- Enhance customer experience
- Reduce downtimes due to network faults
- Improve cost savings
How does Network Operations help you?
Network operations solution leverages AI/ML mechanisms to predict network faults proactively and take necessary pre-emptive actions that help improve customer experience and satisfaction.
- Reduces downtime due to network faults and helps deliver a better quality of experience through AI-enabled monitoring mechanisms.
- It leads to faster resolution of issues by identifying the potential failure or issues for the selected network components.
- Ensure profitable growth and improve cost saving by leveraging AI/ML techniques in network management.
- Drive efficiency in network operations without human efforts through AI-enables automation across the processes and help deliver a better customer experience.
- It leads to faster root-cause analysis and accurate forecasting of network disruptions through predictive modeling.
Features of Network Operations
Deliver excellent customer experience by forecasting and addressing anomalies in network data
Leverages AI/ML to predict network faults in real-time or near real-time and take preventive maintenance actions beforehand
Identify the root cause of network alarms and automatically categorized them into fault tickets by finding correlations across multiple events sources to resolve the issue faster
Track KPIs in real-time and discovers nontrivial KPIs relationships and anomalies through built-in dashboards and advanced visualizations
Identify root cause and classifies network data based on historical behavior. Through adaptive mechanisms, it automatically suggests groupings, tagging, and potential root-cause alarms
Through automation and autonomy, it drives the efficiency of the network operations without human intervention
Your Questions Answered
We ‘ve put together some frequently asked questions to give you more information about what we offer.
Network Operations solution leverages AI/ML to predict network faults proactively using network data and prescriptively take remediation action beforehand according to the changing conditions. It enhances customer satisfaction and experience, improves cost savings, reduces downtime, drives efficiency, ensures profitable growth, and leads to higher network service availability.
An AI/ML model built on-network data set gives operators a propensity score for potential failures or issues for selected network elements or components. Effective fault prediction models built using alarms data, node-wise faults details, trouble tickets data, and issue resolution data. It enables faster root cause analysis, speedy issue resolution, and accurate forecasting of network disruptions. By enabling automation and lowering service disruptions or downtimes, AI drives a better customer experience.
Both external and internal factors are responsible for predicting network faults. The external factors are weather data such as temperature and social network information such as Twitter, blogs. The internal factors are failure information such as alarm, Syslog, trouble tickets, and network configuration information like topology performance counters.
The other solution offered by HyperSense AI Studio includes Business Assurance, Fraud Management, Revenue Forecasting, Customer Churn Prediction and Prevention, Customer Hyper Segmentation, Revenue Maximization, Product & Price Optimization, Customer Support Prediction, and Network assets and capacity planning.
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