Data is the lifeblood of modern business, and many organizations simply cannot survive long if they lose it due to data breaches or hardware failures. And with the rise of machine learning and big data, large volumes are required to stay competitive in today’s economy. Unfortunately, this means that recovering after a data loss is more difficult and risky than ever.
The good news is that AI can provide more reliable data recovery methods, improve data quality and reduce recovery time objectives (RTOs).
How can AI improve the data recovery process?
AI, machine learning, and automation tools have a part to play at every phase of the data recovery process. This includes before, during, and after a data loss event. With AI-integrated tools, organizations can prevent data loss events from occurring, mitigate their damage in real time, and accelerate and improve the recovery process.
Here are just a few ways they accomplish this:
Predictive analytics
Utilizing large volumes of data that includes historical data patterns, performance metrics, and hardware capabilities, AI models can forecast data loss events. Predictive analytics can provide guidance on when a storage device will fail or when data backups will become corrupted, giving users time to address the problem. By knowing when to replace aging systems or perform maintenance, you can avoid data loss events altogether.
AI-powered data analysis
Data loss events are rarely total, so organizations need to scan their databases and storage to find what’s affected and focus recovery efforts. Automated analysis and intelligent data classification tools can complete this task in a fraction of the time it would take humans to inspect everything, improving efficiency and minimizing RTOs.
Instead of doing a full inspection or simply recovering all of their data, organizations can focus their efforts and prevent costly prolonged downtime.
Adaptive data recovery
Recovering lost data is not as simple as pressing a “recover” button. Every data loss event is different, and each has an optimal solution and recovery process that will maximize speed and efficiency.
AI-powered recovery tools can analyze the incident, contextual information, and historical/simulation outcomes to determine the appropriate recovery strategy for a given event. This not only reduces the time and resources needed to recover data, but also provides valuable new data that the AI can use to improve performance next time.
Smart data reconstruction
In many data loss scenarios, data is not simply gone, but corrupted beyond use. This data must be pieced back together and re-integrated into structures and databases. Obviously, this task is tedious and time-consuming when done manually.
Machine learning tools can quickly analyze patterns and relationships between data, and can even reconstruct multiple file types in different data formats. By using AI to reconstruct data, both speed and accuracy can be improved to reduce the risk of future issues.
Simulations, reporting, and training
After a data loss event, it is vital to collect data on its cause and the effectiveness of the recovery process to improve RTOs and prevent data loss in the future. This data can be used to generate reports as well as simulations and training materials for stakeholders to improve performance. AI tools can assist with all of these tasks, not only collecting large amounts of performance metrics but also converting them into actionable data.
Want to improve your data recovery and business continuity processes with AI? Ask the experts
outsourceIT employs an experienced team of IT consultants with extensive knowledge of both business continuity planning and custom AI tools. We can put together a personalized plan to implement data protection and AI-powered recovery tools that minimize the risk of devastating data loss events and accelerate your recovery.
