ServiceNow Performance Analytics for ITSM for Life Sciences and Pharma

This page helps a life sciences IT or quality lead decide which Performance Analytics indicators to build for validated systems, and how to write each indicator's calculation rule before its dashboard is built. It covers tiering systems, reconciling dashboards with incident and change records, and keeping indicator changes visible to auditors.

Measuring IT Performance in Regulated Environments

Life sciences and pharmaceutical organizations operate under strict regulatory oversight. IT system performance directly affects ability to maintain compliance. Performance Analytics in life sciences must measure not just system availability but compliance with regulatory requirements. System failures that prevent regulatory reporting create regulatory risk, not just operational inconvenience.

Life sciences IT performance metrics often include compliance-specific measures: system availability for regulatory reporting systems, change control compliance rate, and audit trail completeness. These metrics reflect business requirements unique to regulated industries.

Compliance and Audit Trail Performance

Life sciences operations depend on audit trails: which systems processed which data, when, and by whom. System performance directly affects audit trail quality. If audit logging fails or lags behind real-time operations, compliance evidence becomes incomplete. Performance Analytics should track audit trail system health alongside overall system performance.

Some systems must operate 24/7 to maintain compliance: systems processing regulatory submissions, laboratory systems, or manufacturing quality systems cannot be unavailable without creating compliance risk. Performance metrics should reflect criticality: a 2-hour outage of a 24/7 system represents greater risk than 2-hour downtime for a daytime-only system.

Data Quality and Analytics Accuracy

Performance analytics quality depends on clean, complete incident and change data. Life sciences organizations often maintain stricter data standards than typical IT: incident descriptions must be precise, changes must be fully documented, and relationships to regulatory requirements must be clear. Analytics built on clean data are reliable; analytics built on poor data are misleading.

Organizations implementing Performance Analytics in regulated environments should start with data quality review: are incident and change records complete and accurate, or would cleaning required before analytics become reliable.

Connecting IT Performance to Compliance Outcomes

Advanced analytics correlate IT performance with compliance outcomes. When system downtime occurs, does that downtime create regulatory reporting delays or gaps in audit trails? Analytics platforms can show the connection, demonstrating why IT performance matters for business compliance.

This connection also works in reverse: compliance audits that find gaps in audit trails or incomplete records suggest IT systems that need monitoring, redundancy, or availability improvements. Connecting compliance findings to IT improvements helps organizations prioritize IT investments.

Metric Maintenance and System Updates

Performance metric definitions should be maintained as systems change. When ServiceNow is upgraded, when new features are implemented, or when organizational processes change, performance metric definitions may need adjustment. Organizations should revisit metric definitions periodically to ensure they still accurately measure what matters.

Performance analytics should track not just current metrics but metric trends: are metrics getting better or worse over time. Sustained improvements in metrics like mean time to resolution or system availability demonstrate effective process improvements and justify continued investment.

System Availability and Redundancy

Life sciences organizations require systems supporting regulatory operations to maintain high availability. Performance analytics should track system uptime by system criticality: mandatory 24/7 systems require higher availability targets than daytime-only systems. Redundancy investments should be evaluated based on criticality: critical systems may require geographic redundancy or active-active configurations ensuring continuous operation even during component failures.

Availability metrics should account for planned maintenance: distinguishing between unplanned downtime (system failures) and planned downtime (maintenance windows). Performance reports should show both availability during operational hours and mean time to restore during maintenance.

Data Integrity and Audit Trail Completeness

Life sciences performance analytics depend on complete audit trails: systems must record all changes, queries of sensitive data, and access events. Performance should track audit trail system health: are all events being captured, is audit logging complete, do logs contain required information. Systems that drop audit trail entries create compliance gaps: if a regulatory audit asks for evidence of who accessed patient data on a specific date, missing entries prevent proving compliance.

Implementation Planning and Regulatory Alignment

Implementing Performance Analytics in life sciences requires alignment with regulatory and compliance teams. Early in implementation, involve compliance leads to identify which metrics matter for regulatory reporting. Define performance targets that match regulatory requirements: if regulations require very high uptime for critical systems, set aggressive performance targets reflecting that requirement.

Plan rollout to prioritize systems with high regulatory impact first. Regulatory reporting systems and laboratory systems should have comprehensive monitoring before general business systems. Document how analytics support your compliance posture and use analytics to demonstrate to auditors that systems meet regulatory requirements for availability and audit trail completeness. Regular reporting to compliance teams ensures analytics remain aligned with evolving regulatory needs.

Write each indicator’s calculation rule before its chart exists

A service owner in a regulated site will be asked, sooner or later, to explain why an availability figure dropped or rose. If the calculation rule for that figure was never written down, the answer becomes a debate about the spreadsheet rather than a discussion of the system.. Before any Performance Analytics for ITSM indicator is published, write its rule in plain words: what counts as downtime, which records supply the start and end times, which events are excluded, and who may change the rule. Only then build the widget that displays it.

The rule is what makes the indicator defensible. Two sites can both report availability of a validated system and mean different things, because one excludes planned work and the other does not. Neither figure is wrong, but a quality review that compares them will reach the wrong conclusion unless the rule travels with the number.

Tiering systems so the indicators mean the same thing across them

A single availability target for every system in the estate will often be wrong for many of them. Group validated systems into tiers according to what an outage does to production, testing or regulated records, and set each tier’s indicators separately. A release system and a historical reporting tool should not share a dashboard row, because a shared row invites the reader to compare figures that carry different obligations.

The tier definition belongs to the quality function and the service owner together. The service targets for each tier belong in service level management. Store it as a field on the service record so that the indicator uses the tier automatically and nobody types it into the report by hand.

1. Define the tiers in words that a quality reviewer can apply to a new system without asking the IT team.
2. Assign every validated system to a tier, and record who approved the assignment.
3. Attach the tier-specific indicators and their calculation rules to the tier, not to individual systems.
4. Review the assignments when a system changes its role, for example when a reporting tool begins to feed a release decision.

Reconciling the dashboard with the incident and change records

Performance Analytics reads from the same tables that people write to during an incident, so an error in how those records are kept flows straight into the chart. A pattern to watch for is a short outage that is logged as three separate incidents, each with a partial duration, so that the indicator looks better than the event felt. Check the reconciliation before trusting a trend. Pick one month, list every outage that the operations team remembers, and confirm that each one appears in the indicator with the right start, end and class.

Repeat the check after any change to how incidents are categorized or closed. A change that looks like tidying up a category list can move events out of the unplanned class and quietly improve the figure. Record such changes in the indicator’s rule history, not only in the configuration log. Planned windows should trace back to an approved change enablement record.

Keeping indicator changes visible and reviewed

Indicator definitions change over time, and a regulated site needs to show when and why. Version each rule. When a rule changes, keep the previous version attached so that a historical trend can be read on the rule that applied at the time. Restrict who can edit the rule, and require the quality function to approve any change that affects a validated tier.

Use the quarterly or monthly quality review to look at the trend and the rule together. Turning those reviews into improvement actions is a separate step, and continual improvement management is the place to set it up. A figure that improves only after a rule change needs a different conversation from one that improves through fewer unplanned failures, and the review should say which one it is.

What not to measure with these indicators

Avoid indicators that reward a team for closing incidents quickly when the quality of the closure cannot be seen. Mean time to close is a poor target for a validated system, because a faster close can mean the restoration was not checked. Do not publish individual engineer rankings from these figures in a regulated setting; they can create pressure to avoid logging faults rather than fixing them. Measures of restoration and of repeat failures tell a more honest story.

Frequently asked questions about performance analytics in regulated IT

Should planned maintenance count against availability?

Report it separately. Planned downtime with an approved change record belongs in its own figure, and unplanned downtime should be the number that the service owner is held to. Combining them hides how often the team is forced to stop systems without warning.

How often should the tier assignments be checked?

Check them when a system changes its use, and at each scheduled quality review. A static assignment becomes wrong as soon as a system is used for a new purpose, so treat the review as a trigger for change rather than a formality.

Can we share the dashboard with the quality auditors directly?

Share the view that uses approved rules and the rule history, not an ad hoc export. Auditors will ask how a figure was produced, and a shared view that cannot show its rule invites a longer discussion than the figure itself.

What do we do when a trend looks good but the incident notes suggest otherwise?

Treat the discrepancy as a finding. Check whether events were split, recategorized or closed early, correct the record where the error is clear, and record the correction in the rule history. Do not adjust the indicator to match the notes without a documented reason.

Agreeing the indicator set

Setting tiers and writing calculation rules before any further dashboards are built saves a good deal of rework.