AIOps stands for Artificial Intelligence (AI) for IT Operations and was coined by Gartner in 2016. It refers to the use of AI capabilities such as machine learning models and natural language processing to automate operational workflows. AIOps augments and supports IT processes and monitors real-time data to quickly detect and resolve issues.
There has been remarkable growth in the amount of data that is being generated by IT systems, and the lack of monitoring and analysis of the same could lead to missed opportunities and expensive downtime. This is where AIOps comes in handy. It uses smart monitoring systems and AI capabilities to prevent outages, maintain uptime, and achieve continuous service assurance, ensuring that organizations continue to operate at the desired speed.
Why is AIOPs necessary?
The significant growth of data generated by IT systems necessitates monitoring of the same to detect any anomalies. However, manually dealing with thousands of alerts is laborious and time-consuming. Moreover, managing, interpreting, and correlating multiple applications for tracking performance metrics is daunting. AIOps helps tackle these issues by providing a single analysis pane, detecting issues, and alerting the team to reduce the time spent on these alerts.
Most businesses nowadays use predictive analytics to ensure a seamless user experience, one of the most sought-after capabilities of AIOps. Furthermore, many IT professionals have realized the importance of AIOps in terms of automation capabilities, better efficiency, and predictive insights, which has greatly increased the demand for AIOps in the past few years.
How does AIOPs work?
As mentioned earlier, AIOPs leverages artificial intelligence to automate and optimize IT processes. It is generally powered by five algorithms that are as follows:
- Data Collection: Collecting large amounts of noisy data from structured and unstructured sources, such as application logs and event data, and highlighting only those parts that indicate an issue.
- Data Analysis: Analyzing the selected data using algorithms like anomaly detection and predictive analytics to detect anomalies and separate the real issues from false alarms.
- Inference: AIOps helps in identifying the root cause of recurring issues and helps IT teams prevent the same.
- Collaboration: AIOps notifies the appropriate teams once the root cause analysis is complete and facilitates collaboration between them by providing relevant information.
- Automation: AIOps significantly reduces manual intervention by automating responses and remediation.
Key use cases of AIOps
Some of the common use cases of AIOps are as follows:
- Root Cause Analysis: AIOps can help identify the leading cause of an issue and suggest appropriate measures to tackle the same. For example, AIOps platforms can find the cause of network outages and fix them immediately. Moreover, they also take protective measures to prevent similar issues.
- Performance monitoring: AIOps acts as a cloud infrastructure and storage system monitoring tool and reports on metrics like usage, availability, and response times. It combines and aggregates information to improve the end user's experience.
- Threat detection: AIOps also assists in identifying security risks by detecting patterns of malicious activity, thus reducing threats and intrusions.
- Anomaly detection: Using AIOps tools, users can discover data anomalies and predict problematic events, such as data breaches.
- Intelligent alerting: AIOps filters only the meaningful data and separates the real issue from false alarms. Moreover, it also prevents alert storms, where one false alarm triggers another, leading to a domino effect.
- Automation: Automation is one of the key use cases of AIOps. It automates remediation for known issues, thereby saving time and effort.
Limitations
Although AIOps tools help streamline IT processes, setting up and maintaining the same requires significant time and effort. Moreover, for best results, organizations must ensure that their data is up-to-date and accurate since AIOps algorithms are dependent on the data they are trained on. Lastly, there is always a risk of bias and ethical difficulties because of the prevailing biases in the datasets.
