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Key Takeaways Companies can no longer treat data as endlessly renewable. We’re facing a “data liability gap,” — the difference between the data you think you can access and what you can actually recover in a usable format.
AI systems depend on complete historical datasets to learn and correct their mistakes, so lost or corrupted data can lead to flawed or incorrect conclusions.
Many executives assume cloud availability equals data protection. In reality, cloud providers run the service, but partners and customers still own data protection and recovery.
Over the past several years, the corporate world has adopted the mantra that data is always renewable. Basically, people have treated storage as a utility and bandwidth as something that will always be there. Backup was viewed in a similar way to insurance. Since the emergence of artificial intelligence, all of this has been proven to be false. As companies now rush to use AI and predictive analytics, terrifying possibilities are arising.
We are currently facing a “data liability gap,” which is the difference between the data a company thinks it can access and what it can actually recover in a usable format. With AI systems being very dependent on old data to learn and correct their own mistakes, permanent data loss is no longer just an operational hazard; it is now something so serious that it may have to be mentioned in year-end reports. If it was lost due to negligence, the staff responsible could be fired due to the reputational risk to the business.
For generations, the C-suite viewed data protection as something akin to data recovery. They aimed to get the systems back online as quickly as possible after the main operational equipment went down. The concept of Recovery Time Objective (RTO) was something that focused on speed before anything else. The most important thing it aimed to do was get the servers back up and running.
AI has changed the game completely. Rather than caring about how long your systems are online, AI systems care about historical data. An AI language model will face severe problems if it is discovered that records from the company’s first five years of existence have been destroyed or corrupted. This will mean that its predictive algorithms will lack vital historical data needed to draw conclusions. In the worst-case scenario, it will make misleading or totally wrong conclusions.
Unrecoverable data could cost you heavily
Many CFOs will agree that data is the essential raw material needed in the AI industry. Data integrity is also important and a key backbone of keeping things running. A manufacturing company would suffer heavily if it found out that a small amount of its raw materials from its warehouse had been destroyed. If this happened, there would be a serious investigation and an adjustment to the company’s overall value.
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