How do you handle data recovery failures and learn from them?

There are potentially a number of reasons while data recovery may not be possible. These include: - Data corruption (accidental). - Hacking corruption (deliberate). - Hardware failure. - Storage media integrity (e.g. magnetic tape deteriorates over time). - Power failure (at the wrong time), etc. This is why you need a resilient backup process. To ensure you have the ability to restore backed up data. Using immutable backups may help In that they use a Write Once Read Many approach. Though you need to ensure the validity of the data being backed up. So, you need to have an end to end disaster recovery/ restoration solution that is validated on a semi-regular basis. Along with data integrity, security, etc., policies and procedures.

Data recovery failures can occur due to various reasons, such as hardware malfunctions, human errors, or software bugs. If using an AWS Ecosystem; here is how we could enhance the data recovery success rate. Use AWS S3 versioning and AWS Glacier for data backup. Implementing cross-region replication using Amazon S3 can mitigate regional outages. Use CloudWatch for monitoring and AWS CloudTrail for auditing to identify failure causes promptly. In the aftermath of a failure, conduct a thorough post-mortem analysis using AWS Config to understand root causes. Use Lambda to automate and validate recovery processes. Continuously iterate on recovery strategies based on lessons learned, and deploy AWS Backup for centralized management.

Failure to recover data, from physical or cloud-based storage appliances is often down to the lack of skills and training around how to restore data, and the processes to activate to notify and mitigate risks to business continuity. A data recovery plan should be created and reviewed periodically, and this should dovetail into an incident management or disaster recovery strategy. Not to mention, there should be a full smoke test of this plan at least once a year (but the more frequent the testing, the more recovery readiness you will accumulate). Sometimes the mistakes in data recovery can be down to outdated or insufficient training on recovery strategies, tooling, and poor knowledge of the data you are attempting to restore.

Data recovery can fail for numerous reasons - Corrupted Backup, Missing incremental backups, Incompatible Recovery Mode, Physical Storage Issues, Insufficient Resources, Software Bugs and many more.

Regular backups, verify/validate backups, monitor database health (like integrity checks). Test and refine recovery plans, utilize dedicated recovery tools, Automate recovery processes. Keep software updated, Document procedures, continuously learn and adapt.

In addition to doing regular backups you need to: - Validate the integrity of the data being backed up. - Validate the viability of the data restoration process(es). - Ensure the security of the backed up data. So that it can not be compromised by hackers and/ or be used in a way that breached data protection legislation, etc. - You have restoration (partial (focused) disaster recovery)/ full disaster recovery processes in place. You will need to validate the integrity of the backed up data and undertake disaster recovery tests on a semi-regular basis. So that you ensure you have the ability to restore your services, etc., as/ when required. Remember a good disaster recovery process requires resources from a lot of different teams.

My top tips for increasing data recovery success rate: 1. Regularly check and maintain your backup systems to ensure that you are A) Identifying and backing up your mission-critical data, and B) Your backups have full integrity in the data itself to prevent errors or missing data values. 2. Document and define a clear, concise, and action-orientated Data Recovery Plan - and ensure all team members needed for this plan understand their role to play in a data loss/corruption event. 3. Test against data loss and corruption at least once a year, or more frequently. Either have a maintained list of test scenarios to validate or consider chaos testing in a test environment for a more life-like experience of data loss/corruption recovery.

Performing an After-Action Review (AAR) session after a data loss or corruption incident is crucial to ensure: 1. Error Logs are analyzed as to why data could not be restored properly, and what prevented success 2. Allow a team to openly and honestly discuss what happened and how to improve or mitigate for the future. 3. It's also a good practice to document 'Near-miss' incidents. This is useful because if you identified an item which was particularly risky in the restore, but was averted/mitigated - it can be used as a case to build resilience and as a learning moment. 4. If new or unexpected items have been found during a recovery, it is important to update your data recovery and incident management plans as appropriate.

Analyze error logs and event history, Evaluate the recovery process, reflect on planning and execution, Document lessons learned, share knowledge and adapt procedures, Benchmark against best practices.

You should always undertake semi-random (as opposed to fixed interval) disaster recovery/ restoration tests. This is to validate: - Your data backup processes are working correctly. - You are able to restore selected/ all of the data from backup if/ as required. - That the backup/ restoration process is able to accommodate changes that have been made to in the infrastructure since the last test. - Updates to the disaster recovery plan, etc., is/ are made to accommodate changes, that have been made, etc. - All IT teams gain an understanding of their roles and responsibilities in a disaster recovery scenario, etc. Using the above details a formal, well managed restoration/ disaster recovery process can be formalised.

Identify the cause, Assess the damage, Prioritize recovery, Restore from backups, Data repair tools, Communicate clearly, Minimize downtime, Evaluate and mitigate risks, Review and document.

When a data loss or corruption occurs, this can damage a business financially and reputationally, but if can also lead to poor mental health in those involved with the recovery process if things are not successful or complex. In data recovery scenarios, you should never be in a position where you recover data alone; you should assign a team to share the tasks and responsibilities to ensure full success. Involving more team members in such an incident can be an advantage as you can also cross-validate work before, during, and after the recovery process. It is also good practice to avoid silos and bottlenecks forming; allowing under-pressured teammates to take regular breaks and to provide emotional and professional support/encouragement.