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AIOps is a buzzword that is making the rounds now. It helps optimize costs and streamline IT Operations. Let’s learn how.
IT Operations, or ITOps, form the backbone of many enterprises, ensuring that the IT infrastructure they depend on functions seamlessly 24/7. From managing data centers and networks to monitoring applications and providing user support, ITOps is essential for maintaining business continuity. However, the scale and complexity of modern IT environments are presenting a challenge for traditional ITOps. With ever-growing data volumes and massively distributed environments, enterprises are turning towards AIOps. As the name suggests, AIOps leverages advanced AI capabilities to automate and enhance various IT operations.
While ITOps involves extensive manual monitoring and analysis, AIOps aims to streamline these IT operations, providing real-time analysis and insights into massive data volumes. AIOps can correlate data across diverse sources, such as application logs, network data, performance metrics, and security events. It can detect anomalies and uncover patterns that might be overlooked by traditional ITOps processes.
HSC focuses on helping enterprises integrate AIOps into their processes to achieve greater operational efficiency and improved service reliability. Here are some of the benefits that enterprises can realize by including AIOPs in their tech arsenal:
Improved Time Management
Cost Efficient IT Operations
Faster Innovation
Scalability through Automation
Digital Transformation
HSC specializes in implementing AIOps models to enhance and automate operational workflows for its client’s specific use cases, Here are some of our AIOps offerings:
AI enables more quick and efficient anomaly detection, and automatic issue resolution. AIOps look at the vast variety of data being generated from various components such as servers, applications, cloud services, etc to understand the typical IT workflow in an enterprise. It can then detect changes in patterns to identify anomalies.
Monitoring user trends and patterns also allows AIOps systems to connect related issues together to find the root cause. AI can learn hidden relationships from historical data to predict future incidents and recommend actions to avoid them, reducing MTTR and operational costs.
Event correlation is meant to analyze alerts generated by the various applications, systems, and networks to diagnose and prevent issues. AI can automate this process by correlating alerts, pattern changes, and system topology to detect evolving issues before they escalate.
AIOps can be a virtual assistant in the whole recovery and remediation process, identifying the processes that lead to system instability. Autonomous remedial and recovery systems start by looking at the root cause and then taking the appropriate steps to resolve the issue, notifying the ITOps teams to ensure business continuity.
AIOps tools can identify critical performance metrics (e.g., response time, throughput, resource utilization) necessary for accurate modeling. They can also establish performance baselines by analyzing historical data, helping to identify deviations and anomalies.
AIOps can automatically gather data from various sources (websites, mobile apps, CRM systems, etc.), resulting in comprehensive datasets for cohort analysis. It can ensure that the data used is clean and standardized, reducing errors and improving the quality of analysis.
