I would establish robust data validation processes to compare source and target systems before, during, and after the migration. This includes implementing ETL pipelines to clean, transform, and standardize data, ensuring consistency. Running parallel systems during migration allows for real-time comparison of outputs, identifying discrepancies early. Additionally, automated data quality monitoring tools can help track issues as they arise. Collaboration with stakeholders is key to defining data quality standards and resolving discrepancies promptly, ensuring alignment and minimizing risks.

To ensure a smooth transition during system migration, I would take the following approach: -Identify and resolve data issues such as duplicates and inconsistencies through a data cleansing process. -Establish clear data mapping rules and validate the data between the source and target systems. -Test the ETL processes and migration rules iteratively to catch and address issues early. -Use analytics tools to compare data post-migration, ensuring accuracy across both systems.

phased approach ensures data quality is maintained throughout the migration process: -Planning and Analysis Phase: - Evaluate current data quality and identify issues like duplicates or inconsistencies - Set clear standards and rules for accuracy, completeness, and integrity in the migrated data. -Design Phase: - Ensure accurate mapping between source and target systems, considering data quality needs. - Run small-scale DQ test migrations -Execution (Migration) Phase: - Set up automated validations to ensure migrated data meets quality standards. - Monitor migration in real-time to catch errors or quality issues. -Post-Migration Phase: - Compare migrated data with source data to ensure completeness and accuracy.

During the data migration and modernization of a leasing company system, we ensured a smooth transition by first conducting a thorough data audit to identify discrepancies. We standardized the data and applied validation rules throughout the migration. Using a phased approach, we migrated data in batches, testing each step for accuracy and completeness. Involving key stakeholders and data stewards ensured alignment with governance standards. After migration, we validated data integrity through reconciliation checks, ensuring quality and consistency across systems.