Servicenow Performance Analytics Guide

From Data Deluge to Actionable Insights: A Practical Guide to ServiceNow Performance Analytics

In today’s fast-paced business environment, organizations are drowning in data. ServiceNow, a powerful platform for IT service management (ITSM), customer service management (CSM), and more, generates vast amounts of data daily. However, raw data alone is useless. To truly leverage the power of ServiceNow, you need to transform this data deluge into actionable insights. That’s where ServiceNow Performance Analytics (PA) comes in.

This guide provides a practical approach to using ServiceNow Performance Analytics, from understanding its core components to implementing strategies for driving meaningful change.

What is ServiceNow Performance Analytics?

ServiceNow Performance Analytics is a built-in application that helps you track and visualize key performance indicators (KPIs) and trends across your ServiceNow instance. It enables you to:

Measure Performance: Identify areas of strength and weakness.
Track Trends: Understand how performance changes over time.
Predict Outcomes: Anticipate future performance based on historical data.
Drive Action: Identify opportunities for improvement and take data-driven action.

Core Components of Performance Analytics

Understanding the core components of Performance Analytics is crucial for effective implementation. Here’s a breakdown:

Indicators: Indicators are metrics that you want to track. They represent a specific value measured over time. Examples include:
- Number of resolved incidents.
- Average resolution time.
- Customer satisfaction score.
Indicator Sources: Indicator sources define the data used to calculate indicators. They specify the table, conditions, and fields that contribute to the indicator. For example, an indicator source for ‘Resolved Incidents’ would point to the incident table, with a condition state=Resolved.
Breakdowns: Breakdowns allow you to segment your data and analyze performance across different dimensions. Examples include:
- Assignee group.
- Category.
- Priority.
Breakdown Sources: Breakdown sources are used to associate the breakdowns with fact tables.
Jobs: PA jobs are scheduled processes that collect data and update indicator scores. They ensure that your data is up-to-date and accurate.
Dashboards: Dashboards provide a visual representation of your key performance indicators (KPIs). They use charts, scorecards, and other widgets to display data in an easily understandable format.
Widgets: Widgets are individual components of a dashboard that display specific data visualizations (e.g., charts, scorecards, lists).
Automated Indicators: Automated indicators allow you to automatically create indicators and breakdowns based on a predefined configuration. This is useful for tracking common metrics across multiple tables.

Flowchart: Performance Analytics Data Flow

Practical Examples of Performance Analytics in Action

Let’s explore some practical examples of how you can use Performance Analytics to improve your ServiceNow processes:

1. Incident Management:

Indicator: Average Incident Resolution Time.
Breakdown: Assignment Group, Category, Priority.
Insight: Identify which assignment groups are consistently taking longer to resolve incidents, or which categories of incidents have the longest resolution times.
Action: Provide targeted training to underperforming groups, optimize processes for specific incident categories, or automate tasks to reduce resolution time.

Example Scenario:

Suppose the “Network” assignment group consistently has a higher average incident resolution time compared to other groups. Drilling down using the breakdown, you discover that “Router Configuration” incidents are the primary driver.

Further Investigation: Analyze the incident logs and resolution notes for these incidents.
Potential Solutions:
- Develop standardized router configuration procedures.
- Create knowledge articles to address common configuration issues.
- Provide specialized training to the Network team on router configuration.

2. Change Management:

Indicator: Percentage of Successful Changes.
Breakdown: Change Type, Change Requester.
Insight: Determine which types of changes are most likely to fail, or which change requesters have a higher failure rate.
Action: Implement more rigorous testing procedures for high-risk change types, provide coaching to change requesters with high failure rates, or implement a more robust change approval process.

Example Scenario:

Analysis reveals that “Emergency Changes” have a significantly lower success rate than other change types.

Further Investigation: Examine the reasons for failure in emergency changes. Common causes might include inadequate testing or insufficient planning due to time constraints.
Potential Solutions:
- Develop a streamlined emergency change process with pre-approved templates.
- Implement automated testing procedures for emergency changes.
- Establish a dedicated emergency change team with specialized expertise.

3. Problem Management:

Indicator: Number of Open Problems.
Breakdown: Configuration Item (CI), Impacted Service.
Insight: Identify which configuration items or services are associated with the most open problems.
Action: Prioritize problem resolution efforts based on the impact to critical services, proactively address underlying issues with problematic configuration items, or invest in improving the reliability of specific services.

Example Scenario:

The data shows that a specific database server (a CI) is associated with a disproportionately high number of open problems.

Further Investigation: Analyze the problem records associated with this server to identify recurring issues.
Potential Solutions:
- Upgrade the database server hardware or software.
- Optimize database configurations.
- Implement proactive monitoring to detect and address potential issues before they escalate into problems.

4. Service Level Management (SLM):

Indicator: Percentage of SLAs Met.
Breakdown: SLA Definition, Assignment Group, Priority.
Insight: Determine which SLAs are frequently breached, which assignment groups are struggling to meet SLAs, or which priority levels are most often missed.
Action: Re-evaluate SLA targets, optimize workflows to improve SLA compliance, or provide additional resources to support teams struggling to meet SLAs.

Example Scenario:

The report shows that SLAs for P1 incidents are consistently being missed by the “Help Desk” assignment group.

Further Investigation: Analyze the reasons for the SLA breaches. Possible causes include insufficient staffing, inadequate training, or inefficient incident routing.
Potential Solutions:
- Increase staffing levels for the Help Desk during peak hours.
- Provide specialized training to Help Desk staff on handling P1 incidents.
- Improve incident routing rules to ensure that P1 incidents are immediately assigned to the appropriate team.

5. Customer Service Management (CSM):

Indicator: Customer Satisfaction (CSAT) Score.
Breakdown: Product, Channel (e.g., phone, email, chat), Agent.
Insight: Identify which products have the lowest CSAT scores, which support channels are performing poorly, or which agents are receiving consistently negative feedback.
Action: Address product defects that are driving customer dissatisfaction, optimize support processes for underperforming channels, or provide coaching to agents with low CSAT scores.

Example Scenario:

Customers consistently report low satisfaction scores related to a specific product, “Product X”.

Further Investigation: Analyze customer feedback comments associated with Product X to identify common complaints.
Potential Solutions:
- Invest in product improvements to address the identified defects.
- Create knowledge articles and FAQs to help customers troubleshoot common issues with Product X.
- Proactively reach out to dissatisfied customers to offer assistance and gather further feedback.

Configuring and Implementing Performance Analytics

Here’s a step-by-step guide to configuring and implementing Performance Analytics:

Define Your Objectives: Clearly define what you want to achieve with Performance Analytics. What KPIs are most important to your organization? What questions do you want to answer?
Identify Data Sources: Determine the data sources you need to collect data for your chosen indicators. This will typically involve identifying the relevant ServiceNow tables and fields.
Create Indicator Sources: Configure indicator sources to specify the data used to calculate your indicators.
Create Indicators: Define the indicators that you want to track. Specify the indicator source, aggregation method (e.g., sum, average, count), and collection frequency.
Define Breakdowns: Configure breakdowns to segment your data and analyze performance across different dimensions.
Schedule Data Collection Jobs: Schedule PA jobs to automatically collect data and update indicator scores.
Build Dashboards: Create dashboards to visualize your data and share insights with stakeholders. Use a variety of widgets, such as charts, scorecards, and lists, to present data in an engaging and informative way.
Analyze and Act: Regularly review your dashboards and identify areas for improvement. Take data-driven action to address issues and optimize your processes.

Best Practices for Performance Analytics

Start Small: Begin with a focused set of indicators and dashboards. Avoid overwhelming yourself with too much data upfront.
Focus on Actionable Insights: Choose indicators that are relevant to your business goals and that can be used to drive meaningful change.
Automate Data Collection: Use scheduled PA jobs to ensure that your data is always up-to-date.
Customize Dashboards: Tailor your dashboards to meet the specific needs of your stakeholders.
Provide Training: Train your users on how to use Performance Analytics to access and interpret data.
Regularly Review and Refine: Continuously review your indicators and dashboards to ensure that they are still relevant and effective.

Resources and Further Learning

ServiceNow Documentation: The official ServiceNow documentation is a comprehensive resource for all things Performance Analytics. https://docs.servicenow.com/
ServiceNow Community: The ServiceNow Community is a great place to ask questions, share knowledge, and connect with other ServiceNow users. https://community.servicenow.com/
ServiceNow Training: ServiceNow offers a variety of training courses on Performance Analytics, ranging from introductory to advanced levels.

Conclusion

ServiceNow Performance Analytics is a powerful tool for transforming data into actionable insights. By understanding its core components, following best practices, and focusing on your specific business goals, you can leverage PA to improve your ServiceNow processes, drive better outcomes, and achieve your organizational objectives. Start small, focus on actionable insights, and continuously refine your approach to unlock the full potential of your ServiceNow data.