Network Fault Analytics

Ops-IQ Network Fault Analyticsaddresses alarm management challenges through advanced machine learning (ML) techniques to understand which alarms are truly important and most relevant and will impact customer experience (CX).

Designed for Network & Service Operations Centers, Ops-IQ Network Fault Analytics optimizes OPEX and achieves better CX through its rich feature set. This approach has saved Communications Service Providers (CSPs) upwards of $10 million a year in OPEX.

Don’t Miss Real Alarms

Network Operations Centers (NOCs) receive millions of network alarms each day. These alarms force an already overwhelmed operator to manually employ alarm suppression or rules-based filtering techniques. Ops-IQ Network Fault Analytics reduces foundational MTTA (mean-time-to-acknowledge), MTTD (mean-time-to-diagnose), and MTTR (mean-time-to-resolve) to achieve dramatic operational efficiency improvements.

Network Fault Analytics
Network Fault Analytics

Automate Root Alarm Detection

Root alarm detection and the consolidation of symptomatic alarms ensure that the NOC technicians and engineers are not wasting time investigating multiple independent trails. Rather, they can stay focused on resolving critical issues or incidents affecting subscriber quality-of-experience (QoE).

Easily Visualize Relationships Between Alarms

By leveraging Bayesian network analysis, Ops-IQ Network Fault Analytics automatically discovers the relationships between the alarms to form a set of alarm families for further root issue analysis.

Improve Alarm Suppression Rates

The parent alarm is the root or progenitor alarm which leads to the other alarms in the family. Through our unique approach, we can dramatically reduce the number of alarms to be investigated by the NOC technicians and engineers.

Escalate Alarms for Predicted Incidents

Using ML-based probabilistic algorithms, network alarms with a high probability of leading to network incidents are automatically prioritized/escalated, so that the highest risk alarms predicting subscriber QoE problems are trouble ticketed and more efficiently resolved without relying on network inventory, topology or static rules.

Maximize Alarm Noise Reduction

Network alarms with a low probability of leading to network incidents are automatically deprioritized, while alarms correlated with scheduled maintenance events and tickets are automatically suppressed, reducing alarm noise to the utmost.

Stop Relying on Network Topology

Our approach to building relationships between alarms in alarm families and prioritizing alarms, is network topology-independent, which means it doesn’t rely on a view of the network itself, an essential requirement with network topology data constantly changing and typically refreshed only periodically.

Increase CX Quality Scoring

Truly relevant alarms are uncovered in real time, allowing the NOC to operate more effectively. This results in optimal use of resources and thus reducing the MTTA, MTTD and MTTR for service-impacting issues.

Where Ops-IQ Network Fault Analytics Fits to Seamlessly Complement Existing Workflows in a Typical Network Operator Environment

Guavus Ops-IQ Network Fault Analytics Datasheet

Most Popular CSP Use Cases

This is just the surface. Deep-dive into Network Fault Analytics