Advanced anomaly detection enables traditional CSP network and service ops to integrate AI-driven automation and intelligent operations into their workflows. Since network data is so complex and dynamic, AI/ML-based autonomous solutions are critical for achieving business outcomes and avoiding blind spots: static monitoring approaches based on dashboards and manual thresholds aren’t sensitive, robust or agile enough to withstand this challenge. 

AI-based anomaly detection effectively augments and automates early detection, predictions and decision-making in operations and in business processes where humans can’t deal with the volume or velocity of data. Improving overall time to detect invariably leads to quicker resolution of incidents and thus results in reduced costs associated with outages, and aids in the prevention of lost revenue and brand impact. 

This is especially true with the industry’s massive shift to new service offerings enabled by 5G and edge computing technologies. In modern mobile networks, engineers are already overwhelmed by data, and this is only going to worsen as 5G is deployed. Managing 4G RAN with 5G components and NFV-based networks creates another layer of complexity for network monitoring. OSS/monitoring systems are vendor- and technology-oriented in many cases. KPIs and alerts for 4G and 5G are different, requiring a steep learning curve from NOC/SOC teams. 5G adds many challenges, including the volume of data, the real-time requirement with URLLC, and the added complexity of the infrastructure and the virtualized environment. For that reason, 5G services monitoring must be based on real-time data, a more accurate view of the dynamic network and service topology, powerful diagnostics, and AI-driven predictive capabilities.

AI-based anomaly detection solutions are capable of analyzing multiple dimensions of data sources, looking at cell, subscriber and device level KPIs, monitoring for faults in network equipment, and correlating alerts across domains for noise reduction and root cause analysis. This gives engineers a transparent view of both network & service performance and subscriber experience — at any given time.


Features and benefits of advanced anomaly detection

In the network operations context, every network generates millions of time series data, measuring all aspects of the network. Anomalies can cause service degradations and system-wide outages/incidents. Discovering these anomalies and identifying the technical root cause to fix incidents is a key objective of network operations. 

Advanced anomaly detection is geared at finding and fixing incidents as they are starting to happen and before they become an incident. Advanced monitoring solutions use a Machine Learning approach to monitor 100% of data, learn every metrics’ behavior, and provide spot-on alerts on critical failures. By identifying anomalies within network big data, these solutions enable telecoms to resolve issues before they generate impacts or outages, to deliver a better customer experience, and to decrease revenue leaks and customer churn. 

ML-based advanced anomaly detection solutions help operations and NOC/SOC teams become proactive in their ability to identify service degradations and outages by providing: 

Autonomous monitoring is more accurate. AI-powered anomaly detection is 100% autonomous for 100% of the data. Rather than setting manual thresholds, these solutions rely on machine learning algorithms to autonomously create a dynamic baseline for each metric. They continuously analyze 100% of the network data (regardless of the CSPs original data scheme or silos) to understand normal metric behavior under different conditions and seasons.

Real-time analysis provides faster time to resolution. Correlations are crucial for understanding metrics in context, and with dynamic baselines for network data, advanced anomaly detection can correlate incidents to root causes faster than traditional monitoring tools. Events are correlated across metrics, dimensions and other concurrent processes. Once root causes are identified, real-time analysis provides a prioritized set of opportunities to cut time to remediation.

Cross-silo monitoring provides holistic visibility of the network. By correlating between metrics across network layers, applications, databases, storage, CRMs, monitoring and analytics tools, advanced monitoring solutions sees beyond traditional data silos, enabling faster time to resolution, improved network availability and a streamlined customer experience.  


The first step on the road to zero-touch

As network monitoring and alerting platforms mature there is a growing expectation that they will go from anomaly detection to full remediation, without a human in the loop. While zero-touch technologies are still in their infancy, over the last five years monitoring telecom networks has evolved to the extent that autonomous remediation (sometimes referred to as “the action phase”) is likely to become a dominant feature for leading CSPs. 

But to get there, robust machine learning capabilities are key. AI, and specifically unsupervised machine learning, enables the transformation of traditional network and service operations towards automation and intelligent operations through three crucial steps: anomaly detection, correlations & root cause analysis, and, finally – remediation. AI-based anomaly detection is the first and critical step in taking telecoms to the next level of performance, service, availability,  and customer experience.

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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