Metrics That Matter: What to Observe Beyond Uptime by Mark Hewitt
For decades, enterprise reliability was measured through uptime. If systems were available and outages were rare, leaders assumed operations were stable.
That approach no longer works.
Modern enterprises run on distributed systems, complex integrations, continuous delivery cycles, and expanding data and AI dependencies. Under these conditions, uptime becomes a lagging indicator. It tells leaders the system was accessible. It does not tell them whether the system was healthy, trustworthy, governable, or resilient under change.
In 2026, uptime is table stakes. Operational confidence requires a broader set of metrics.
Executives need to observe what matters before disruption occurs. They need leading indicators that reveal fragility, risk accumulation, and readiness to change.
This is the shift from uptime monitoring to engineering intelligence.
Why Uptime Is No Longer Enough
Uptime was effective when systems were monolithic, dependencies were limited, and release cycles were slow. In that world, failures were often binary. A system was up or down.
In modern environments, failures often emerge differently.
systems remain “up” while customer experiences degrade
data pipelines drift while dashboards still populate
critical services slow down while alerts remain quiet
security posture weakens through small changes over time
dependency failures cascade without triggering traditional monitoring thresholds
AI outputs degrade subtly while still appearing plausible
In these scenarios, uptime remains high while trust and performance decline.
This creates a dangerous illusion of stability.
Executives do not need visibility into whether systems are technically available. They need visibility into whether systems are behaving correctly, whether data is trustworthy, and whether operations can withstand change.
The New Measurement Goal: Operational Confidence
Operational confidence is the enterprise’s ability to answer four questions at any time.
Are our critical pathways healthy and stable right now?
Are we accumulating fragility or risk beneath the surface?
Can we change quickly without introducing instability?
Can we detect and correct issues before customers, regulators, or revenue are impacted?
If leaders cannot answer these questions, the enterprise is operating on assumptions.
Metrics that matter should make these questions measurable.
The Metrics That Matter Beyond Uptime
Executives should consider three categories of metrics.
Service health and resilience metrics
Delivery and change risk metrics
Data and intelligence trust metrics
These categories provide a balanced view of operational continuity.
1. Service Health and Resilience Metrics
Uptime measures availability. Resilience metrics measure survivability.
Key resilience metrics include:
Latency and performance degradation trends
Error budgets tied to customer experience, not infrastructure
Mean time to detect and mean time to recover
Incident frequency by critical pathway
Blast radius measurement for incidents and failures
Dependency health scores for key services
Recovery readiness score based on runbooks and rehearsal frequency
These metrics show whether systems can withstand stress and recover quickly.
2. Delivery and Change Risk Metrics
Many enterprise disruptions are caused by change, not random failure. A modern enterprise must measure change risk continuously.
Key change metrics include:
Change failure rate
Deployment frequency combined with defect rate
Lead time from commit to production
Time spent in rework and remediation
Release rollback frequency
Percentage of changes passing automated governance gates
Risk-weighted backlog for resilience and security remediation
The goal is not to slow delivery. The goal is to make delivery safe.
Executives should be able to see whether the enterprise is moving quickly with confidence or moving quickly with increasing risk.
3. Data and Intelligence Trust Metrics
Data quality and drift are now operational risk factors.
AI systems and decision-making workflows rely on data integrity. Trust metrics should therefore be treated as operational indicators, not analytics concerns.
Key trust metrics include:
Data quality score for critical datasets
Drift indicators for key pipelines and business definitions
Time to detect and correct data anomalies
Lineage completeness for datasets used in reporting and AI
Access control compliance for sensitive data
Model and output monitoring metrics where AI is deployed
Confidence scoring for AI outputs in critical workflows
These measurements determine whether leaders can trust operational reporting, forecasts, compliance outputs, and AI decisions.
A CFO and COO Lens: Metrics Are Cost and Risk Controls
Executives often treat operational metrics as technical telemetry. In reality, these metrics are financial and operational controls.
When change failure rate rises, cost to change rises.
When recovery time is slow, downtime cost increases.
When data quality declines, forecasting and compliance risk increase.
When dependencies are unknown, incident impact grows.
When governance is manual, delivery slows and audit cost rises.
Metrics that matter create a direct line of sight from operational conditions to enterprise cost and exposure.
The Executive Dashboard for Engineering Intelligence
Executives do not need hundreds of metrics. They need a small number of measures that capture the health of critical pathways and the organization’s readiness to change.
A practical executive dashboard should include:
Critical pathway health index
Change failure rate and recovery performance
Dependency risk coverage
Data trust score for enterprise reporting and AI
Governance automation coverage
Operational risk trend line over time
This dashboard becomes the interface between engineering intelligence and executive decision-making.
It answers the most important question.
Can we move forward with confidence?
Take Aways
Uptime is not the wrong metric. It is simply insufficient.
Reliable enterprises measure far more than availability. They measure fragility, change readiness, data trust, recoverability, and governance effectiveness.
These metrics do not exist for reporting. They exist for operational control.
Engineering intelligence turns measurement into decision intelligence. It makes the enterprise observable, governable, and resilient.
That is how leaders reduce surprises, reduce risk, and build operational confidence at scale.