The truth about static thresholds
Static thresholds lead to poor alert management, lack of verified issues, numerous false positives, and increased time to resolution. Netenrich’s deviation management process applies anomaly detection algorithms to detect noisy alerts, analyzes behavior of an instance, and quickly notifies you about abnormalities.
- Alert‐fatigue leads to service downtime, costing companies millions.
- Start baselining of data on dynamic thresholds to trim event noise.
- Detect anomalies to predict alerts from potential events and triage before service impact.
- Leverage machine learning-driven event correlation to reduce MTTR.
DETECT ANOMALIES ACCURATELY
Static baselines and fixed thresholds classify all variations as an anomaly and warrant human attention. Prioritize alerts, eliminate false alarms, and focus on what’s business-critical with dynamic thresholds.
- Reduce IT overhead and inaccuracy associated with static thresholds and manual alert validation.
- Curb noise in your infra by analyzing historical logs, incident, and missed SLAs across disparate sources.
- Automatically detect an abnormality and determine the best baseline to be used depending on the behavior of an instance.
MANAGE ALERT FATIGUE
Remove repetitive and redundant alerts that create alert fatigue by harnessing targeted intelligence. Blend machine-learning with humint to vet baseline configurations and empower your teams with qualified alerts.
- Identify patterns across multiple dimensions like traffic, usage, volumes, anticipated volume peaks, repetition, and missed alerts.
- Remediate issues before services are affected with early warnings of baseline deviations.
- Optimize support costs and prevent resource shortages with intelligent alert routing, assisted by humans.
SET UP ALGORITHMIC, ADAPTIVE ALERTS
Set up notifications that are triggered across a range or a confidence band. Identify ranges of normalcy, suppress fake events, and recognize true abnormalities to make faster decisions.
- Quantify uncertainties posed by incidents with accurate confidence bands via machine learning-enabled pattern and cluster analysis, proofed by experts.
- Identify redundant scenarios by continuously finetuning thresholds for ongoing operations.
- Manage large volumes of alerts and reduce false positives with dynamic limits.
Actionable alerts easier to detect and resolve.
Remove redundant alerts.
Support critical customer issues faster.
Prevent resource shortages.
No service impact.
Improve service quality.