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Nowadays, companies are adopting technological advancements to ensure better outcomes for business processes. There are multiple applications within the IT infrastructure, and these need to be monitored to ensure that there are no issues with the operative systems. Therefore, it is important to use AIOps. AIOps can help to automate and optimize systems. One of the primary tasks of AIOps platforms is to detect anomalies. Anomaly detection can help to create an efficient system.

What are Anomalies?

Anomalies are any deviation from normal activities. These can occur within IT systems when performance metrics are not as they should be. Anomalies can occur due to various reasons. They can be due to third-party threats, malicious activities, fraudulent activities, or system breakdowns. The latter usually happens when the system has not been optimized for a while. Anomalies can also happen during the addition of new data or data mining.

Anomalies can lead to significant losses for companies. If not detected at the earliest, they can lead to increased labor requirements and, thus, rising costs. When the system fails due to an anomaly, the business is likely to suffer a major loss of revenue. Therefore, it is important to detect and resolve these issues. However, anomaly detection is not a manual task. AIOps tools are necessary for quick and effective anomaly detection.

Businesses are implementing IT automation with AI and AIOps tools can also assist in anomaly detection. To flag anomalies, the detection models determine specific thresholds. These thresholds help to determine what should be considered acceptable performance and how current performances are deviating from those limits.

Two thresholds are set in anomaly detection. These are:

1. Static Threshold

As the name suggests, the static threshold is fixed. It is a set limit on how an application needs to perform. If there is any deviation from this threshold, or if the performance levels are not up to the mark, then there is an immediate need to resolve it.

2. Dynamic Threshold

A dynamic threshold is used more in AIOps anomaly detection. When AI tools are used for monitoring, they can set thresholds according to the current condition of the systems. The dynamic threshold may change with time or when newer metrics are introduced within the system.

Anomalies can occur within the IT infrastructure. AIOps platforms can check for anomalies in various metrics like CPU load, network IOps, disk IOps, memory, and load balancers. The AIOps tools will use algorithms to scale and redeploy services.

How Can AIOps Solutions Help to Detect and Resolve Anomalies?

Anomalies can occur in various IT infrastructures and systems. Therefore, businesses need to ensure that all processes are protected and equipped to deal with such issues. The best AIOps platform software will offer various solutions to monitor business processes, detect anomalies and resolve them.

Here are the different ways in which AIOps can help diagnose and fix anomalies.

  • Monitoring of Application Metrics

Anomaly detection is the constant monitoring and analysis of application metrics. When an application is running, there may be different types of anomalies that affect its performance. If such issues continue and are not resolved immediately, the application might fail. AIOps software monitors all applications to obtain metrics and uses them to improve the detection model and the overall system.

  • Introduction of New Metrics

New applications are added constantly to a system, and they need to be monitored and scaled. As new applications are introduced, new metrics will enter, and the detection systems need to be optimized. AIOps platforms can scale all new applications and then use new metrics to reconfigure the systems. This ensures scalability and helps to prevent any issues within the systems.

  • Prompt Alerts to Avoid Complications

An anomaly may happen at any time. But if the threat is not predicted, it can lead to complications. Disruption of business processes can cause a major loss of revenue. Therefore, AIOps solutions are necessary to predict anomalies before they happen. Most AIOps platforms have tools for setting up alarms. These platforms also monitor and analyze current and historical data to understand what causes anomalies. This helps to set up alarms that will alert the operations teams of any threats. Once the anomaly is detected, it can be quickly prevented before it can affect the system.

  • Tracking Metrics

To decrease the occurrence of anomalies and improve scalability, one needs to be able to track metrics. AIOps software will track metrics automatically, and this will quickly reduce the onboarding time. Data obtained from monitoring metrics can also give insights into how the anomaly detection model needs to be modified. This will increase scalability and improve the performance of systems.

  • Simplification of Root Cause Analysis

Anomaly detection is a complex process that includes investigation, scaling, and troubleshooting. This is possible through root cause analysis. AIOps software solutions can analyze the system and determine the root cause of frequent anomalies. Elimination of the root cause will help to ensure that similar issues do not occur again. AIOps software usually does not provide temporary fixes. These tools effectively reduce labor costs and ensure low reaction times to increase optimization.

  • Recalibration of Systems

Feedback from operating systems is necessary to understand what kind of anomaly detection is required. AIOps solutions that work at one point may need to be changed according to the system requirements. Thus, AIOps platforms offer calibration tools. These recalibrate the systems and help to modify the anomaly detection solutions when necessary.


Companies that have extensive IT systems need to invest in proper AIOps solutions. It is important to choose the right AIOps platform that will offer all the features. Operating systems can benefit from AIOps digital transformation solutions. These platforms have various tools for monitoring, scaling, and reducing discrepancies within the system. Through digital transformation and automation, businesses can utilize systems that are optimized and, thus, reduce response time and costs. Once the anomalies are eliminated before they can cause system-wide failures, business processes will become more effective.

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