Top Use Cases of AIOps.

Apr 15, 2021. By Aleena Mathew

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Top Use Cases of AIOps

The evolvement of digital transformation is really at its peak. And seeing the potential benefits the change holds, every CIO's are moving into the adoption of digital transformation. The traditional IT operations were completely shifted to the digital era, and this brought in the replacement of conventional systems to advanced IT architecture. A research study shows that 89% of all the companies have adopted digital-first business strategy or are planning to do so. With this statistics count, it was clear that IT organizations can no longer survive with the traditional mode of operations.

But the happiness of this change or the shift to the digital era did not persist for long. The impact of the change started affecting the system. To cope with the digital transformation, the use of more IT resources came into the picture. But this led to the underutilization of resources. The IT operators needed to do a thorough check if every IT infrastructure element and resources are working or not. This just added up the operational cost and people's time to manage every resource. One other major concern with the digital transformation was a massive volume of structured and unstructured data generated. Collecting, manually analyzing, and managing every data set is a tedious task. This just led to a situation where several event noises were produced, and along with that, false positives. All of these just created big chaos in the system. So, the era of digital transformation just began to produce disorder in the systems. What is the right solution here? The implementation of much advanced and intelligent solution termed Artificial Intelligence for IT Operations (AIOps).

The AIOps Solution:

Modern problems require a modern solution, and in this picture, AIOps is the right solution. AIOps enables achieving the true power of IT automation. The implementation of AI can automate almost every IT operation. AI-based models are capable of handling every IT request and auto-remediating or auto-fulfilling them. In this way, IT ticket routing and resolving handles at a smooth pace. This is just one scenario where the true capacity of AIOps works. Listed below are some of the major AIOps use cases.

1. Multivariate KPI Anomaly Detection

With the evolvement of the digital era, data generation just multifold. There was a huge volume of KPIs to monitor from multiple sources. Manually monitoring these KPIs and identifying outliers from them was a tedious task. That's where AI-based observability came into the picture. The AI-based observability system was capable of ingesting a huge volume of data and correlate them among multiple other KPI's. In this way, intelligent analysis of multiple KPI is made possible. Based on this analysis, an AI-based anomaly detection takes place. The AI-based models are capable of performing multivariate KPI anomaly detection. By this, multiple KPIs will be correlated, and an efficient and intelligent analysis can be made.

2.Log Anomaly Detection

Much similar to the case of KPI, log data are also collected from the system. The AI-based models perform the log anomaly detection where the entire logs are collected and ingested into the system. The AI-based models will then perform the anomaly detection on these logs and proactively identify any unwanted log entry or log mismatch and intelligently alert the IT operator.

3.Incident Recognition

We have seen how KPI and log anomaly detection works. Now correlating them and analyzing and effectively finding unknown issues is the critical task. AI-based incident recognition works effectively here. The mechanism is capable of identifying unknown issues correlating KPIs and logs together. Based on this correlation, any anomalies problems will be identified intelligently by the root cause analysis. The root cause analysis mechanism will effectively point out and alert where the exact problem is.

4.L1 Helpdesk Automation

One of the major concerns faced by the IT organization is the handling of IT requests. Before the implementation of the AI-based system, operators manually needed to handle every IT ticket or request that was issued into the system. Due to the manual handling of each request, the IT operators were overloaded with tickets and were not able to handle them on time. This led to a situation where the SLA of the tickets were getting breached. AI-based helpdesk automation helped in resolving this by the implementation of AI-based virtual agents. The AI-based virtual agents can handle every IT ticket and resolve them based on the end user's needs. In this way, the SLA of the tickets won't get breached, and every ticket will get properly resolved.

5.Auto-Remediation and Auto-Fulfillment

We have seen how effectively we can file IT tickets in the system with AI-based models. The issues or anomalies found in the system will be automatically generated as IT incident tickets or IT service request tickets. The next task is to identify how to resolve them effectively. That's what the auto-remediation and auto-fulfillment phase of AIOps is all about. The AI-based models will work effectively to automatically schedule a workflow and resolve the IT tickets that have occurred in the system. In this way, there is no need for a 3rd party manual intervention to resolve the IT tickets. Every IT ticket will be handled effectively.

6.Patch Automation:

With the upcome of the digital era, the number of resources and applications used just multifold. All of these applications have their own version update that should be handled efficiently. The AI-based models are capable of handling these application updates and automatically updating these applications. In this way, every application update will take place efficiently, and the system will automatically initiate every node where the update should happen.

To learn more about Algomox AIOps, please visit our AIOps Platform Page.

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