: The retention policy defines the scope of data to be migrated, ensuring relevance.
• Obsolete Data Management: Directs the strategy for managing and archiving obsolete data, reducing migration time and costs.
• Prioritization: Guides the identification and prioritization of essential data aligned with the policy’s retention periods.
• Quality Standards: Sets the standard for data quality and accuracy, ensuring only high-quality data is migrated.

Final Thoughts

In conclusion, migrating to Dynamics 365 Finance & Operations is a substantial task that requires meticulous planning and execution. By following these data cleansing guidelines, you can ensure a smooth and successful transition. Remember, the quality of your data is crucial to the effectiveness of your new system. Engage stakeholders, prioritize essential data, and adhere to your data retention policy to achieve the best results.

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