The lifecycle of an operational incident has multiple phases, each of which is often fragmented. While there are tools to assist at each stage, these solutions fall short because they do not work together. To eliminate downtime, it is essential to use a seamless approach.  Achieving zero downtime requires automating the full lifecycle of an operational incident.

Anomaly detection tools identify potential problems. After an alert is triggered, it is not clear where and when an incident will occur. Without any contextual awareness, other tools are needed to determine where the problems are coming from. As a result, the root cause analysis process is long and manual, and may not be resolved in time to prevent an incident from occurring.

When AIOps is involved, the anomaly can be used to predict when a potential incident will occur. Without AIOps, there is no incident prediction. AIOps brings intelligence from systems management to observability tools in order to make sense of the anomalous data patterns.

Without AIOps, multiple tools and sources must be reviewed to triangulate the root cause of the problem. Different tools typically do not process different data types together, such as logs, metrics, and events. The user looks at these different observability tools and can see the change in performance. However, they do not provide actionable insights that point to the root of the problem. This is because the tools do not work together and provide context, or an overall view, to understand the root cause of the issue.

Once the root cause is found, action needs to be taken to resolve the problem. While observability tools can display a problem, but cannot prescribe actions to take and solve the problem. ITSM tools can assign work actions and tickets to teams and or machines, but the observation and actions that need to be taken are not connected. With a tool that automates the entire process, the tool pinpoints the root cause and can deliver actionable insights to the appropriate teams to fix the contributing factors to the problem.

The lifecycle of an incident should not end when the incident is resolved. Rather,  teams and systems must learn from past issues to prevent future ones. Once a root cause is identified and a problem solved, the data should be fed back into the system, so machine learning can detect new patterns, correlate them with past issues, and resolve the issue before it impacts the system.

There are many tools that can be used to manage each stage of an incident lifecycle. These tools are not sufficient to achieve zero downtime. Only a tool that manages the entire lifecycle of an incident will help a company eliminate outages. Unlike other tools, InsightFinder manages the entire incident lifecycle, connecting alerts to observation,  observation to prediction, insight and action, and action to remediation. Learn more about InsightFinder’s technology today.

Other Resources

A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.