Sep 1, 2021. By Aleena Mathew
IT organizations host multiple applications and resources to run their business day-to-day activities. Along with that most of the applications and resources are used by customers and partners also. At times, this usage can lead to situations where the applications are crashing down due to high load or user transactions thus making it difficult for users to access the system. There are also situations in which IT resources present in the environment may not function properly. All these situations make it difficult for IT operators to understand the issues that are causing the crashing of applications and also making it difficult to understand the root cause of the problem. Moreover, manual monitoring and managing these applications with large volumes of data are inefficient and time-consuming.
Apart from that, the IT team could not detect issues proactively, such as application performance downgrading, and so on. With traditional monitoring tools, the team could not detect anomalies at the point of occurrence. This created a lot of inefficiencies in the system. Furthermore, as these unknown issues persist without resolution, they lead to a much bigger problem. In this digital era, where the IT competition tightens day by day, holding back to identify the problem manually and resolve the unknown issues was not an option. Thats where artificial intelligence-based incident recognition stepped in to scale up the IT team operations.
AI-based Incident Recognition:
AI-based incident recognition enables the automation of detecting unknown anomalies from multiple applications. The AI-based incident recognition system automatically observes every incoming data from various applications and resources. This observation will proactively trigger and alert the IT team if any unknown event or anomaly has occurred in the system. In addition, the AI-based system will automatically ingest KPI and log data and trigger issues in them. That is, the incident recognition mechanism can correlate every KPI and log metrics to identify the root cause of the problem. The identification of the root cause of an issue is another big advantage of AI-based incident recognition. With traditional monitoring tools, the IT operators could identify what the issue was after hours of occurrence. They were not even able to diagnose the root cause of the problem. But, with the implementation of incident recognition, the root cause of the problem is identified. The AI-based system will say which all metrics lead to an abnormal situation and due to which reason such an issue occurred.
Auto-Generation of Unknown Incident Summary:
The above paragraph mentioned how the incident recognition mechanism can detect unknown anomaly scenarios. That is proactively detecting an abnormal situation in the system and intelligently alerting the IT team. Therefore, with AI-based incident recognition, abnormal incidents are automatically detected. There is one more advantage with incident recognition which is auto-generation of incident summary. Along with the event detection, the AI-based system is capable of automatically generating an incident summary report which contains a detailed insights about the unknown event. Auto summarization enables an IT operator to understand the exact root cause, that is, in the case of KPI anomaly, the description will contain the exact metrics that faced the issue. As in the case of log anomaly, exact log messages will be shown. Thus enabling the IT team to understand the problem and resolve the issue proactively. Let's see with the help of an example how incident recognition mechanism identifies an unknown performance issue with an example.
Example of Incident recognition detecting unknown performance anomaly:
Let's consider a situation in which an organization's daily work depends on the applications used by the customer. But at some point, high traffic or abnormal amount of transactions occurred in the applications. This makes it difficult for the users to access the applications as they note that it is not accessible. Behind the screen, the IT operators are fire-fighting to understand what exactly the problem is. But, unfortunately, they are not able to diagnose the issue. But with the implementation of AI-based incident recognition, the IT team was proactively alerted of the application performance issue. This was an unknown performance issue of the application; that is, the performance was affected due to a high load of user transactions. The AI-based system automatically identified the issue with its root cause, stating which KPI metrics were affected. This detection enabled the IT team to resolve the issue before it affected the end-user.
The above example shows how efficiently end-to-end monitoring and detection of unknown issues can be achieved with the help of AI-based incident recognition. This implementation helps to improve the IT team efficiency and thus to improve business growth.
To know more about the Algomox Incident Recognition mechanism, please visit our page AI-based Incident Recognition