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            Monitor Configuration Item Criticality Prediction Card

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            Monitor Configuration Item Criticality Prediction Card

            Forecast configuration item (CI) business criticality by using machine learning, CMDB dependency, and incident data analysis. Use the resulting scores, ranging from 0 through 100, to prioritize incidents, improve change risk decisions, and focus resources on the most business-critical assets.

            Required Editions

            Available in: Lightning Experience
            Available in: Enterprise, and Unlimited Editions with Einstein for IT Services add-on, and AI Accelerator for IT Services add-on.
            1. From the App Launcher, find and select Incidents or Change Requests or CMDB and Service Graph.
            2. Open an incident, a change request, or a configuration item record.
            3. On an incident or change request or configuration item record page, go to the Configuration Item Criticality Score card in the side panel.
            4. Review the predicted score to determine the probability of a configuration item failure. For example, a score of 0.08 indicates a low risk.
            5. In the Top Predictors list, identify the factors influencing the score.
            6. Use the feedback icons to help refine the machine learning model:

              • Select the Thumbs Up icon if the prediction is accurate.

              • Select the Thumbs Down icon if the prediction is incorrect.
            Example
            Example

            Maria, an Incident Manager at Cumulus Bank, manages high-impact service events across customer-facing systems. During a trading slowdown, an incident is created for a core database service CI with these details.

            • Incident Number: INC-5824
            • Subject: Trading Platform Performance Degradation - Core DB Cluster
            • Category: Infrastructure/Database
            • Configuration Item: PROD-DB-CLUSTER-03

            Maria opens the incident and sees a CI Business Criticality Score of 94. The score indicates that this CI has a high business impact based on dependency topology, incident history, and major incident associations.

            Maria reviews the model signals and confirms why the score is high. Predictive AI shows that this CI supports multiple revenue-critical applications, has frequent linkages to upstream and downstream services, and involved in past major incidents with long resolution times.

            To reduce business risk, Maria prioritizes this incident ahead of other open tickets and assigns it directly to the specialized database reliability team. Maria also flags related records for closer monitoring because a failure in this CI can affect multiple business services.

             
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