How to troubleshoot Salesforce data migration and transformation issues?

Troubleshooting Salesforce data migration and transformation issues requires a structured approach that addresses common pitfalls in data quality, system compatibility, and process execution. The most critical challenges stem from poor data preparation, mismatched field mappings, and inadequate testing—each of which can derail migration efforts and compromise system performance. Successful troubleshooting begins with validating data integrity before migration, ensuring legacy systems align with Salesforce’s field requirements, and systematically testing transformations in controlled environments.

Key findings from the sources reveal:

• Data quality issues account for the majority of migration failures, with uncleaned or poorly mapped data causing errors like duplicate records, invalid field values, and failed validations ^[2][5][10].
• Field-level mismatches (e.g., character limits, picklist values, or required fields) trigger frequent errors in tools like Data Loader, requiring pre-migration audits of Salesforce’s schema ^[7][9].
• Batch processing and validation during migration reduce downtime and simplify error resolution, with tools like Data Import Wizard and Data Loader offering built-in error logs for troubleshooting ^[1][6].
• Post-migration validation is critical but often overlooked, with experts recommending iterative testing of small data batches before full-scale execution ^[6][8].

Systematic Troubleshooting for Salesforce Data Migration Issues

Diagnosing and Resolving Data Quality Problems

Data quality issues—such as duplicates, incomplete records, or inconsistent formatting—are the leading cause of migration failures. These problems manifest as errors during import (e.g., DUPLICATE_VALUE or REQUIREDFIELDMISSING) or as post-migration inconsistencies that disrupt analytics and user adoption. Addressing them requires a multi-step cleansing process before migration begins.

Key steps to diagnose and resolve data quality issues:

• Conduct a data audit: Profile legacy data to identify missing values, duplicates, and formatting inconsistencies. Tools like Excel’s COUNTIF or dedicated data quality platforms (e.g., Talend) can automate this process ^[2][5].
• Example: A Reddit user emphasized that "cleansing is tedious but critical"—highlighting that 30% of migration time should be allocated to this phase ^[3].
• Standardize formats: Ensure dates, phone numbers, and addresses adhere to Salesforce’s expected formats (e.g., MM/DD/YYYY for dates). Mismatches here trigger MALFORMED_ID or FIELDINTEGRITYEXCEPTION errors ^[9].
• Deduplicate records: Use fuzzy matching algorithms or Salesforce’s native deduplication tools to merge or remove duplicates. For example, the INVALIDCROSSREFERENCE_KEY error often stems from referencing duplicate parent records ^[10].
• Validate against Salesforce constraints: Check field character limits (e.g., Text fields max out at 255 characters in standard Salesforce) and picklist values. Overlength data or invalid picklist entries will fail during import ^[7].

A common oversight is assuming legacy data will "fit" into Salesforce without adjustment. As noted in SalesforceBen, "evaluating legacy data for quality issues" should precede any mapping efforts, as poor-quality data exacerbates transformation errors ^[5].

Fixing Field Mapping and Transformation Errors

Incorrect field mappings and transformation logic account for ~40% of migration failures, according to migration experts ^[10]. These errors arise when legacy data structures don’t align with Salesforce’s schema, leading to issues like:

• INVALIDFIELDFORINSERTUPDATE: Attempting to write to read-only fields (e.g., CreatedDate).
• FIELDINTEGRITYEXCEPTION: Inserting values not defined in picklists.
• INVALIDCROSSREFERENCE_KEY: Referencing non-existent lookup records.

To troubleshoot mapping issues:

• Create a field mapping document: Explicitly link each legacy field to its Salesforce counterpart, including data type conversions (e.g., legacy "Y/N" flags → Salesforce checkboxes). WalkMe recommends using spreadsheets with columns for source field, target field, transformation rules, and validation status ^[6].
• Test transformations in a sandbox: Use Salesforce’s Data Import Wizard or Data Loader to run test loads with small datasets. The Winter ’24 release notes highlight that batch data transforms can now be edited directly in the canvas editor, allowing real-time fixes ^[8].
• Example: If a MALFORMED_ID error appears, verify that legacy IDs are converted to Salesforce’s 18-character format.
• Handle multi-select picklists and default values: Legacy systems often use comma-separated values for multi-select fields, which must be split into Salesforce’s semi-colon-delimited format. Default values (e.g., "None" for empty picklists) should be predefined in Salesforce’s schema ^[1].
• Leverage error logs: Data Loader generates .err files for failed records. Common fixes include:
• For REQUIREDFIELDMISSING: Populate mandatory fields (e.g., LastName for Contacts) or mark them as non-required in Salesforce setup.
• For DUPLICATE_VALUE: Enable Salesforce’s duplicate management rules or append timestamps to duplicate records ^[9].

Additional Critical Steps (Brief Overview)

While the above sections cover the core issues, two other areas require attention:

1. Performance and scalability: For large datasets (>50,000 records), use batch processing (e.g., Data Loader’s --batchsize parameter) to avoid STORAGELIMITEXCEEDED errors. Schedule migrations during off-peak hours to minimize downtime ^[2].
2. Post-migration validation: Compare record counts and sample data between source and target systems. WalkMe advises validating "at least 10% of migrated records" for accuracy ^[6].