Here are some strategies to address these issues and ensure a smooth transition: 1. Data Profiling and Assessment: Identify Discrepancies & Prioritize Issues 2. Data Cleansing and Standardization: Correct Errors, Standardize Data & Handle Missing Values 3. Data Validation and Quality Checks 4. Data Transformation and Mapping 5. Data Migration Testing 6. Data Reconciliation and Verification 7. Data Governance and Stewardship By following these strategies, you can effectively address data quality discrepancies and ensure a successful data migration.

If smoothness is the priority, simply deleting any conflicting records will do. - As a professional you will not rely on LinkedIn as a source for critical information.

To ensure a smooth, value-driven data transition: 1. ???????? ?????????????????? & ????????????????????: Assess source data quality to improve relevance, migrating only clean data. 2. ?????????????????? & ????????????????????????????: Standardize and enrich data for usability in the new system. 3. ?????????????? & ???????????????? ??????????: Define mappings with business rules to add value beyond replication. 4. ?????????????????????? ?????????????????? & ????????????????????: Migrate in phases, verifying each batch to ensure accuracy. 5. ?????????????????????? ????????????????????: Involve stakeholders to align with business goals and ensure data remains relevant post-migration.

This happens when there is miss in the process of data migration. Follow the below steps 1. Divide the migration into multiple steps and modules. Example like Finance, Inventory, HR etc. 2. Migrate tables in batches and maintain the corresponding log tables, so that we can refer the same afterwards. 3. Atleast have 2/3 runs in DEV/UAT/Pre-prod to have enough confidence. 4. Have discussions with all stakeholders like Business Analysts, End business users, Development team and Data Architect and make sure they have done enough testing 5. Have verified and tested rollback plan in place as not have much business downtime If it comes, try to solve and if it seems it take longer time without egos, have heart to rollback and start again.

In the context of data migration, validation rules are the most important point to maintain the integrity of the data and reducing errors.