Roughly a decade ago, cloud became the must-use IT buzzword. Practically overnight, it seemed that every IT product suddenly became cloud-integrated, cloud-enabled or cloud-centric. Today, we are seeing the same thing happen with artificial intelligence.
AI has become the new must-use IT buzzword, and it is everywhere. As was the case with cloud, AI works better for some things than others. One of the areas where AI may ultimately prove to be useful is in data protection.
There are a few different ways in which AI and data protection might ultimately work together. One possibility is that backup-level AI could enhance business analytics. The big data revolution -- another once-trendy IT buzzword -- was based on the idea that an organization could derive hidden business insight from seemingly mundane IT data. Although this idea was once completely theoretical, AI has succeeded in finding trends hidden within an organization's data.
The more data that this type of analytical engine has access to, the more effective it will ultimately be. An organization's data protection systems contain copies of nearly all of its data. As such, it is conceivable that, with AI and data protection software in the future, APIs will enable backup data to be analyzed by an organization's business analytics software.
An organization could also use AI as a tool to ensure it is meeting data protection requirements. If, for example, an integrated AI engine detects that a certain service-level agreement is coming up short, then the engine could readjust the backup software's priorities in order to correct the problem. Although this AI and data protection idea may sound completely theoretical, it is something that Commvault has been working on for a while. Veritas is also using AI in its backup product line.
An AI engine baked into the backup software could conceivably learn a lot about the data that the organization is protecting and about the way that data is recovered. The AI and data protection software might, for example, learn which data sets have the highest change rate, which backup targets have the best performance and reliability, and what types of data are restored the most often. By using this information, the AI engine may be able to design an optimal backup strategy that places data on targets based on the target's performance and how likely it is that the organization will need to restore the data.
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