Monitoring and Analytics for Task Intelligence for ITSM
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Summary of Monitoring and Analytics for Task Intelligence for ITSM
The Monitoring and Analytics for Task Intelligence for ITSM allows users to evaluate the performance of their trained incident prediction models. This feature enables tracking of model performance over time, business value assessment, and monitoring of agent predictions.
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Key Features
- Accessing Analytics Dashboard: Navigate to All > Task Intelligence for ITSM > Monitoring to view the dashboard. Use the Model drop-down to select and configure your desired model.
- Performance Visuals: The dashboard presents key performance indicators through graphs, including:
- Number of Predictions: Displays the volume of predictions made by models over time.
- Incident Mean Time to Resolve (MTTR): Shows average resolution times, ideally decreasing as predictions improve agent efficiency.
- Prediction Tracking: Widgets track the following:
- Predictions Agents Accepted: Indicates the correct predictions used by agents. A downward trend may suggest a need for model retraining.
- Predictions Agents Replaced: Reflects incorrect predictions removed by agents. An upward trend may also indicate retraining is necessary.
- Predictions Skipped: Shows predictions not utilized by the model based on selected criteria.
- Performance Overview Table: Provides mean percentage values for correct, incorrect, and skipped predictions across model-output field combinations.
- Usage Tracking: A bar chart visualizes the usage of predictions over time, allowing users to customize comparisons by toggling displayed components.
Key Outcomes
By utilizing the Monitoring and Analytics features, ServiceNow customers can enhance their incident management processes through effective model evaluation, leading to improved prediction accuracy, reduced resolution times, and overall increased operational efficiency.
You can view the impacts of your trained incident prediction models. Monitor model performance overtime, track business value, and view what predictions your agents did and didn't use.
To access the Task Intelligence Analytics dashboard, navigate to .
Use the Model drop-down list to select a model. Select Apply to open the model's configuration.
The Analytics dashboard contains the following sections:
- Get an overview
- See how your trained model is doing
Get an overview
The Analytics dashboard uses visuals to represent the performance overview of the model.
- Number of predictions
- The line graph shows the number of fields that the Incident categorization and Similar Incidents models predicted over time. As the model continues to learn, it can increase the number of predictions.
- Incident Mean time to resolve (MTTR)
- The line graph shows the average amount of time that it takes to resolve incidents over time. As the model makes more predictions, the MTTR must decrease as the predictions help agents.
See how your trained model is doing
The Analytics dashboard uses visuals to track how the model used predictions over time.
- Predictions agents accepted
- The widget shows the correct predictions that your agents used during case management over time. If this number is trending downward, you can look to retrain your model. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
- Predictions agents replaced
- The widget shows the incorrect predictions that your agents removed during case management over time. If this number is trending upward, you can look to retrain your model. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
- Predictions the model skipped
- The widget shows the number of predictions that were skipped by the model based on the model, output field, and date range selection. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
- Performance overview
- The performance overview table shows the mean percentage values for correct, incorrect, and skipped data for each combination of model and output field.
- Track usage of individual field predictions over time
- The bar chart tracks the usage of the individual field predictions over time. Each bar in the chart shows three components, which are the predictions accepted, the predictions replaced, and the predictions that were skipped by the model. A red outline around each bar represents the total number of records for each day. To compare specific components, navigate to the legends and deselect the ones that you don't want to include so that you can have a more customized and focused comparison based on user preferences. By default, the view displays the number of predictions. However, you have the option to switch to the percentage view by toggling the Show Percentage option. In the percentage view, you can also access the information about the baseline that was replaced and accepted, which is derived from the training data. This option enables you to gain insights into the performance of the model with the baseline.