In my previous role at Liberty, we undertook a significant data migration project that involved transitioning legacy systems to modern platforms like Salesforce and AWS Workforce. Initially, the project was planned with a strict timeline, and we had accounted for potential issues such as data compatibility and system downtime.
However, during the migration, we encountered unexpected challenges with data inconsistencies and quality issues that were more severe than anticipated. These issues arose because the legacy systems had inconsistent data formats and lacked comprehensive data validation rules, leading to discrepancies when transferring data to the new systems.
To address these challenges, we had to pause the migration process and implement an extensive data cleansing and validation phase. This involved using Python scripts and tools like Salesforce Inspector to standardize data formats and validate data accuracy. While this extended the project timeline, it was crucial to ensure that the data integrity was maintained post-migration.
After the adjustments, we successfully completed the migration with improved data accuracy, which was a critical requirement for the project's success. This experience taught me the importance of thorough data assessment and planning for contingencies, especially when dealing with complex data systems. It also reinforced the value of a flexible project management approach to accommodate unforeseen challenges.
