Data Quality Use Case: How to Mitigate the Risk & Failure of Data Migration

Data migration is a complex process that involves transferring data from one system to another. Almost 85% of data migration projects overrun time and budget and more often than not, the root cause of these failures is a lack of investment in systems and processes that ensure reliable, high-quality data. When newly-migrated data appears to be inaccurate in a new storage system, end users are likely to distrust the data and go back to the old way of working and therefore, it is crucial for businesses to ensure a smooth and successful migration to avoid any potential risks and failures. Data reconciliation is crucial in the process of data migration. It plays a vital role in various industries, including finance, healthcare, and manufacturing, where large volumes of data are generated and analyzed, to identify and rectify discrepancies or errors that may occur during data collection, storage, or transmission. Data reconciliation also helps in maintaining compliance with regulatory requirements and in improving operational efficiency by streamlining data processes and reducing manual efforts, allowing employees to focus on other critical tasks. This 45-minute session contains: - Data migration use cases and best practices - A product showcase to test, manage, and assure data quality - Guidance on how to automate the reconciliation process to save time and resources