End User Experience Monitoring with AIOps.

Apr 22, 2021. By Aleena Mathew

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End User Experience Monitoring with AIOps

Most organization's success factors rely on the customer satisfaction and customer experience of their business services built on top of their IT applications. If the user experience is poor, this indicates that there is something wrong with the organization and, they need to improve. But the question that arises here is, how fast can we identify any issues from the end-user side? How quickly can we resolve the issues that are reported? Is there any pro-active mechanism to identify issues pre-hand?

Challenges Faced with End-user monitoring:

Some of the challenges faced in this area are providing the right quality of service for end-user and continuously monitoring the application performance. What many IT operators fail here is to foresee every application's performance in a unified manner. In this era of digital transformation, the use of applications just multifold to a great extent. This led to a chaotic situation in which traditional monitoring tools could not handle and monitor all these applications in one stretch. End-user experience monitoring is one of the key capabilities of an end-to-end application performance management stack. There was a lot of noise and false positives that were generated, which led to situations in which IT operators could not handle out user requests. Eventually, the MTTR started to shoot up to a drastically high rate. All of these were uncontrolled situations in the organization which needed to be handled out efficiently.

Introducing AI in End-User Experience Monitoring:

In this era of digital transformation, the use of applications is on a large scale. Moreover, meeting and fulfilling customer requirements is a critical factor for any business organization. But in this era, IT operators find it challenging to address all the IT requests that led to high MTTR. That is where the role of AI can into play. The application of AI in end-user monitoring drove things from a new angle. The implementation of AI helped in automating most of the IT tasks that were done manually. In this way, IT operators did not need to take in unwanted headaches and let the AI-based systems do their work. Let's see some of the areas where AI-based techniques helped in automating end-user monitoring.

AI-based Observability:

One of the major setbacks with end-user monitoring is collecting and analyzing every IT data and getting inference from them. The implementation of AI-based observability helped in overcoming this with the help of AI models. The AI-based models were capable enough to ingest every IT data and proactively identify and alert the IT operators in case of unknown issues. It helped automatically capture errors, crashes, network requests, page load details, and other metrics. This enabled the IT operators to provide an excellent customer experience and proactively resolve issues before impacting the end-user.

Application Performance Monitoring:

As the number of applications starts to multifold, the need for monitoring and observing every application is a real must. For providing a seamless end-user experience, it is a must to analyze and track all these applications in a unified manner. That's where application performance monitoring comes into play. APM helps in monitoring every application in real-time in a unified manner. Every application will be observed and monitored, and a proactive analysis will be made in order to understand if any applications are down or slowing the user experience. In this way, issues triggered from any application can be cleared pre-hand before even affecting the customer side. This eventually helps in providing a unified method of monitoring and analyzing every application. This enabled the IT operators to focus on much more important activates.

Cutting down MTTR:

Reducing MTTR was one of the most significant concerns faced by IT operators. With the pile of IT issues/requests, they were not able to understand the real issue and resolve the IT request before breaching the SLA. The implementation of AI helped here. AI-based models were capable of automatically resolving the IT request that the users issue. AI-based systems effectively capture the issues, identify the root cause and fix them automatically. In this way, the MTTR can be reduced at an extreme level.

The AI-based implementation of end-user experience monitoring helped in gaining proper end-end visibility across the entire application platform. By this, customer issues can be resolved with excellent quality and higher priority as businesses won't get affected.

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

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