Analytics for Network Operations

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