Servicenow Performance Analytics Proactive Insights

Beyond the Dashboard: Mastering ServiceNow Performance Analytics for Proactive Insights

ServiceNow’s Performance Analytics (PA) is more than just generating pretty dashboards. It’s a powerful engine for proactive insights, enabling you to anticipate issues, optimize processes, and drive data-driven decision-making across your organization. This blog post dives deep into mastering PA, moving beyond basic dashboards to unlock its full potential.

The Limitations of Simple Dashboards

Dashboards offer a snapshot of current performance, which is useful. However, they are reactive. You only see the problem after it has occurred. Consider these scenarios:

Incident Management: A dashboard shows a spike in P1 incidents. Useful to see the problem, but it doesn’t tell you why it happened or how to prevent it next time.
Change Management: A dashboard displays the percentage of successful changes. Good to know, but it doesn’t highlight potentially problematic changes before they’re implemented.
Service Catalog: You see which items are popular. But, are people abandoning the request process midway? Where do they get stuck? Dashboards alone cannot tell you.

This is where Performance Analytics shines – it helps you anticipate and prevent issues, going beyond simply reporting on them.

Core Components of Performance Analytics

To effectively leverage PA, you need to understand its core components:

Data Collectors: These extract data from ServiceNow tables and transform it into a usable format for analysis.
Indicators: These define the specific metrics you want to track (e.g., “Number of Open Incidents,” “Average Resolution Time”). They are based on data collected.
Breakdowns: These categorize data based on specific attributes, allowing you to analyze trends within specific segments (e.g., “Number of Open Incidents by Assignment Group,” “Average Resolution Time by Priority”).
Visualizations: These present the analyzed data in a meaningful way (e.g., charts, tables, scorecards).
Forecast Models: These use historical data to predict future trends, enabling proactive intervention.

Moving Beyond Basic Dashboards: Practical Examples

Let’s explore some real-life examples of how to leverage PA for proactive insights:

1. Predictive Incident Management

Problem: High volume of P1 incidents causing service disruption and impacting business operations.
Traditional Approach: Monitor the number of open P1 incidents on a dashboard and reactively address them.
PA-Driven Proactive Approach:
- Indicators:
  - Number of P1 incidents
  - Average time to resolution for P1 incidents
  - Number of changes implemented in the last 24 hours
  - Number of known errors
- Breakdowns:
  - Category
  - Assignment Group
  - Configuration Item
- Analysis: Correlate the number of changes implemented with the increase in P1 incidents. Identify specific categories or configuration items that are frequently associated with P1 incidents. Analyze known errors to identify recurring problems that could lead to P1 incidents.
- Forecast Model: Use time series forecasting to predict the number of P1 incidents in the next week based on historical trends and external factors (e.g., upcoming product releases).
- Action: Implement proactive measures, such as pre-implementation testing, communication plan and enhanced monitoring for high-risk changes, create knowledge articles for known errors, and allocate resources to address potential future P1 incidents.

2. Proactive Change Management

Problem: Failed changes causing service disruptions and impacting business operations.
Traditional Approach: Monitor the percentage of successful changes on a dashboard.
PA-Driven Proactive Approach:
- Indicators:
  - Number of changes with failed test results.
  - Number of changes without proper documentation.
  - Number of changes scheduled during peak business hours.
  - Average time spent in the “Planning” phase.
- Breakdowns:
  - Change Category
  - Change Manager
  - Configuration Item
- Analysis: Identify change categories or change managers with a high failure rate. Analyze changes that were not properly documented or scheduled during peak hours. Check if changes are properly planned before scheduled.
- Forecast Model: Develop a risk assessment model based on historical change data. Predict the likelihood of change failure based on factors such as the complexity of the change, the availability of resources, and the time of implementation.
- Action: Implement stricter change management procedures for high-risk changes, improve training for change managers, and avoid scheduling changes during peak hours.

3. Optimizing Service Catalog Performance

Problem: Low service catalog adoption and user frustration with the request process.
Traditional Approach: Monitor the number of service catalog requests submitted.
PA-Driven Proactive Approach:
- Indicators:
  - Number of abandoned service catalog requests.
  - Average time to complete a service catalog request.
  - Number of users who searched for a service but didn’t submit a request.
- Breakdowns:
  - Service Catalog Item
  - Stage in the Request Process
  - User Role
- Analysis: Identify service catalog items with a high abandonment rate. Pinpoint specific stages in the request process where users are dropping off. Analyze user search queries to identify services that are not easily discoverable or don’t exist in catalog.
- Forecast Model: Model abandonment rates based on request complexity, required approvals, and associated costs. Use this model to identify potentially problematic service catalog offerings before widespread launch.
- Action: Simplify the request process, improve the search functionality, and add new services to meet user needs.

Technical Implementation Considerations

Data Governance: Ensure data quality and consistency for accurate analysis.
Performance: Optimize data collection and processing to avoid performance bottlenecks.
User Training: Train users on how to interpret data and take appropriate action.
Security: Secure access to data and reports to protect sensitive information.

Diagram: PA Workflow

Reference URLs

ServiceNow Performance Analytics Documentation: https://docs.servicenow.com/bundle/quebec-performance-analytics-and-reporting/page/use/performance-analytics/concept/c_PerformanceAnalytics.html
ServiceNow Community - Performance Analytics: https://community.servicenow.com/community?id=community_topic&topic_id=performance-analytics

Conclusion

Moving beyond basic dashboards with ServiceNow Performance Analytics empowers you to proactively identify and address potential issues, optimize processes, and drive data-driven decision-making. By understanding the core components of PA and implementing practical strategies, you can unlock the full potential of the platform and improve your organization’s overall performance. Remember to focus on defining meaningful indicators, leveraging breakdowns for granular analysis, and using forecast models to anticipate future trends.