In its article 10 Rules for Condition Monitoring, Plant & Works Engineering claims that “The best condition monitoring device ever invented is man.” They might be right, to a certain extent, but AI and AIOps are set to take an expanding role in the future of IT monitoring and alerting. Nothing compares with the experience of an engineer who understands the nuance of a system he’s been monitoring for months or even years, but AIOps can examine and supervise on a scale that is far beyond the capabilities of man.
By using AIOps-driven alerts, errors can be caught before they become service-affecting issues. Forward-thinking scenario modeling can map out new demands on systems and identify where bottlenecks might occur. This will give technicians the ability to proactively address potential problems.
Today’s increasingly complex IT environments are a jumble of hardware and software systems. According to a report, “Seventy-two percent of IT organizations rely on up to nine different IT monitoring tools to support modern applications.” Those are just the monitoring tools. Throw transactional, CRM, BI, CI, digital marketing, operational, analytics, and other AI and ML software into the mix and things quickly spin out of control.
AIOps tools include sets of specialized processes and procedures narrowly focused on specific tasks. These algorithms can spot errors in noisy event streams and mitigate them before they negatively impact a service. They can also identify correlations between issues, use historical data to spot reoccurring problems, and then address these issues immediately.
One of the biggest problems facing an IT Ops team is operational noise, which can increase IT costs, while also lowering performance. AIOps can bring organization and order to jumbled IT systems while eliminating or severely reducing operational noise.
IT noise can create severe problems for the business, including higher operating costs and lower performance. Analytical models build on noisy data can make them almost worthless. An AIOps tool can help eliminate noise by creating correlated incidents and workflows that point to the probable root cause of a problem, thereby giving IT personnel a roadmap to any data issues.
AIOps is about data-driven decision-making and its predictive analytics capabilities can be used to forecast probable future events that might impact availability, performance, and/or problems. Predictive analytics can help with capacity planning, which is concerned with adding CPUs, memory, and storage to a physical or a virtual server to ensure the IT estate is used in the most optimized way.
By leveraging the vast troves of data in its systems, AIOps can build predictive models that will proactively address any issue that might negatively affect the business. These will be far more complicated and effective than any models humans can build alone.
AIOps can lead to increased collaboration amongst peers and business units. AIOps gets everyone on the same data page, so they can all speak the same data language. AIOps can improve remote workflows, as well as empower virtual workspaces. “In this virtual workspace, everyone involved in solving an incident assembles, sharing a ‘single pane’ UI with all necessary data and context,” explains Arsalan Lari.
AIOps can quickly identify and resolve system issues across a vast network. When properly implemented, an AIOps platform can reduce the time needed by IT personnel to focus on routine, everyday alerts. Time is needed to program the AIOps platform, but then it can be automated so that necessary alerts can be sent without human interaction to departments that need them.
Today, AIOps is used to avert problems, reduce operational noise, decrease downtime, increase the use of predictive analytics, improve customer experience, provide more accurate root cause analysis, and, ultimately, free IT personnel to concentrate on what they do best – innovate. AIOps elevates the strategic importance and visibility of IT to the business by improving the performance and availability required, no matter how complex the environments become.
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