Top 5 AIOps use cases to enhance IT operations

In today’s competitive landscape, enterprises must constantly improve and speed up their IT operations to stay ahead. Data plays a critical role in process optimization and enhancing the efficiency of IT operations. However, monitoring diverse live data from multiple sources is challenging. Significant delays in identifying and resolving IT issues can cause a loss of business and even a crisis. It is critical to monitor data and detect application glitches in real-time. Artificial intelligence for IT operations (AIOps) equips businesses with the capability to monitor streaming data in real-time to quickly detect and identify business-critical issues. More and more businesses are turning to AIOps to predict, detect, and fix IT issues. As per Gartner, the exclusive use of AIOps and digital experience monitoring tools by large enterprises will increase from 5% in 2018 to 30% in 2023.

What is Artificial Intelligence for IT Operations (AIOps) ?

Artificial intelligence for IT operations (AIOps) involves the application of artificial intelligence and machine learning for monitoring and analyzing large volumes of data generated by IT platforms. In addition to real-time monitoring of data, AIOps tools also help enterprises in event correlation and root cause analysis, thereby enabling faster issue resolution. As a combination of machine learning and big data, AIOps is designed to automate IT operations and reduce outages.

Why do you need AIOps to optimize IT efficiency?

As an enterprise grows, automating IT operations becomes critical for delivering a seamless user experience. Speed is essential for making rapid, operational decisions and automation. IT teams face the challenge of dealing with large volumes of complex data and logs. The traditional approach to data monitoring and analysis is time-consuming and difficult to scale. There is a need to quickly and continuously monitor data from various sources.

AIOps helps enterprises to monitor and analyze complex streaming data in real-time, thereby enabling automation of IT operations. With AIOps, businesses can take a proactive approach to data monitoring to prevent serious IT issues or outages. By proactively monitoring, and remediating incidents, companies can improve their IT efficiencies and take their application performance to the next level.

Top 5 AIOps use cases to enhance IT operations

AIOps essentially helps enterprises to automate real-time incident resolution. Here are some popular use cases of AIOps:

1. Real-time anomaly detection

AIOps gives enterprises the capability to ingest, monitor, and analyze large volumes of streaming data at a granular level, detecting anomalies or threats in real-time that are otherwise hard to identify. Enterprises can proactively monitor performance metrics and detect any unexpected incidents or anomalies for quick remediation before any serious impact on service availability. Companies can thus leverage AIOps to detect anomalies in time-series data and eliminate the risk of application blind spots.

2. An intelligent way to manage alerts

Alert fatigue is real and the last thing businesses want is to drown their IT teams with a large number of alerts. As per an AIOps exchange report, 40% of organizations are flooded with over 1 million alerts a day. By learning from historical data to spot outliers of high business value and grouping correlated incidents, AIOps delivers actionable alerts to IT teams, bringing their attention to only critical issues that need attention. By highlighting critical events, it helps in effective prioritization and better issue resolution.

3. Root Cause Analysis

In addition to pinpointing anomalies in a complex IT infrastructure, AIOps also helps in investigating the root cause of issues and correlates abnormal incidents. It essentially enables the early detection and diagnosis of IT issues. This in turn helps in quicker resolution and remedial action. With visibility on the correlation between incidents and better diagnosis of the primary cause, IT teams can respond more efficiently to the incidents.

4. Automated incident management

By leveraging machine learning algorithms, enterprises can not only set automatic alerts for incidents but also trigger automatic system responses to remediate the issues. By automating and resolving incidents in near real-time, enterprises can deliver a seamless user experience.

5. Capacity planning and management

By leveraging AI-enabled analytics, businesses can proactively monitor metrics that impact application performance such as CPU types, memory, bandwidth, and other parameters to effectively manage workload across their IT infrastructure. Using AI-based, data-driven recommendations, workloads can be mapped to the right combination of servers and machines. With AI-enabled, real-time insights, IT teams can improve the capacity of IT infrastructure while reducing operational costs.

Companies can thus leverage AIOps to enhance IT operations and improve the digital experience they deliver to end-users. AIOps essentially equips companies with a single pane view of all their data in one place and makes it easier to identify business-critical outliers and their causation in real-time. With the capability to manage data complexity and volume, AIOps is transforming IT operations across organizations.

Author: Tharika Tellicherry

CrunchMetrics is an automated real-time Anomaly Detection system, that leverages the #AI/ML-based techniques to sift through your data to identify incidents.