Solutions for Managing Large-Scale Data and Workloads with AIOps

Digital transformation benefits your small business or large organization by increasing productivity with scalability in IT infrastructure, expanding data storage and resources, and accelerating application delivery. However, the large-scale expansion of web services like cloud environments has created challenges for IT professionals and engineers, affecting their security and operational efficiency. To curb these challenges, here are some of the most effective solutions that will enhance your company’s use of artificial intelligence for IT operations (AIOps) by making complex automated decisions and managing large-scale data.

Use Cases for AIOps for Large-scale Data and Workload Management

AIOps can provide several benefits to your business to streamline and automate their IT operations and management processes. Here are a few use cases:

Detecting And Fixing Issues More Rapidly

AIOps offers full insight into the private, hybrid, and public cloud resources that identify and fix problems with large-scale data swiftly. AIOps platforms may combine this insight on the event and problem data to analyze the data to identify the issue before it arises.

IT Noise Reduction

IT noise is referred to as an enemy of productivity in modern systems, which increases complexity by creating false and unnecessary alerts and notifications that hinder IT professionals from figuring out the real problem with the system, resulting in a waste of time and resources. This is where AIOps come into the picture, as AI (artificial intelligence) and ML (machine learning) algorithms solve the problem by automatically collecting and calculating alerts across the application. This ensures that the data you and your team can leverage to identify the root cause saves time and energy compared to using manual analysis.

Root Cause Analysis

It is imperative to find the underlying cause of the issue to understand the complexity of modern systems, which often makes it a time-consuming job for IT professionals to analyze large amounts of data and alerts to find the root cause of the problem. AIOps can collect and correlate events to use machine learning inference models to segregate and identify the root cause of the issue.

Case studies – Electrolux AB

Electrolux AB is a worldwide home appliance manufacturer known for its efficiency and elegant design that is currently working on innovative and ground-breaking appliances. Automated operations were the need of the hour as its rival companies were on their way to digital transformation. Thus, the company incorporated AI to automate its IT operations, which will boost efficiency and contribute to environmental sustainability goals. Joska Lot, Global Solution Service Architect: Monitoring and Events Management of Electrolux AB, said, “We see about 100,000 events per day. It is so important in this huge ocean to identify exactly the drop of venom that you have to remove to save your life.”

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