CIOs have been charged with a difficult mission. The CEO and board of directors expect the CIO to provide higher service reliability, faster time-to-resolution for issues, fewer incidents that affect business operations, and an increasingly efficient IT department that can do more with less. To achieve these goals, CIOs are turning to AIOps, a method that uses artificial intelligence (AI) to reduce noise, accurately identify potential issues and their causes, and even automate a significant portion of resolution tasks.
But AIOps must stand on a solid foundation of specific tools and practices before the IT team can put it to use. It’s a significant project to lay this groundwork, so it carries a fair amount of risk. But it’s fair to say that without AI capabilities CIOs will never meet the ever-growing expectations of the business. On their own, IT practitioners can no longer effectively manage ever-increasingly complex IT environments, which can span multiple clouds, locations on the edge, colocation service providers, and enterprise data centers. The stack has become an intricate web of interdependencies, not all of which are well understood. Finding the root cause of an existing issue can be extremely difficult given the immense quantity of information that an IT team member must analyze. What’s more, reliably predicting future issues so they can be nipped in the bud is essentially impossible.
AIOps can rapidly analyze this large volume of data to uncover patterns and relationships that a human wouldn’t see. Then, it can point an IT practitioner to the cause far faster, significantly reducing mean time to resolution (MTTR). And in many cases, AIOps can fix the issue automatically, enabling IT teams to focus on more complex problems.
Think of it this way. With a few simple tools and some very basic training, anyone can make some broad, short-range predictions about the weather from their backyard based on atmospheric pressure, humidity, temperature, cloud formations, and the time of the year. But someone in this position is unlikely to predict a blizzard five days from now or the potential for tornadoes. The reason? There’s a lot more weather data available that a professional meteorologist (especially one armed with software designed for analyzing weather data) could use to make detailed forecasts for anywhere from an hour to five days out.
AI-powered discovery works in a similar way by examining what elements are talking to other elements, the topology, the service models, and much more to understand the entire picture. But to do this, the discovery tools must integrate with other tools and systems to access as much data as possible. Just as a shipping manager would be unable to determine when a package would arrive without communicating with UPS, AI can’t properly pinpoint why a service is down if it can’t communicate with the tools that manage it.
The ability to pull metrics and logs from many different sources — such as multi-cloud and hybrid cloud environments, containers, and databases — gives AIOps a view of the entire enterprise environment that is both broad and deep. This enables it to pinpoint root causes of issues with startling speed and accuracy.
But identifying root causes is just the tip of the iceberg for fully integrated AIOps. It also enables teams to accelerate innovation.
BMC Helix Discovery provides comprehensive visibility into software, hardware, and dependencies across multi-cloud and hybrid cloud environments. Lightweight, agentless, and scalable, this software-as-a-service (SaaS) tool is ready to run in a hybrid cloud or on-premises environment, depending on what works best for the enterprise .
Want to learn more about how BMC Helix Discovery can provide you with the visibility you need for AIOps? Experience it first-hand with a guided demo.
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