How AIOps Helps Stop Alert Fatigue

Alert fatigue can lead to frustration, misuse of labor time, and disorganization in a DevOps team. Without the application of a well-constructed alert handling solution, alert fatigue will end up taking away from the focus of a good DevOps team: solving problems, reducing downtime, and ensuring a smooth end-user experience.

This blog explains how AIOps helps DevOps teams avoid alert fatigue.

Reduce the Manual Load of Aggregating, Routing, and Classifying Alerts

While eliminating alert fatigue is critical to improved focus, DevOps teams risk losing valuable time solving serious problems if they also have to create and/or add systems to properly aggregate, route, and classify alerts. Maintaining this system would be a source of unnecessary labor that may solve the problem, but it also reduces the amount of time that DevOps staff can work on more pressing matters.

This brings up another consideration that a team has to make. When coming up with a plan, the amount of complexity involved in traversing all of the monitoring tools necessary to cover an entire application and/or service can be equally laborious to not having a solution at all. At this point we may be merely shifting the labor emphasis.

Reducing alert fatigue across the toolchain often involves teams:

■ Using incident-response tools instead of email threads to manage incidents

■ Preventing repeatable issues

■ Creating and implementing standards for cross-team collaboration

■ Identifying team members that can aggregate, route, and classify alerts

■ Identifying a team or team members to analyze alerts across the toolchain and report

■ Manually creating predictive models and solutions based on the steps taken

Before a team can even get to proactive issue detection and root cause identification, there is a lot of work that needs to be done. This work to reduce fatigue ironically causes more fatigue, low morale, and ineffective use of DevOps resources. Thankfully there is a solution using AI and machine learning.

AIOps Can Help by Automating Processes to Reduce Alert Fatigue

The power of AIOps is in the utilization of machine learning to effectively:

■ Analyze alerts and collected monitoring data

■ Aggregate multiple alerts into related events and contexts

■ Reduce the need to manage alert thresholds

■ Reduce alert noise

■ Increase speed in root cause identification and analysis

■ Deliver predictive insights to remedy persistent and/or low priority alerts

■ Automate issue remediation

AIOps can effectively manage the scale of issue resolution and reduce human labor. The holistic manner in which AIOps traverses the toolchain and application can reduce alerts overall through rapid remediation of issues, and can also deliver key insights to a human team to get to the root of a critical issue and its cascading effects on the entire system.

Alert fatigue, at its root, comes from the inability to manage alerts in a timely manner. This is not only a question of volume. A lack of timely problem-solving and long hours in alert and data analysis, solving the problem, and creating documentation and reports for prediction and prevention also create fatigue.

A team should get to a point where they can appreciate an alert the same way that a software engineer appreciates an error. DevOps teams definitely want to solve problems. Leveraging AI reduces unmanageable volume and ambiguity so that teams can solve more problems and work to maintain a solid working application. In automating key processes, AIOps can help reduce alerts, not only by volume, but by also automating a workflow to help teams effectively detect and remediate issues.

There is still a lot of work to be done, both in the implementation of AIOps and in the machine learning necessary to deliver the expected results to help alleviate the many problems that can arise as applications and enterprise software scales and increases in complexity. It is clear that this is a needed step in the evolution of DevOps. Humans are asking for these solutions — There are only so many hours in the day, and a magnitude of issues arise in a complex piece of software. Delivering a great customer experience while taking care of human resources should be the goal, and using AIOps to reduce alert fatigue is a step in the right direction and should be considered.

For more information on AIOps, including use cases and a definitive guide to AIOps, visit this link. There is also a Gartner report on AIOps and digital experience monitoring that you can get here.