What is the importance of AIOps for digital performance?
Want to know why artificial intelligence is imperative to analyze digital performance? Read this blog to know why it’s becoming a necessity for IT operations.
There is a well-known big data paradigm: higher quantities of data don’t always lead to better quality of information. This applies to digital performance analytics as well. Organizations tend to use a lot of point solutions to monitor layers of a variety of workloads for a multitude of purposes such as availability, security, compliance and performance. Each workload, each layer, each team and each use case usually requires different datasets which are derived from the big heaps of data existing in all the monitoring solutions. But does all this available data deliver the outcomes we need?
Too much data to humanly comprehend
Performance-related issues are rarely constrained to just one spot and often touch on a variety of workload components that are in place. The incredible amount of monitoring metrics that are available for a modern workload have become too overwhelming for humans to go through and to analyze without the help of software. The complexity of these workloads is fed by our need for security, availability and added functionality. This means we require help from smart software to plow through the heaps of data and point out when and where things were different.
The rise of AIOps
Most modern IT monitoring solutions include some artificial intelligence-based features designed for their specific use in the stack, usually referred to as AIOps. Artificial Intelligence for IT operations means we use big data analytics, machine learning, and other artificial intelligence technologies to automate the identification and resolution of common information technology issues. This should shorten our mean time to resolve and the quality of root cause analysis we make on issues that occur. However, these functions are constrained by the datasets available to the specific software solution, limiting their use within complex workloads that consist of multiple layers.
The need for centralized AIOps
The need to combine digital performance data from various sources to enable coherent performance analysis and resolve issues faster is growing rapidly. This goes beyond siloed solutions that cover a limited part of the entire stack. To leverage both traditional and modern monitoring data from these point solutions we require a centralized platform to enhance the entire dataset with artificial intelligence-based features to go through the data heaps. This will create an overview of the complete workload rather than its parts.
See for yourself
If you are interested to see what a centralized AIOps platform specifically designed for digital performance can do for you please visit our platform solution page to continue reading.