AIOps: How AI and Automation Are Transforming Business IT

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AIOps: How AI and Automation Are Transforming Business IT

Current AIOps systems struggle to understand the relationships between applications, infrastructure, and other datasets. In the two or three years, expect that to change.

· AI Zone ·
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The technological revolution that businesses have undergone over the last few decades hasn’t been without its problems. Exacerbated by the growth over the last few years of the Internet of Things (IoT) and edge computing, there’s an exponential growth in the number of applications, services, and incidents that IT staff have to manage.

Data is also on an exponential trend upwards. According to IDC, by 2025, there is expected to be 163 zettabytes of data produced annually as the IoT takes off — ten times the amount we currently produce (16.3 zettabytes). This has dire consequences with respect to conventional technologies, which simply cannot keep up with the volume of data being produced and don't have the necessary context to analyze the inbound information streams in a cost-effective fashion.

Altogether, there are many more considerations and issues to which IT operations staff must respond to, but there’s a problem: there simply aren’t enough humans with enough time to do all the necessary analysis, error-resolution, and monitoring of infrastructure performance. Business IT is now more complex than ever. There’s too much to do, and too little time with which to do it all; at some point, IT staff are going to hit a brick wall.

The solution inevitably involves artificial intelligence: what takes humans potentially hours takes machines just a few minutes — with improved accuracy to boot. If there’s a repeatable fault, there’s a repeatable fix — one that can be solved much more efficiently by AI. Cue the birth of AIOps, a term coined by analyst firm Gartner to describe the growing trend of AI in the field of IT operations.

Exploring AIOps

Powered by machine learning, AIOps allows for the automatic discovery, analysis, and fixing of IT issues — all of which can be completed significantly faster than if put into human hands. A variety of IT processes are open for automation, delivering benefits across the entire IT environment.

What does this mean in practice? Take a memory leak, for example.

The noise of excessive data from multiple monitoring tools makes it incredibly hard to detect service issues, and even more difficult for IT staff to diagnose and fix them. If the problem is in production, it may mean allocating the developers in the team for an impossible-to-estimate period of time, forcing them to drop everything else they were doing.

AI is better equipped to both gather data and to filter out all the irrelevant pieces of data; the human mind simply isn’t designed to spot subtle patterns in huge volumes of data, particularly when that data is typically spread across a range of devices and applications.

With only humans in the equation, it may take hours to locate and solve a leak. AI may take just a few seconds. Liberated from firefighting IT problems, staff can undertake more proactive, meaningful work.

Looking to the Future

AIOps is a rapidly developing area. As things currently stand, AIOps allows for partial automation: some tasks are automatic, reducing the average time it takes to resolve issues, but humans ultimately still form an integral part of monitoring for issues and fixing problems when they arise. Current AIOps systems struggle to understand the relationships between applications, infrastructure, and other datasets.

In the two or three years, expect that to change. As automation technologies evolve, monitoring will become mostly or even fully automated. IT problems will also increasingly be solved automatically, resolved before businesses even notice there was a problem in the first place.

Looking to the future, as AIOps technologies develop and become more impactful in creating efficiencies, businesses will find it harder and harder to sustain their competitive edge with performance bottlenecked by outdated and manual work. By applying AI to IT operations, IT issues become easier to identify, predict, prevent, and even fix.

ai, aiops, automation, data analysis, machine learning

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