An effective incident management strategy is crucial for any business, especially those offering consumer-facing digital services. This is because when incidents occur, they may be easily detected by your users, impact your reputation, and ultimately affect your bottom line. So, to minimize the reach and severity of incidents, your response needs to be swift and effective. One way to ensure your approach meets these requirements is to implement AIOps.

AIOps is the application of sophisticated analytics like natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) to automate, enhance, and optimize IT operations and workflows. It encourages IT automation and provides insights into where your workflow can be improved.

Applying AIOps to incident management can assist incident response teams in detecting issues, diagnosing problems, and coordinating responses, ultimately leading to a more efficient incident response that helps to minimize an incident’s damage and costs. It enables you to monitor your IT assets better and identify potential incidents before they occur—or, if they’re happening, quickly identify the root cause. Additionally, AIOps can help decrease service disruptions and maintain application availability during incident remediation.

In this article, we’ll explore how you can use AIOps tools to enhance your incident management practices, making them more flexible and adaptable so you can respond more effectively to emergent incidents.

Creating a Flexible Incident Response with AIOps

With the widespread adoption of DevOps and the implementation of practices like continuous integration/continuous deployment (CI/CD), your developers and engineers need to monitor metrics and performance across platforms while using dispersed tools. Because of the number of tools they use, it’s common for teams to get overwhelmed with general performance alerts, leading to alert fatigue and missed issues. Therefore, your teams must find a way to quickly identify issues that are the most critical to reliability and usability.

This is where AIOps comes into play. AIOps tools combine AI and ML capabilities to assist the effectiveness of your incident response in the following ways.

Detect Anomalies that May Indicate an Incident

One of the best ways your teams can identify incidents proactively is to monitor and analyze different performance metrics. Some of these metrics include CPU use, memory use, disk space use, and network bandwidth. Monitoring these metrics provides a baseline of system, application, or network performance, making it easier to detect when something is out of the ordinary and where an incident may occur.

However, your teams won’t be able to monitor these metrics at all times. This is especially true if your organization is working at scale. To watch for and help you identify anomalies, you can employ AIOps. AIOps tools identify anomalies using ML and AI, allowing you to quickly identify and respond to incidents.

Unsupervised ML algorithms like Isolation Forest or Local Outlier Factor are some of the most popular anomaly detection algorithms. Upon detecting an anomaly, these algorithms help you create charts and graphs that indicate when tracked metrics—CPU use, network bandwidth, and so on—spike. You can then use that report to identify issues in the environment that you should explore further.

Use Incident Response History to Support Faster Resolution

It’s always beneficial to have a historical record of all past incidents that impacted your business processes or applications. This record should include the details shared in the incident postmortem, such as the root cause, time and steps to resolution, and measures taken to prevent the incident from happening again. Comparing new incidents with prior issues and responses can help you develop ways to mitigate incidents faster.

You can use an AIOps tool to locate patterns in past incidents, and you can use them to respond to new ones. These tools mine your knowledge base of issues—and your responses to them—and can immediately recognize similar incidents when they occur.

AIOps tools use machine learning techniques such as K-means clustering and mathematical approaches like Euclidean distance to identify the similarity between incidents in this case. Moreover, AIOps tools can suggest the next course of action based on this historical data, providing recommendations about who to escalate the issue to and whether to auto-close, cancel, or hold incidents based on the severity of similar problems in the past.

Notify Relevant Teams Automatically

When incidents and critical events occur in a system, relevant teams should be notified about the issue immediately. Failing to notify the appropriate incident response team not only delays the incident response but can also result in more damage to your system or application and more significant issues for your users.

Automated notifications are a vital component of resolving issues quickly. Issues and outages must be escalated to the appropriate teams as soon as the incident or anomaly appears. Automating this notification process ensures that no incidents are missed and that teams see notifications in the order of priority.

An AIOps tool can use AI to intelligently collate relevant information about an incident and translate essential information into notifications that can enhance actionability for the responding team. Additionally, AIOps’s ML capabilities can filter noise from notifications to ensure only actionable reports reach responders, helping to minimize alert fatigue.

Interpret Incident Reports and Expedite Communication

If you’re experiencing an outage or another widescale incident, your organization will likely get swamped with tickets. While you could add more support staff, doing so will increase costs and might not help you sort through the tickets any faster. Client expectations for speedier interactions also mean service representatives must spend more time addressing specific customer concerns.

Instead of doing this work manually, you can implement an AIOps tool to interpret tickets faster. AIOps tools can use NLP algorithms to sort through the free-text information shared in the tickets, categorize tickets based on similarities, and quickly pinpoint issues. By doing so, NLP can help to interpret incident reports and, in turn, expedite communications during incident response.

Perform Root-Cause Analysis and Identify Optimization Opportunities

Although it’s fairly easy to identify when an application is broken, determining the underlying cause is much more complex. It can entail spending hours combing through dashboards and logs to determine what went wrong. Fortunately, you can perform the root cause analysis more quickly by applying unsupervised ML techniques in correlating logs and monitored metrics.

AIOps tools enable teams to proactively identify crises and respond to them in real-time. Additionally, they can use ML predictions to forecast and stop future incidents—whether similar or different—from happening.

When performing this work manually, it’s challenging to identify the cause of the issue. Because you’d need to sort through mass amounts of IT monitoring data and data of different kinds and formats across different dashboards, ticketing systems, and monitoring tools, the resolution process would be slow.

AIOps tools can help address all these concerns by handling the collection of data from different sources and consolidating them into meaningful information available for viewing at a centralized dashboard. Additionally, an AIOps tool can discover actionable insights, spot the right issues, reduce alert fatigue, and improve incident response times. With these capabilities, implementing AIOps allows you to optimize your incident response strategy.

Why You Need a Flexible Incident Management Strategy

Modern big data or microservices-based applications are distributed in nature, and such applications are deployed in large-scale systems. The amount of data and events being processed in such a system is high in volume and, therefore, hard to monitor manually. With a more traditional approach, you can’t resolve the incidents in time. In some instances, you might even miss knowing that an incident has already occurred in the system due to manual dependency, alert fatigue, and various other reasons.

To have better visibility and control of incidents and to shorten your response time, you need to develop an incident management strategy that’s flexible and able to adapt to unexpected incidents. A flexible incident management strategy ensures that each incident is approached in a way that will be effective for that particular incident, decreases incident response time, and allows your teams to use resources effectively.

AIOps tools enable a flexible, adaptable incident response strategy by allowing teams to remain nimble and free up brain power for handling more complex or high-risk incidents. Automating repetitive tasks and refining your automatic incident notification process ensures that accurate and up-to-date information is readily available. Moreover, AIOps doesn’t just automate this information—it intelligently sorts and correlates it, applying ML capabilities to discover insights that help your teams handle incidents effectively.

Conclusion

Incidents are unavoidable. Therefore, it’s crucial that you have an efficient incident management strategy in place. Although automated incident reporting and creating detailed incident postmortems are a great place to start, your incident response needs to be flexible and adaptable to ensure that all incidents—whether on a feature-specific problem or a full outage—are handled appropriately.

AIOps can help you formulate a flexible incident management strategy that prepares you to handle unexpected incidents. They can proactively identify issues, automatically escalate emerging issues, collect and share information about the nature of the incident, and more. Collectively, these capabilities enable your teams to respond more quickly, efficiently, and confidently.