Change Insights

Updated on 26 May 2025
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Change Insights in Service Management focuses on using data and analytics to assess, and optimize the impact of IT changes. Such as identify patterns, suggest changes based on Incident, Problem clusters and automate changes based on the change type.

It enables IT teams and leaders to make better, faster, and safer change decisions within frameworks.

Insight Area and Description

Insight Area	Description
Change Pattern Detection	Identify recurring change patterns such as hardware failures, performance optimization, repeated upgrade requests.
Incident-to-Change Correlation & Recommendation	Analyzes clusters of high-priority incidents with known root causes and recommends related changes to prevent recurrence.
Proactive Change Opportunity Identification	Detects high-frequency issue or problem clusters that suggest the need for a formal change request (example: config update, performance improvement changes).
Change Automation Opportunity Analysis	Evaluates historical data by change type (Standard, Normal, and Emergency) to suggest which changes are for automation.

Following are a few of change request use-cases:

Identify patterns in change that point to an issue

This insight area focuses on identifying recurring patterns or anomalies in change records that are contextually associated with issues such as increased incidents, degraded performance, or failed deployments. It helps pinpoint systemic problems in the change process or in the infrastructure being modified.

User Input: “Identify patterns in recent changes that point to an underlying infrastructure issue.“

Copilot Response:

Figure: Identify Change Patterns

Recommend Change based on problem clusters

This insight area focuses on identifying recurring issues or problems that indicate a need for a change request. By analyzing the volume, frequency, and patterns of related problems, the copilot recommends change request for the issue pattern that could prevent future disruptions or optimize performance.

User Input: “Recommend potential change requests based on high-frequency issue clusters.”

Copilot Response:

Figure: Change Request Recommendation

Suggest changes for incident clusters

This insight area analyzes high-priority incident clusters that follow known or recurring patterns and recommends targeted changes to address their root causes. It helps IT teams move from reactive incident management to proactive and preventive change management.

User Input: “Suggest proactive changes for high-priority incident clusters with known patterns.”

Copilot Response:

Figure: Proactive change suggestions

Identify change patterns to Automate based on Change Type

This insight area identifies change requests based on the patterns such as low, high risk changes that can be automated, based on the change type such as Standard, Normal, or Emergency. The goal is to streamline change execution, reduce manual effort, and minimize risk by applying automation to the change patterns with a change type.

User Input: “Identify patterns of low risk changes that can be automated as standard changes.”

Copilot Response:

Figure: Change Pattern Identification

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Table of contents

Insight Area and Description