Autonomous correlations are also the only way to effectively gauge real customer impact. Sleeping cells are a great example, as there is no ONE specific KPI that indicates the cell is not working: multiple metrics, including drop in downlink throughput, spikes in ERAB drops, and increases in S1 failures, need to be correlated in order to identify a sleeping cell. When metrics arent actively correlated by the carrier, the real monitoring is being done by the customers that call and complain. Using AI-based anomaly detection with advanced correlation capabilities is the only way to raise such an alarm. The correlation will use different KPIs (such as drop in downlink throughput) and will compare it to the data to analyze the actual root cause.
Reduce alert fatigue. Correlation analysis reduces alert fatigue by filtering irrelevant anomalies (based on the correlation) and grouping correlated anomalies into a single alert. This dramatically reduces one of the pains many CSPs face today managing hundreds, even thousands of separate alerts from multiple systems, many of which stem from the same incident.
Current CSP correlation challenges
Despite their significant advantages that directly translate into improved ROI, AI-based correlations are far away on the roadmap for many CSPs. The main hurdles faced by CSPs are the complexity of the network, limited resources and internal knowledge, and an overwhelming number of potential rules, leaving the majority of companies stuck at the POC stage.
However, most correlation solutions are rule-based and rely on static threshold models that are best suited for data-at-rest, and therefore cannot provide the benefits inherent in the latest technologies. With older solutions, correlation rules are limited and mostly applied on fault alarms which are reactive instead of on granular time-series data. Of course, minimizing TTR can only be achieved by anomaly detection and correlation analysis carried out on live data streams, requiring advanced correlation techniques.
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