Data Science & Machine Learning

Detect anomalies before they affect critical processes with baseline-triggered alerting and forecasting features.

Machine Learning Based Approach

Our Data Science team builds advanced machine learning models to aid in the understanding and solution of complex network problems. We have been able to predict the impact of storms and other weather events on the performance of the radio access network and we have correlated the performance impact of localised transmission outages on linked cell sites. Using clustering and predictive modeling we can bring insight to your complex data.

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Case Study: Mobile Network Congestion Forecasting for Vodafone

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Predictive Analytics

VisiMetrix models your data to highlight trends, alert on statistical anomalies and makes use of advanced analytics to learn seasonal patterns and dynamically adapt thresholds to expected limits.

Adaptive Thresholds

Powerful thresholding and alerting features ensure that relevant information is presented clearly and in a timely manner, enabling real-time visibility of network data, service characteristics and quality measurements in multiple combinations across all major network technologies.

Event Correlation

Event correlation, service status calculation and automated actions enable VisiMetrix to proactively alert about a potential problem and understand its business impact. IT teams can use this information to implement dynamic capacity planning and disaster prevention procedures.

Anomaly Detection
and Baselining

VisiMetrix learns how a given service should behave over time and is able to report anomalies or unusual network behaviour. With manual or automated baseline calculation and baseline-triggered alerting, VisiMetrix allows you to prevent issues before they become serious problems.

Resource Utilisation Forecasting

By means of advanced analytical models, VisiMetrix helps to forecast IT or network resource utilisation and allows users to perform capacity analysis and predict future requirements. VisiMetrix forecasting features help make better infrastructure investment decisions by identifying the time frame when upgrade decisions will be required and minimizing the risk of underestimating or overestimating infrastructure’s future needs.

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