ServiceNow Performance Analytics for ITSM for Energy and Utilities

How Energy and Utilities Use Performance Analytics for ITSM

Energy and utilities organisations use Performance Analytics to track ITSM KPIs that directly impact business operations, regulatory compliance, and customer service. Key metrics include incident resolution time (how quickly critical infrastructure incidents are resolved and systems restored), availability of critical IT systems supporting power generation or distribution operations, change success rate (percentage of changes that are deployed without causing subsequent incidents), and compliance with SLA commitments to field operations teams and internal stakeholders. For utilities, performance analytics goes beyond standard IT metrics; it includes understanding how IT incidents cascade into operational risks and customer impact. For example, a data center outage affecting meter reading systems has direct consequences for billing accuracy, customer service, and revenue, making real-time visibility critical. Performance Analytics helps utilities understand where they are vulnerable to IT failures and where investment in redundancy, process improvement, or tooling would yield the most benefit.

Unique Metrics for Energy and Utilities

Standard ITSM metrics (mean time to restore, incident backlog, SLA compliance) are important but not sufficient for utilities organisations. Utilities also need to track: critical asset availability (percentage uptime for systems essential to operations), change risk metrics (number of changes that resulted in incidents or service degradation), time to recover from outages (how long it takes to restore operations when failures occur), and compliance with security incident response times mandated by regulatory frameworks. Utilities also benefit from metrics that correlate IT incidents with operational impact: did this IT incident cause billing failures, meter reading delays, service availability issues for customers, or emergency response delays? This correlation helps prioritize IT investments based on operational impact and customer risk. Availability metrics for specific systems (SCADA, billing, meter data) are often tracked separately from generic incident metrics.

Design Decisions for Analytics Implementation

When implementing Performance Analytics for ITSM in a utilities organisation, decide which metrics matter most to your business strategy and priorities. If critical infrastructure availability is your highest priority, focus analytics on tracking mean time to recover and change success rate. If regulatory compliance is the primary driver, focus on metrics that regulatory auditors care about: incident response times, change approval processes, and security incident handling. Utilities often operate geographically distributed teams and may need to track performance by facility or region to identify local issues and patterns. You should also decide whether to track metrics at the ITSM module level (overall incident performance) or by service or application (how well is our asset management system performing?). This granular view helps identify where problems exist and where intervention would help most.

Regulations and Stakeholder Considerations

Utilities organisations subject to regulatory oversight must ensure that Performance Analytics dashboards support compliance reporting and audit requirements. Regulatory agencies may require documentation of incident response times and change control effectiveness. Performance Analytics should be configured to generate compliance reports automatically and maintain a reliable audit trail showing when metrics were captured and how they changed over time. Different stakeholders have different analytics needs: executives care about overall system health and risk exposure, IT directors care about team performance and process metrics, operations managers care about how IT impacts the systems they run. Performance Analytics dashboards should provide different views tailored to these audiences so each group sees the metrics that matter to them and can make better decisions.

Implementation and Continuous Optimization

Implement Performance Analytics by starting with essential metrics aligned to your organisation’s strategic priorities and identified pain points. Track metrics for long enough to build a reliable baseline before attempting to improve them, so you can measure improvement accurately. Then identify the root causes of underperformance: are incidents taking too long because of knowledge gaps, inadequate tooling, process delays, or staffing constraints? Use Performance Analytics not just to report metrics but to drive continuous improvement and resource allocation decisions. Utilities organisations that use analytics effectively typically see steady improvements in critical metrics over time. Regular reviews with IT leadership and business stakeholders ensure analytics remain aligned to priorities and regulatory requirements and that actions are taken based on the data.

Advanced Analytics and Predictive Insights

Beyond tracking historical performance, some utilities organisations use advanced analytics to predict future trends. For example, if incident volume is rising, analytics can help forecast staffing needs. If change success rates are declining, analytics might reveal that changes are being deployed too frequently or that testing procedures need improvement. ServiceNow Performance Analytics can also help identify patterns: certain types of changes fail more often, specific time periods have higher incident volume, or certain teams consistently miss SLAs. Using these insights to drive process improvements and training investments accelerates performance gains. Over time, analytics become the foundation for strategic IT decisions about investment and capability building.

Defining an indicator so two teams calculate the same number

Many disputes about a service metric are arguments about definitions, not about performance. Two teams can both report mean time to restore and reach different answers because one starts the clock at logging and the other at the first monitoring event, or because one counts a partial restore as restored. Before any dashboard is built, write the definition of each indicator with its clock rules, counting rules and exclusions settled.

Store the definition with the indicator itself, not in a separate document that drifts. When someone questions a figure a year later, the definition should be one click away from the chart.

Why averages hide the cases that matter

A mean hides the tail, and the tail is where utility operations feel the most pain. A desk with a comfortable average can still have a small group of long incidents on critical feeders that drive every complaint. Performance Analytics lets you view the same indicator in several ways, and each view answers a different question.

Building the collection and refresh routine

Indicators are only as current as the data collection that feeds them. Treat the collection job as an operational service with its own owner, and check it before anyone reads the dashboard in the morning. The sequence below keeps the build honest.

1. Agree the indicator definition and sign it off with the service owner, including the clock start and pause rules.
2. Confirm the source fields are populated for the categories in scope, and record the gaps as a data quality issue rather than correcting them silently.
3. Build the indicator against a fixed historical period first, so the team can check the output against cases it already knows.
4. Set the breakdowns for critical services, sites and teams, then remove any breakdown nobody will act on.
5. Schedule collection, and add a visible check that flags when the latest data is older than the reporting cycle expects.
6. Review the first two cycles with the people who will act on the numbers, then freeze the definition.

A common mistake is to publish the first live numbers before the historical check is complete. The first month of figures then becomes the argument instead of the evidence. Breakdowns by criticality depend on the tags held in the configuration records, so check those tags before the first build.

Signs that a dashboard is misleading its readers

• A single headline figure with no definition beside it, and no note on the clock rules.
• A trend line that changes shape on the day a definition changed, with no break marked.
• Breakdowns that show large differences between teams without the volume of work behind each team.
• Critical services that appear in no breakdown at all, often because the tag was never applied.
• Figures that nobody can reproduce from the source records when a manager asks for the list behind the chart.

When a scorecard is the better tool

A dashboard suits an operations room that watches a few live measures. A scorecard suits a monthly review that compares services against agreed targets and records what was done about each miss. Building both for every audience multiplies the definitions you must maintain. Start with one scorecard for each service owner and add live views only where someone looks at them every day. The service availability review article covers reviewing evidence of availability, which suits the monthly format.

Questions about reporting and governance

Should the report lead with incident counts or with duration?

Lead with duration for the services that carry the most operational risk, and show counts beside it so a rise in volume is not hidden behind a steady average. For lower-risk services, counts may be the more useful lead because they show where demand is growing.

Can a Performance Analytics figure serve as regulatory evidence?

Treat it as supporting data. The authoritative record remains the underlying incident, change and restoration records, and the indicator definition must be stored with every report so the figure can be explained. Ask the compliance lead to confirm which reports they will accept before the first submission.

What should happen when the collection job fails overnight?

The dashboard should say that the data is stale rather than show yesterday as today. Assign a named person to re-run the collection, and record the gap in the report notes so the trend line does not appear to fall for no reason.

Next step for your analytics set-up

The Performance Analytics for ITSM page describes the product and the metrics it provides.