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.
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.
