Proactive Notification
Proactive Notification refers to the ability of systems to detect anomalies, performance degradation, or failure conditions and alert the right people before customers are impacted.
It’s a key component of modern incident management and observability, ensuring that teams can respond swiftly, reduce downtime, and protect user trust.
Level 1 – Initial (Ad Hoc)
Notifications are either absent or based on manual observation.
Teams find out about issues from users or too late to prevent significant impact.
- Alerts are missing, misconfigured, or ignored
- No distinction between noise and meaningful signals
- Teams lack confidence in alerts, leading to alert fatigue
- Incidents are often escalated without context or actionable information
- Critical issues go unnoticed until they cause business disruption
Level 2 – Managed (Emerging Practice)
Basic alerting exists, typically tied to infrastructure or uptime.
Some incidents are caught early, but coverage is limited and responses are inconsistent.
- Static thresholds trigger alerts for known conditions (e.g. CPU > 90%)
- Email or Slack notifications may be in place, but are uncoordinated
- Alerts may be noisy or fire after customer impact has already occurred
- Runbooks or escalation paths may exist but are not followed reliably
- Alert ownership is unclear or siloed by role (e.g. ops only)
Level 3 – Defined (Standardised)
Notification practices are structured, actionable, and aligned to system health and business impact.
Teams receive meaningful alerts that support rapid triage and response.
- Alerts are routed based on service ownership and severity
- Notifications are tied to SLIs and SLOs, not just technical metrics
- Alert fatigue is managed through tuning and suppression strategies
- On-call rotas and escalation paths are clearly defined and followed
- Alerts include rich context (logs, traces, linked dashboards)
Level 4 – Quantitatively Managed (Measured & Controlled)
Notification systems are tuned based on performance data and feedback.
Alert quality, relevance, and response effectiveness are measured and optimised.
- Metrics include false alert rate, alert-to-acknowledge time, MTTR
- Teams measure alert volume per service and engineer
- Intelligent alerting (e.g. anomaly detection, rate of change) is used
- Automated escalations and incident triggers improve responsiveness
- Post-incident reviews track whether alerts were timely and helpful
Level 5 – Optimising (Continuous Improvement)
Proactive notifications are intelligent, adaptive, and fully integrated into incident and product feedback loops.
The system detects and responds to failure patterns before customers notice.
- Predictive alerting identifies degradation before failure
- Notifications adapt to usage patterns and business impact
- Alerts are simulated and tested regularly (e.g. chaos engineering)
- Alert quality is continuously improved through feedback and automation
- Notifications inform product priorities, engineering focus, and platform evolution