AIOPS
Implementing AIOPs in 5 simple steps IT Operations teams have faced mounting challenges as the tech stack has both grown in size and complexity. BY MICHAEL PROCOPIO, PRODUCT MARKETING MANAGER, MICRO FOCUS NOW HAVING TO OVERSEE data distribution and accessibility, applications, varying interdependent infrastructures, customer touchpoints and more, keeping these plates spinning has become a monumental task. Ticket management alone can place overwhelming pressures on teams, where sifting through the sheer noise can be incredibly resourceintensive. Fortunately, for IT Operations teams, IT management solutions have matured in line with these challenges. Around 2015, the idea of IT Operations Analytics (ITOA) crystalised. The application of big data analytics to raw IT operations data unlocked the ability to analyse huge amounts of information. This would, in principle, allow for more informed and focused decision making. At the time, however, the tools for doing so were cost-prohibitive and highly specialised. Fast forward a couple of years and deep learning, machine learning and AI are at the top of the Gartner hype cycle and AIOps enters the lexicon of cutting edge CIOs. The idea of combining AI, machine learning and automation with big data analysis on
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ISSUE II 2021
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this ITOA data frames AIOps as the ideal extension of the ITOps team. Although at the time, the “how” was somewhat missing. Now, with commercialised intelligent systems and white-label solutions widely available, AIOps helps leading enterprises manage their IT estates and streamline workflows. With this market maturation, we’ve seen more technical deployments such as “multi-domain AIOps”, differentiating between domain agnostic vs domain specific solutions, and the concept of an AIOps overlay of existing tools emerge. At its core principles, however, AIOps always comprises of three tenants: the ability to Observe estate performance, Engage with IT personnel, and Act through automation and remediation capabilities. With these abilities, vast, complex, hybrid environments are able to be managed relatively simply. And yet, despite its proliferation and market growth, adopting AIOps solutions still sounds like a job for an Oxford data science graduate. It isn’t. With the right
