Fighting Alert Fatigue with AIOPS

We live in a world filled with alerts, buzzes, and notifications. Some of these are important, while others are trivial or could be ignored without unpleasant consequences. Our brains naturally develop a system to filter out all the noise and purposefully ignore most of them. The only issue occurs when we miss something that deserved an answer or taking action.

Alert fatigue affects IT development teams, regardless of their organization. In large, centralized organizations, it takes the form of countless broadcasted notifications (like being in the CC on a corporate email). In small or decentralized teams, it can become so overwhelming that it replaces actual work.

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Why is Alert Fatigue an Issue in IT?

Firefighting instead of meaningful work. When an alarm is ringing every couple of minutes, the focused workflows necessary to create, code, or optimize just disappear. This approach makes people more stressed, less effective, and has a negative impact on the outcomes.

Cut costs. A team affected by alert fatigue is more expensive since it has less productive time and produces less innovation. Such a team only responds instead of creating, and it doesn’t have time to put in place systems for the long run, thus making frequent re-works of the same things.

Slow work, longer release time. Every daily delay in fixing problems can have a snowball effect on the entire project’s schedule and postpone the release date.

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How to fix Alert Fatigue?

There are two simple answers, which are both a cost hazard. The first one is to bring more people to the team to deal with issues. The second one is to disable some of the current alarms.

The first one means adding to the recruiting and salary costs. In contrast, the last one is borderline irresponsible because it could lead, similar to alert fatigue itself, to ignore problems that could significantly impact the development process. Disabling the alarms is not realistic and is in fact what most of the time happens due to the complexity of the systems used nowadays in IT. Numerous tools and data systems all have different underlying issues that could trigger alerts.

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Is AIOps the future?

Like any other technology, AIOps can never be better than the underlying strategy since it is just a tool. When adopting AIOps it helps to have realistic expectations from this as well as from your team and expect resistance. However, as soon as the AIOps platform is in place and properly used it can have a significant impact on the KPIs, preventing bottlenecks, downtime and allowing staff members to be more productive and more creative since the routine is handled by the algorithm – and here our approach on NOC / SOC alert triage is just an example.

This is a natural evolution of automation in IT and the goal should be to allow high-level workers to focus on creating value instead of firefighting.

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How can AIOPS help with alert fatigue?

The real solution lies in building an optimized noise reduction strategy and delegating the alert severity analysis to an automated smart system. Downplaying the noise from too many alerts boils down to creating an effective method to highlight the real alerts from false positives or a simple warning. One way to do this is to use the power of artificial intelligence for IT operations, called AIOps.

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Identify the root cause

When thinking about alert triage, there is a delicate balance between filtering out unnecessary notifications and missing out on critical alerts. If such a warning is not visible, the operations teams could waste days before finding the problem and more days in solving the underlying issue.

When installing a system to detect the problems, this shouldn’t add to the work’s complexity but simplify it.

In an ideal setup, the system could scan the alerts, prioritize them by urgency, and also provide the potential root cause of the problem to the operational team. Also, since a single underlying problem could trigger multiple alerts, a smart system would eliminate duplicate alerts and only signal once if the root cause can have a real impact.

This approach also removes many false positives and fake leads generated when every point of the system is analyzed backward. This also means saving much time, which would be otherwise invested in a trial and error approach to solve the problem.

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Automatize current error identification

The advantage of using AIOps is that the entire error identification process is automated. It starts with retrieving data from across the IT infrastructure. Then it aggregates the data, and the algorithms start looking for anomalies and correlations. AIOps also performs the previously described root cause analysis. The IT department members get a bird’s eye view of the system’s problems instead of being overwhelmed with hundreds of notifications.

The approach and the high level of automation allow teams to collaborate to solve cross-department issues.

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Predict and prevent with AIOps

Having all that data in the system means that by analyzing it, there is also the opportunity to identify trends and make predictions. This kind of insight can then be leveraged to put in place preventive measures. This type of hedging will also reduce alert fatigue by only triggering fewer alerts due to better organization. In this setup, the AIOps takes on an essential part of the time-consuming tasks of analyzing cues.

Having fewer alerts means making better use of the information available and doing so in real-time, with as little human intervention as possible. As complexity increases, more notifications only become noise, and our limited processing capacity makes us insensitive or overwhelmed.

Prediction and prevention switch the way teams work from a reactive, firefighting approach to a more proactive and strategic development. It also saves much time since the AI handles the bulk of the work.

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AIOps – an efficiency and office wellbeing tool

Studies show that in most systems, not only in the medical one, fake alerts are more than 52% and that 64% are redundant. The numbers are even higher in IT systems. This situation is called alert fatigue, and it is one of the plagues of using inefficient and old tools to analyze the increasing data inflows.

Using AI to handle the operational side is a normal evolution of tech systems. It should be implemented in every department which has enough data to feed the algorithm. This approach does not replace humans; instead, it gives them more time to focus on the tasks which require their expertise instead of checking countless notifications.

Furthermore, although surprising, the AIOps can add an element of office wellbeing. Alert fatigue is a huge stress factor in most IT environments and taking this burden off the to-do list is a way to make them feel more relaxed and creative. Managers should always look for ways to save time while allowing people to do more focused work.