A comprehensive guide to planning, executing, and validating data migrations in Salesforce CRM.
Understanding the fundamentals of enterprise-grade CRM data migration
Salesforce CRM data migration is the process of transferring data from an existing system (legacy system, another CRM, or flat files) to the Salesforce platform. This is a critical step in any Salesforce implementation or integration, ensuring that valuable business information is accurately and efficiently moved to the new platform.
The process requires strategic planning, meticulous execution, and validation to ensure data integrity, minimize downtime, and avoid business disruption. It's not just about moving data; it's about transforming, cleaning, and structuring it to fit Salesforce's data model and business processes.
Migrating from legacy on-premise systems (e.g., old databases, spreadsheets)
Consolidating data from multiple disparate systems into a single Salesforce instance
Upgrading from an older CRM system to Salesforce
Merging data from acquired companies into an existing Salesforce org
The complexity of a data migration project can vary significantly based on several critical factors:
A typical Salesforce data migration project follows a structured approach, encompassing several key phases to ensure data quality, integrity, and a seamless transition.
This crucial document defines the scope of the migration. It meticulously lists all in-scope objects and fields, their respective data types, transformation rules (how source data is manipulated), and mapping rules (where legacy data to the target (Salesforce CRM). Documenting all business rule transformations appropriately in this mapping sheet is vital. This document serves as a "sign-off" sheet, confirming the successful alignment of data and transformations, and ensuring no open-ended questions before proceeding to the development phase.
Simplified example showing field-level mapping from legacy systems to Salesforce
| Source Object | Source Field Name | Source Data Type | Target Salesforce Object | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules |
|---|---|---|---|---|---|---|
| Legacy_CRM_Accounts | Account_ID | VARCHAR(50) | Account | External_ID__c | Text (External ID) | Direct Map |
| Legacy_CRM_Accounts | Account_Name | VARCHAR(255) | Account | Name | Text | Direct Map |
| Legacy_CRM_Accounts | Customer_Type | VARCHAR(20) | Account | Type | Picklist | Map 'Retail' to 'Customer', 'Whole' to 'Partner'. Default to 'Other' if no match. |
| Legacy_CRM_Accounts | Annual_Revenue_USD | DECIMAL(18,2) | Account | AnnualRevenue | Currency | Direct Map |
| Legacy_CRM_Accounts | Created_Date | DATETIME | Account | CreatedDate | DateTime | Direct Map (if allowed by Salesforce API, otherwise use custom field) |
| Legacy_CRM_Contacts | Contact_ID | VARCHAR(50) | Contact | Legacy_Contact_ID__c | Text (External ID) | Direct Map |
| Legacy_CRM_Contacts | First_Name | VARCHAR(100) | Contact | FirstName | Text | Direct Map |
| Legacy_CRM_Contacts | Last_Name | VARCHAR(100) | Contact | LastName | Text | Direct Map |
| Legacy_CRM_Contacts | Email_Address | VARCHAR(255) | Contact | Validate format (e.g., contains '@' and '.'). Clean leading/trailing spaces. | ||
| Legacy_CRM_Contacts | Legacy_Account_ID | VARCHAR(50) | Contact | AccountId | Lookup(Account) | Lookup Salesforce Account ID using Legacy_Account_ID from Account object's External_ID__c. |
| Legacy_CRM_Opportunities | Opportunity_ID | VARCHAR(50) | Opportunity | Legacy_Opportunity_ID__c | Text (External ID) | Direct Map |
| Legacy_CRM_Opportunities | Opportunity_Name | VARCHAR(255) | Opportunity | Name | Text | Direct Map |
| Legacy_CRM_Opportunities | Deal_Stage | VARCHAR(50) | Opportunity | StageName | Picklist | Map 'Prospecting' to 'Prospecting', 'Negotiation' to 'Negotiation/Review'. Default to 'Closed Lost' if historical status is 'Lost'. |
| Legacy_CRM_Opportunities | Close_Date | DATE | Opportunity | CloseDate | Date | Direct Map |
| Legacy_CRM_Opportunities | Deal_Amount_USD | DECIMAL(18,2) | Opportunity | Amount | Currency | Direct Map |
| Legacy_CRM_Opportunities | Legacy_Account_ID | VARCHAR(50) | Opportunity | AccountId | Lookup(Account) | Lookup Salesforce Account ID using Legacy_Account_ID from Account object's External_ID__c. |
In this phase, the development of ETL (Extract, Transform, Load) code begins. A common practice involves creating intermediary tables to manage data flow and transformation:
Data is initially submitted by clients or extracted from source systems.
Source data is first imported into a "Synonym Table" (Staging Table). This table acts as an exact, untouched replica of the raw source data, preserving its original format and source system headers. This ensures that the original source data remains intact and can be re-referenced if any transformations go awry.
Data from the Synonym Table is then imported into the "Landing Table." This is where all necessary data transformations, cleansing, and standardization operations are performed. The Landing Table can contain source system headers or a mix of source and target system headers. Critically, it should include additional columns for tracking:
Once all transformations and validations are complete on the Landing Table, the prepared data is moved to the "Load Table." This table is specifically structured to match the Salesforce data model, containing only the exact Salesforce API names (not labels) as headers. This strict adherence to API names is essential for successful backend data loads into Salesforce.
Detailed Conceptual Data Flow: Source to Load Table
During code development, it's crucial to incorporate robust pre-validation checks. These checks ensure that the data quality meets minimum Salesforce system's requirements and identify any records that might cause failures during the actual data load. Generating a detailed error report from these checks and sharing it with the client is a vital preliminary step. This proactive approach helps in rectifying data quality issues at the source, ensuring a smoother migration and validating the data's readiness for Salesforce.
When it's time to load data into Salesforce, several tools are available, chosen based on data volume, complexity, and security requirements:
A popular open-source browser extension (e.g., for Chrome, Firefox) often used for quick data loads and inspections. It's suitable for loading moderate volumes of data, typically up to 50,000 to 100,000 records. Its ease of use has made it a favorite among administrators during testing phases.
This is an official Salesforce-supported desktop application. It's highly preferred for its robust capabilities without any API call consumption and administrator-level access to Salesforce. It can handle larger volumes of data than Salesforce Inspector and is ideal for production loads.
For very large data volumes, complex integrations, or automation pipelines, tools like Azure Data Factory (ADF) or similar ETL tools (e.g., Informatica, Mulesoft, Talend) can be used. These tools can build data push jobs that extract data from SQL databases (or other sources) and directly load it into Salesforce using Salesforce's Bulk APIs (preferably Bulk API 2.0 for enhanced performance and simplified error handling). This method is highly scalable and efficient for enterprise-level migrations.
After the initial data load, especially for parent objects, it's crucial to capture the newly generated Salesforce IDs. These Salesforce IDs (often referred to as 'External IDs' in the context of migration for linking purposes) are essential for associating child object records with their respective parent records. Since child records typically have a lookup relationship to parent records, the Salesforce ID of the parent must be available to correctly link the child records during their load. This often involves an iterative loading process where parent records are loaded first, their Salesforce IDs are extracted, and then these IDs are used to populate the lookup fields on child records.
Parent-Child Data Loading Flow
A common challenge in data migration arises when an object has a lookup relationship to itself (e.g., an Account referring to a Parent Account, or an Opportunity referring to a Predecessor Opportunity). In such cases, a direct single-pass load is not feasible because the Salesforce ID of the referenced record might not exist yet during the initial load.
The solution involves a post-data load update strategy:
Conceptual Flow for Handling Self-Referencing Lookups
When performing data transformation or update operations, especially within SQL Server stored procedures or ETL scripts, it's a best practice to include tracking columns. These columns can significantly aid in debugging, auditing, and understanding data quality issues.
For environments dealing with large volumes of Salesforce records, manual data extraction or reliance solely on tools like Salesforce Inspector can be inefficient. An automated approach using ETL tools like Azure Data Factory (ADF) combined with "parameter tables" is highly recommended.
These "base tables" or "parameter tables" act as control mechanisms for the ADF pipelines. A common example is a table named 'Salesforce_Extract_Metadata', which stores essential information about Salesforce objects and their fields.
| Column Name | Data Type | Description |
|---|---|---|
| 'Object_API_Name' | VARCHAR(100) | Salesforce API Name of the object (e.g., 'Account', 'Contact', 'Opportunity'). |
| 'Field_API_Name' | VARCHAR(255) | Salesforce API Name of the field (e.g., 'Name', 'Industry', 'Email'). |
| 'Field_Label' | VARCHAR(255) | User-friendly label of the field (e.g., 'Account Name', 'Industry Type'). |
| 'Is_Active' | BOOLEAN | Flag to indicate if the field should be included in extraction (TRUE/FALSE). |
Here's a sample of the 'dbo.salesforce_extract_metadata' table entries for the Account object:
| EntityName | BusinessEntityName | Field API Name | Filter Condition |
|---|---|---|---|
| Account | Account | Name | where Processing_Status__c='Confirmed' |
| Account | Account | Source_System_Id__c | where Processing_Status__c='Confirmed' |
| Account | Account | State | where Processing_Status__c='Confirmed' |
| Account | Account | City | where Processing_Status__c='Confirmed' |
| Account | Account | OwnerId | where Processing_Status__c='Confirmed' |
| Account | Account | RecordTypeId | where Processing_Status__c='Confirmed' |
| Account | Account | RecordType.Name | where Processing_Status__c='Confirmed' |
| Account | Account | Owner.Employee_Code__c | where Processing_Status__c='Confirmed' |
| Account | Account | CreatedDate | where Processing_Status__c='Confirmed' |
| Account | Account | LastModifiedDate | where Processing_Status__c='Confirmed' |
SELECT
Object_API_Name,
'SELECT ' + STRING_AGG(QUOTENAME([Field_API_Name]), ', ')
WITHIN GROUP (ORDER BY [Field_API_Name]) + ' FROM ' + Object_API_Name
FROM
dbo.salesforce_extract_metadata
WHERE
-- Add any condition to filter active fields or specific objects if needed
-- For example, to get fields for 'Account' object:
Object_API_Name = 'Account'
GROUP BY
Object_API_Name;This dynamically generated SOQL query is then executed by ADF against the Salesforce environment. The entire process of generating the SOQL query and writing data back into the database can be visualized as follows:
Conceptual Flow: Metadata to SOQL Query Generation and Data Write-Back
A successful Salesforce data migration isn't a single event but a carefully orchestrated series of steps, often iterated through various testing environments before reaching production.
Here's a typical end-to-end flow:
This initial step involves creating a detailed mapping of all source system data fields to their corresponding Salesforce target objects and fields. This process is crucial for defining the project's scope, identifying necessary data transformations, and estimating the effort required for code development. It ensures everyone agrees on what data moves where and how it changes.
Formal approval of the API mapping sheet by all key stakeholders (business, technical, and data governance). This step-by-step technical checkpoint confirms that everyone is in alignment before actual development work begins.
Before loading into Salesforce, prepare your data. This might involve creating dummy data sets or taking a small, representative sample of your actual source data then undergoes pre-validation checks to identify and fix any quality issues or transformation errors in a lower sandbox environment (Development or Validation cycle).
Perform the initial data loads using the prepared dummy or sample data into a development or validation sandbox. This is where you test your ETL scripts and loading mechanisms in a controlled environment.
After initial data loads, especially for objects that refer to themselves (like a Parent Account field on an Account record), perform a separate update step. This ensures that all records link correctly (as the target records within the same object did not exist when Salesforce IDs are generated.
Create a "crosswalk" file. This is essentially a merged file containing your original source system IDs alongside the newly generated Salesforce IDs for each record. This file is invaluable for:
Conduct thorough Quality Assurance (QA) testing. This involves comprehensive validation of the migrated data, testing business processes, and verifying integrations. This phase also includes dedicated support for identifying and resolving any defects or data discrepancies found.
Obtain formal approval from key stakeholders ensuring that data quality, migration processes are validated and meet requirements in the Development/Validation environment.
The process from Step 3 (Dummy data creation/sample pre-validation) onwards is typically repeated across comprehensive load cycles:
For the production cycle, the testing support transitions into Hypercare, an intensive support period immediately after go-live to address any critical issues that arise.
Define exactly how data from your old system maps to Salesforce objects (like Accounts, Contacts) and their fields. This clarifies what data moves, how it transforms, and helps estimate the work.
Get formal approval on the detailed data mapping document. This ensures everyone agrees on the data structure before development begins.
Prepare either test data or a small, representative sample of your actual source data. This is used for initial testing in a non-production Salesforce environment (like a Sandbox).
Load the prepared dummy or sample data into your Salesforce Development/Validation Sandbox to test the migration scripts and processes.
After initial data loads, perform necessary updates, such as linking related Salesforce records (e.g., setting the 'Parent Account' lookup on Account records, or linking Contacts to Accounts).
Create a "crosswalk" file. This is a crucial document that links your original source system IDs to the new Salesforce IDs for each migrated record. It's vital for validation and integrating with other systems.
Thoroughly test the migrated data and processes in the DV environment for about two weeks, identifying and fixing any issues or "defects."
Once testing is complete and satisfactory, formally approve the data migration in the DV environment, confirming it's ready for higher environments.
This entire process (from Step 3 onwards) is repeated for each subsequent environment to ensure data quality and process readiness.
Each cycle loops back to "Step 3: Dummy Data Creation / Sample Data Validation"
Once Production data is loaded and validated, the migration is complete. Hypercare refers to intensive support immediately after the production go-live.
Understanding the core Salesforce objects and their relationships is fundamental for successful data migration. Below is a list of common objects involved in CRM data migrations, along with their functional representation and sample API mappings.
The User object in Salesforce represents an individual who can log in to Salesforce. It stores information about the user, their permissions, and their access levels within the organization. Migrating user data often involves careful consideration of security, roles, profiles, and permission sets to ensure correct access to records and functionalities.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Employee_ID | VARCHAR(50) | FederationIdentifier | Text (External ID) | Direct Map | Used for SSO or external system integration. |
| First_Name | VARCHAR(100) | FirstName | Text | Direct Map | |
| Last_Name | VARCHAR(100) | LastName | Text | Direct Map | Required field. |
| Email_Address | VARCHAR(255) | Validate format; must be unique. | Required for login. | ||
| Username | VARCHAR(255) | Username | Direct Map (often same as Email, but must be unique across all Salesforce orgs) | Required for login. | |
| User_Role_Name | VARCHAR(100) | UserRoleId | Lookup(Role) | Lookup Salesforce Role ID based on Role Name. | Roles define data visibility hierarchy. |
| User_Profile_Name | VARCHAR(100) | ProfileId | Lookup(Profile) | Lookup Salesforce Profile ID based on Profile Name. | Profiles define object/field permissions. |
| Is_Active | BOOLEAN | IsActive | Boolean | Map 'Yes'/'1' to TRUE, 'No'/'0' to FALSE. | Deactivated users do not count towards licenses. |
The Account object represents companies, organizations, or individual consumers that you do business with. It is one of the most fundamental objects in Salesforce, serving as the central hub for all related activities, contacts, opportunities, and cases. Proper migration of accounts is critical for maintaining customer relationships and historical data.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Account_ID | VARCHAR(50) | External_ID__c | Text (External ID) | Direct Map | Used for upsert operations and linking child records. |
| Account_Name | VARCHAR(255) | Name | Text | Direct Map | Required field. |
| Industry_Type | VARCHAR(50) | Industry | Picklist | Map source industry values to Salesforce picklist values. Default to 'Other' if no match. | Ensure picklist values exist in Salesforce. |
| Primary_Contact_Email | VARCHAR(255) | Primary_Contact_Email__c | Direct Map, validate email format. | Custom field for primary contact email. | |
| Annual_Revenue_USD | DECIMAL(18,2) | AnnualRevenue | Currency | Direct Map | |
| Account_Owner_ID | VARCHAR(50) | OwnerId | Lookup(User) | Lookup Salesforce User ID based on Source Owner ID (e.g., Employee_ID). | Ensures correct ownership in Salesforce. |
The Contact object represents individuals associated with an Account. These are the people you interact with at companies or organizations. Contacts are linked to Accounts, forming the basis of customer relationship management. Migrating contacts involves linking them correctly to their respective accounts.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Contact_ID | VARCHAR(50) | Legacy_Contact_ID__c | Text (External ID) | Direct Map | Used for upsert operations and linking. |
| First_Name | VARCHAR(100) | FirstName | Text | Direct Map | |
| Last_Name | VARCHAR(100) | LastName | Text | Direct Map | Required field. |
| Email_Address | VARCHAR(255) | Validate format (e.g., contains '@' and '.'). Clean leading/trailing spaces. | Invalid emails might cause load failures. | ||
| Legacy_Account_ID | VARCHAR(50) | AccountId | Lookup(Account) | Lookup Salesforce Account ID using Legacy_Account_ID from Account object's External_ID__c. | Requires Account records to be loaded first. |
The Individual object is part of Salesforce's Person Accounts or Privacy and Data Protection features (like GDPR compliance). It represents a natural person and is used to store privacy preferences and other personal data separate from the Contact or Lead object. It allows for a more granular approach to managing individual consent and data rights.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Privacy_Consent_ID | VARCHAR(50) | External_ID__c | Text (External ID) | Direct Map | Used for upsert operations. |
| Contact_Preference | VARCHAR(50) | CanContact__c | Boolean | Map 'Opt-In' to TRUE, 'Opt-Out' to FALSE. | Custom field for contact preference. |
| Data_Deletion_Request | BOOLEAN | IsDataSubjectRequest__c | Boolean | Direct Map | Indicates a request for data deletion. |
| Legacy_Contact_ID | VARCHAR(50) | ContactId | Lookup(Contact) | Lookup Salesforce Contact ID using Legacy_Contact_ID from Contact object's Legacy_Contact_ID__c. | Links the Individual record to a Contact. |
The Opportunity object represents a potential sale or pending deal with an Account. It tracks the sales process from lead qualification to closing the deal, including stages, amounts, and close dates. Opportunities are central to sales forecasting and revenue tracking in Salesforce.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Opportunity_ID | VARCHAR(50) | Legacy_Opportunity_ID__c | Text (External ID) | Direct Map | Used for upsert operations and linking. |
| Deal_Name | VARCHAR(255) | Name | Text | Direct Map | Required field. |
| Current_Stage | VARCHAR(50) | StageName | Picklist | Map source stage values to Salesforce picklist values. Handle 'Closed Won'/'Closed Lost' appropriately. | Required field. |
| Expected_Close_Date | DATE | CloseDate | Date | Direct Map | Required field. |
| Estimated_Amount_USD | DECIMAL(18,2) | Amount | Currency | Direct Map | |
| Legacy_Account_ID | VARCHAR(50) | AccountId | Lookup(Account) | Lookup Salesforce Account ID using Legacy_Account_ID from Account object's External_ID__c. | Requires Account records to be loaded first. |
| Opportunity_Owner_ID | VARCHAR(50) | OwnerId | Lookup(User) | Lookup Salesforce User ID based on Source Owner ID. | Ensures correct ownership. |
The Opportunity Split object allows sales teams to allocate credit for an opportunity to multiple sales team members. This is particularly useful in collaborative sales environments where more than one person contributes to closing a deal. It tracks the percentage of revenue or overlay credit each team member receives.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Opp_Split_ID | VARCHAR(50) | External_ID__c | Text (External ID) | Direct Map | Used for upsert operations. |
| Legacy_Opportunity_ID | VARCHAR(50) | OpportunityId | Lookup(Opportunity) | Lookup Salesforce Opportunity ID using Legacy_Opportunity_ID from Opportunity object's Legacy_Opportunity_ID__c. | Required. Opportunity records must be loaded first. |
| Sales_Rep_ID | VARCHAR(50) | UserId | Lookup(User) | Lookup Salesforce User ID based on Sales_Rep_ID from User object's FederationIdentifier. | Required. User records must be loaded first. |
| Split_Percentage | DECIMAL(5,2) | SplitPercentage | Percent | Direct Map. Ensure sum of splits for an opportunity equals 100% (for Revenue splits). | Required. |
| Split_Type | VARCHAR(50) | SplitTypeId | Lookup(SplitType) | Lookup Salesforce Split Type ID (e.g., 'Revenue', 'Overlay'). | Requires SplitType setup in Salesforce. |
The Opportunity Split Asset object is typically a custom object or part of a specialized Salesforce product (like Revenue Cloud or specific CPQ implementations) used to track how revenue from specific assets (e.g., subscriptions, products) within an opportunity is split among team members. This provides a more granular view of revenue attribution than the standard Opportunity Split.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Split_Asset_ID | VARCHAR(50) | External_ID__c | Text (External ID) | Direct Map | Used for upsert operations. |
| Legacy_Opportunity_ID | VARCHAR(50) | OpportunityId | Lookup(Opportunity) | Lookup Salesforce Opportunity ID. | Required. |
| Legacy_Product_Asset_ID | VARCHAR(50) | AssetId | Lookup(Asset) | Lookup Salesforce Asset ID. | Requires Asset records to be loaded or created. |
| Sales_Rep_ID | VARCHAR(50) | UserId | Lookup(User) | Lookup Salesforce User ID. | Required. |
| Asset_Split_Percentage | DECIMAL(5,2) | Split_Percentage__c | Percent | Direct Map. | Custom field. |
The Opportunity Split Alliance object is also typically a custom object, often used in scenarios where multiple partners or alliances contribute to an opportunity, and their respective revenue or credit shares need to be tracked. This is common in channel sales or complex enterprise deals involving various stakeholders. It allows for tracking splits beyond just internal sales team members.
| Source Field Name | Source Data Type | Target Salesforce API Name | Target Salesforce Data Type | Transformation Logic / Business Rules | Notes / Comments |
|---|---|---|---|---|---|
| Legacy_Split_Alliance_ID | VARCHAR(50) | External_ID__c | Text (External ID) | Direct Map | Used for upsert operations. |
| Legacy_Opportunity_ID | VARCHAR(50) | OpportunityId | Lookup(Opportunity) | Lookup Salesforce Opportunity ID. | Required. |
| Partner_ID | VARCHAR(50) | PartnerAccountId | Lookup(Account) | Lookup Salesforce Account ID for the Partner. | Requires Partner Accounts to be loaded. |
| Alliance_Split_Percentage | DECIMAL(5,2) | Split_Percentage__c | Percent | Direct Map. | Custom field. |
| Alliance_Role | VARCHAR(50) | Role__c | Picklist | Map source role to Salesforce picklist values (e.g., 'Referral', 'Co-Sell'). | Custom field. |
Understanding the various data types available in Salesforce CRM is crucial for accurate data mapping and successful migration. Each data type has specific characteristics, limitations, and implications for how data is stored and displayed.
An Open Text Field in Salesforce allows users to input any free-form text up to a defined character limit. These are versatile fields used for names, codes, short descriptions, or any data that doesn't fit a more structured type.
A Lookup Field establishes a relationship between two objects, linking a record of one object to a record of another object. It's a "many-to-one" relationship, where multiple child records can relate to a single parent record.
A Master-Detail Relationship is a tighter, stronger relationship between two objects compared to a Lookup. It creates a parent-child dependency where the detail (child) record cannot exist without the master (parent) record.
A Normal Picklist provides a drop-down list of predefined values for a field. This helps in controlling data quality, ensuring consistency, and simplifying data entry for users by offering a specific set of values.
A Dependent Picklist is a picklist whose available values are filtered based on the value selected in another controlling field (which can be another picklist or a checkbox). This is used to define hierarchical or conditional relationships between values to improve the user interface.
Data Mapping: Requires careful mapping of both controlling and dependent field values.
Extraction of Values: Historically, extracting the full matrix of dependent picklist values for data mapping and calculation has been complex. While some Salesforce extensions or tools might offer streamlined download options in the past, these are often deprecated or require specific technical expertise.
Technical Activity: Generating accurate dependent picklist mappings may require a "tech debt activity" from the development team, involving programmatic extraction (e.g., Apex, API calls) or specialized tools to correctly map the intricate relationships, as direct UI downloads are not always comprehensive or user-friendly for bulk operations. This is a crucial step to ensure data integrity during migration, as incorrect dependent picklist values can lead to load failures.
For any Salesforce data migration project, a phased approach involving multiple testing cycles is highly recommended. Each cycle builds upon the previous one, adding more rigor and scope to ensure a robust and successful migration.
The entire data migration process, from initial data validation (DV) through End-to-End (E2E), Mock Full Acceptance Testing (MFAT), System Integration Testing (SIT), and User Acceptance Testing (UAT), is an iterative one. Steps like data preparation, loading, validation, and defect resolution are repeated for each cycle, with increasing data values and complexity. The final production cutover is followed by an intensive Hypercare period, providing immediate and critical support to ensure business continuity after go-live.