Network and application performance monitoring has become the de facto standard for meeting service delivery expectations for enterprise IT. But if IT operations personnel aren’t seeing the same picture as the user, how can they see eye to eye?
Enterprises are investing in their networks at high scale. As a legacy, IT on-premises infrastructure gives a path to virtualized environments and hybrid cloud, and an escalating data tsunami drives, data center expansions, increasing investments of money and time are raising the stakes even higher. Unfortunately, end-users’ expectations for service are increasing as well, piling additional demands onto network operators and engineers who are already fighting with network migration challenges.
Yet even though the corporate networking environment is rapidly changing, IT support engineers and teams are still using the identical network performance metrics to monitor their networks and evaluate whether or not service delivery is up to par. The bother is that they’re utilizing a one-dimensional tool to measure a subjective experience that tool was not designed to even know, much less assistance in troubleshooting. It’s sort of like trying to pull tight a screw with a hammer.
There is no wonder that in one Research found that roughly one-third of user-experience issues take more than one and the half month to fix or go unsolved completely.
Defensive mentality –
Nowadays, many IT support teams and engineers are trying to manage their networks in a vacuum without sufficient visibility. For example, the ability to troubleshoot applications such as Oracle, SQL and Microsoft need a apparent understanding of exactly what the user is experiencing.
Too often, IT is tasked with fixing an issue with a particular application that has its behavior and characteristics, only to find they lack the necessary insight. In the end, a determination is made that the response time for the application is just too slow, or that latency is just too high, all with little to no context as to how the end-user consumed and perceived the application to perform.
As a consequence, both support teams and end-users are generally frustrated. Gartner analysts recorded during the 2019 Gartner Data Center Conference, Operations Management and IT Infrastructure that 75% of network engineers were disappointed with trying to assess user activity in the cloud-based performance metrics. Also, Gartner informed that more than 50% of network engineers confessed to being “blind to what happens in the cloud,” while 32% indicated having great clarity gaps.
In a typical situation, everything across the network monitoring dashboard maybe green. but the end-user is still having a bad experience. It could be due to many small nuances in performance, or conjunction of multiple pieces of infrastructure operating defectively in aggregate while running within spec independently, but either way, the network executive can’t visually see the effects that users are feeling.
Armed with only a one-dimensional view, IT support teams can make rough estimates at the cause of the problem. They might lead to an escalated conversation, but after spending precious time in discussion, the user is still facing the issue. If you’re not observing the network from the perspective of users, it’s nearly impossible to ensure optimal user experience. The truth of the matter is that performance metrics don’t equal user experience.
On the identical page –
Traditionally, the only method to know what a user is experiencing is to see for you; either in person or by taking control of their PC remotely. But with the modern reality of decreased funds, shrinking staff resources, and expanded responsibilities, those days is a thing of the past. Particularly given that more than 90% of enterprises have at least some part of their workforce accessing network or application services remotely; often using devices the IT team doesn’t support, thanks to BYOD trends.
As the consumerization of technology results in increased user expectations, the chasm between users’ experiences and their expectations remains to widen. And if network support teams can’t get a handle on troubleshooting end-user experience problems, this gulf will continue to spend a growing number of resources.
Luckily, advancements in machine learning and synthetic intelligence technology are doing it easier to get ahead of service delivery issues and streamline the workload. The choice of algorithmic measurements enables event data to be collected in present-time across both remote and on-premises environments. Combine that with an adaptive ML approach to data analysis, and the help desk may even be aware of looming user experience issues before the users themselves. This not only speeds up the task of tracing and identifying problems but also significantly reduces mean time to repair.
One level ahead –
As IT network technology continues to evolve, the tools and methods that we rely on to manage and optimize those networks need to grow as well if we want to stay a step ahead of the performance degradation. By leveraging adaptive intelligence to mine and analyze network data in real-time, IT teams can monitor crucial services and network health in order to assess and optimize the user experience.
Equally importantly, however, is the requirement to take a step back and reassess our traditional opinions and habits. With the availability of tools to observe actual user experience, network administrators don’t require to waste time trying reading an issue with incomplete data, and they should not presume they know exactly what the user is experiencing. Because as long as we cling to outdated ideas, IT support teams cannot solve enterprise end-users issues and end-user always face issues.
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