Vous êtes confronté à une perte de données au milieu d’une migration de système. Comment allez-vous relever ce défi crucial ?

1)Always have a backup of data for data recovery scenarios and data validation scenarios. 2)Understand what type of data is loss and look for patterns,such as data truncation or dropped rows. 3)Know how data is migrating to the target and the data recovery process. If the migration tool doesn’t support data recovery, this risk must be communicated to stakeholders. 4)Conduct an in-depth analysis of the data loss incident and document the reasons and recovery process. 5)Continuous review and monitoring on the process helps to identify the gaps and audits can help with data validation

In the midst of a system migration, when data loss threatens to derail the project, the strategy pivots to a dynamic response. Instead of relying solely on conventional backups, they initiate a real-time data capture mechanism, intercepting and redirecting the flow to a secure, parallel environment. This proactive approach ensures that no data slips through the cracks, even as the primary system faces turbulence. The team also engages in forensic analysis, not just to recover lost data, but to understand the weak points, turning this crisis into an opportunity to reinforce the system's resilience.

Identify what data is lost and its important.If necessary, pause the migration to prevent further loss. Use backups, data logs, or manual methods.If complete recovery is impossible, minimize the impact.Review the migration process, improve procedures, and seek professional help if needed.

Based on my experience, system migrations present significant risks, including potential data loss. To protect information assets and ensure business continuity, it’s essential to adopt robust strategies. Here’s a concise guide to safeguarding data during major IT transitions: Comprehensive Backup Strategy: Begin with a full backup of all data before starting the migration. Validate data before, during, and after migration by comparing source and target data. This ensures data consistency and completeness, helping identify and address any discrepancies early in the process. Whenever possible, migrate data in smaller, manageable increments.