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AI’s Increasing Role in Data Backups

In this special guest feature, Steve Blow, Technology Evangelist at Zerto, takes a look at four ways in which the combination of machine learning and IT resilience can have a profound impact on the way the technology and IT industry operates. Among other things, Steve is responsible for both helping customers succeed in their digital transformation journeys using Zerto’s IT Resilience Platform. He has a particular interest in automation, particularly all things API. Using his passion for technology Steve works with clients to help optimize their IT strategies, and make sure customers understand how Zerto can bolster a mobility, backup or disaster recovery strategy. Steve’s main initiative and biggest focus is on driving and ultimately seeing real improvements come to fruition for organizations. He has over 14 years’ industry experience in IT solutions architecture, design, engineering and support.

Today, the average person has at least a general understanding of artificial intelligence (AI). With the buzz around self-driving cars; Amazon Go stores and the frequent use of Siri, Alexa and Google Home, AI is front and center in our everyday lives. But overt, in-your-face AI use isn’t what most of today’s IT professionals contemplate whey they think about AI applications. What many people don’t realize, yet what the IT community thinks about daily, is just how deep AI can go and how AI applications can have an impact on almost everything.

What does this mean when we start to think about addressing some of today’s biggest and scariest technology challenges such as security breaches, ransomware and safely moving data in today’s cloud-first environment? A lot of this relates back to backup, recovery and IT resilience as a whole. According to IDC’s recent “The State of IT Resilience” report, “The emergence of nontraditional data types requires innovative backup and recovery methodologies. Application data, machine learning data and data gathered from sensors — ranging in format from structured to unstructured — will all be relevant to an organization’s IT resilience strategy, creating an ongoing data management and visibility challenge.”

So, what are some ways in which the backup and recovery space – combined with AI – can address some of these challenges? Most of the applications likely fall within the machine learning category. While AI involves techniques that enable computers to mimic human intelligence, machine learning is a sub-field of AI that enables machines to improve tasks with experience.

Let’s look at four ways in which the combination of machine learning and IT resilience can have a profound impact on the way the technology and IT industry operates.

Moving AI-Reliant Data

As businesses engage in more complex data opportunities, like AI, they need to first ensure their IT infrastructure is robust, flexible and secure enough to do so. This all falls under creating a resilient environment: an IT environment that ensures access to critical applications at all times with zero disruption to business operations or to customers. This becomes especially relevant as we talk about the tremendous amount of data needed to make machine learning work.

Let’s take Amazon’s new grocery concept, Amazon Go, for example. Amazon Go is a shop Amazon is testing in Seattle that uses a combination of camera images and sensor and mobile application data to allow shoppers to enter the store, grab what they need and leave without ever interacting with anyone – mobile payment happening automatically. Let’s say that Amazon needed to migrate some of the data used to create the algorithms that make Amazon Go work? With such crucial, machine learning-reliant data, a resilient infrastructure can be the difference between an Amazon Go customer that has no idea a migration took place, as it should be, or an Amazon Go customer that reads all about it in the news the next day because the migration caused major glitches at the store and even more speculation about Amazon’s concept.

Automatically Balanced Resources

Most companies today run constant failover tests to ensure their disaster recovery strategy works as intended for when they find themselves in a real disaster situation. However, a common challenge IT organizations grapple with is having enough storage to continually run these failover tests; tests that involve duplicating and moving data over and over again. With machine-learning-based prediction tools, businesses will be able to see expected storage growth rates and be automatically provided with relevant options. Click this button to see ‘what if scenarios’ involving moving data to different points, click this button to see how much it would cost to move the data to different cloud platforms and/or choose one to move to, click this button to see how much storage would be saved by removing redundant data. Or, taking things a step further, if an organization has machine learning that’s advanced enough it can act on pre-determined preferences and automatically balance storage resources based on an algorithm that factors in available space, cost, previous manual decisions and more.

Hands-Free Disaster Recovery

In a perfect world, when a business encounters a disaster, its disaster recovery system does its job automatically, quickly and better than any human could do manually. We’re not there yet, but it’s coming. While automatic disaster recovery sounds appealing, the machine learning still needs to get advanced enough to understand when, for example, a network is only down for five minutes, and a complete resource-intensive recovery is most certainly not necessary and not worth it.

That said, with the right algorithms and variables, and enough data to help machines determine when it’s right to wait five minutes, for example, before performing a failover, a hands-free disaster recovery system is a possibility. The ultimate goal with this kind of advancement is less downtime for businesses and customers. The idea is that machines, once the kinks are worked out with enough algorithm-based trial and error, will always perform better, faster and smarter than humans. This can equate to disaster recovery processes that happen so efficiently that even an unexpected tornado hitting a 200,000 square foot datacenter means absolutely nothing to the businesses the datacenter serves.

Security Integrations

As machine learning advances, the line between security and recovery blends more and more. Machine learning can help security and recovery systems work together to make better IT decisions. Let’s take ransomware for example. Today, before ransomware makes itself known, the virus encrypts a large quantity of data to have the leverage to hold a person or an organization’s data ransom. A security system equipped with advanced machine learning might be able to detect the encryption taking place as it happens and then automatically connect with a recovery system to both stop the attack as it’s happening and failover to unencrypted data. This is one of the most exciting and promising ways in which AI and IT resilience can come together. This scenario will be a reality in the near future, and eventually, with the rate at which cyberthreats are growing, no business will be able to survive or compete without this kind of protective technology in place.

These are just a handful of potential AI-based scenarios in the world of backup and recovery. There are certainly others, many of which we likely won’t even discover for years to come – such is life in this new, evolving, data-driven world. But as in all facets of IT, what we know of backup and recovery today will look completely different in only a few short years. It’s true that we’re only at the beginning of what’s possible, but the speed at which innovation is advancing increases more and more each day, and the implications for how an organization can better protect and preserve its data can equate to a future where the use of the word ‘downtime’ becomes virtually obsolete.

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