AI and machine learning (ML) techniques have witnessed exponential adoption across multiple business sectors such as retail, healthcare, banking, hospitality etc. With the imminence of network function virtualization and software driven networks, as well as digitalization of CSPs,AI is fast becoming part of the Communication Service Provider(CSPs) growth and transformation strategy. The CygNet Smart Predictive Analytics engine use ML/AI techniques to identify patterns of network behaviour that lead to failures of critical eqmt. Taking advantage of the fact that the network is a source of actionable data, the predictive analytics can be used to help CSPs stay ahead of the curve.
Next generation smart service assurance capabilities Use of network data for actionable insights Incorporation of social media feeds, metereological data Prediction of critical equipment failure with high probability
Prediction of MTTR using trouble ticket data Recommender engine for fault resolution at help desk Ensemble learning algorithms Time series analysis techniques for streaming data Offline training, online prediction
Real time technology-driven computation of root cause of failures Automatic inference of customer service impact Incremental online learning algorithm Dashboards for visual depiction of root cause and all impacted services and circuits
Highly scalable, fault-tolerant system Apche Spark with kafka streams Apache Druid to slice and dice data for interactive visualizations Microservices based architecture