- Determine where the discrepancy originates. This could be from the source data, the migration process itself, or the destination system. - Evaluate how widespread the discrepancy is. Is it an isolated incident or part of a larger pattern? - Assess the quality of both the source and destination data. Look for issues like missing values, format inconsistencies, or duplicate records. - Document the resolution steps taken to fix the discrepancies for future reference. - Conduct a reconciliation process to ensure that the migrated data now aligns with the source data. - Keep all stakeholders informed about the discrepancies, their potential impact, and the actions taken to resolve them.

Navigating unexpected discrepancies during data migration requires a systematic and step-by-step approach. 1- Pause and Analyze the Discrepancy. 2- Check Data Integrity and Mapping Rules. 3- Review System Compatibility. 4- Isolate the Affected Data. 5- Engage Relevant Teams. 6- Modify Migration Scripts. 7- Cleanse Data and Apply Fixes 8- Correct any data quality issues and implement necessary adjustments. 9- Monitor the Process Closely. 10-Perform Post-Migration Validation and Conduct End-to-End Testing.

Puedes guiarte con 5 pasos: 1. Debes de identificalas y debes dar prioridad, clasifica las discrepancias según su gravedad 2. Revisa las reglas de negocio entre sistemas para asegurar alineación. 3. Automatiza validaciones para detectar inconsistencias. 4. Resuelve manualmente con el equipo en casos complejos. 5. Lecciones aprendidas, documenta y aprende de los problemas y soluciones. Este enfoque minimiza errores y facilita futuras migraciones.

Navigating unexpected discrepancies during a data migration can be challenging, but here are some effective strategies to address these issues: Conduct a Thorough Analysis: Start by identifying the specific discrepancies. Compare the source and target data to understand the extent and nature of the issues. Review Data Mapping: Check your data mapping and transformation rules. Ensure that all fields are correctly mapped and that any necessary transformations are applied consistently. Consult Documentation: Refer to any existing documentation regarding the data structure and business rules. This can help clarify any misunderstandings about the data.

It would depend on what data migration method is being adopted for migration. Depending on that we need to see if we can fix the issues during the migration or we let the migration get completed and then access the data issues & how much discrepancy is there and the possible fix. Also we need to understand if the data issue is due to the application feeding the data into the source while the migration is in progress. We may need to stop the data migration and the application feeding the data in source and re-run the migration. That’s one way. Otherwise we need to pause the data migration & research further into the cause of discrepancy.