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eoMind Streaming Analytics

Real-time Subscriber Anomaly Detection


Can you improve MTTR using the same resources on a network that is getting more complex?

Service Operators know that they need to work smarter to keep up with the increasing demands on operational teams. Exciting new services and technologies are being rolled out across the world but are adding complexity to the already difficult task of managing and responding to customer issues and escalations.

Many operators are not looking to make extra investments in technological solutions that require human interaction to progress each step of the way from monitoring, troubleshooting, and finally resolving issues. Though some level of automation is used, their highly skilled staff are addressing some issues, again and again, month after month taking up valuable time which could be better used elsewhere.

Alarming has its own issues as it targets finding major issues on the network, is not subscriber focussed and comes with an inherent delay waiting for data and the alarming threshold to breach. It is very much responding to outages after they appear and when the alarms are pushed to teams to work on them, they may not have a next best action recommended or a count of subscribers affected or even know what to do next.

Key Benefits

Reduce MTTR, with fewer impacted subscribers using Augmented Analytics
eoMind uses streaming analytics to detect subscriber-affecting issues. Using patented machine learning algorithms, eoMind automatically detects anomalies in behavior for subscribers, isolates the root cause and then closes the loop using intelligent automation with a customizable suite of next best actions.

It does it faster, with high accuracy, reduces mean time to resolution (MTTR) and, with cases closed faster, significantly reduces knock-on impacts to subscribers. eoMind is real-time Automated Assurance for service providers around the world looking to move to Smarter Operations.

Reduction in MTTR
Proven reductions of 25% in the meantime to resolve issues for customers freeing up valuable expertize to work on critical issues.

Decrease in impacted subscribers
With issues detected faster and closed sooner, overall knock-on impacts to subscribers are reduced with savings on calls to care and fewer escalations.

OpEx efficiency
250 configurable next best actions available for common issues, saving time and money, with the library of actions garnered from customer installations increasing all the time.

Lightweight, data agnostic, impactful
Focusing uniquely on subscriber issues, eoMind is resource-light yet impact-heavy and can be used to target improvements with Corporates, VIPs, particular technologies or device rollouts.

Key Features

Subscriber issue detection using ML
Tailored patented algorithms scan high bandwidth SIRCA (subscriber impacting root cause analysis) data streams looking for subscriber anomalies. Detection of anomalies are changes to subscriber behavior or experience and are surfaced up quickly, using tailored EWMA (exponential weighted moving average) algorithms for immediate remedial actions.

Drill-down and root cause analysis automation
Automated localization of issues, based on subscriber, error, device, and node are made with a weighted root cause analysis assessment highlighting the most likely root cause.

Real-real-time streaming analytics
Detection of anomalies are predicated on anomalous changes to subscriber behavior or experience and are surfaced up quickly, usually within the first minute and alongside drill-down and root cause, for immediate actions.

Next best action
Sophisticated next best action tooling processes cases automatically and, based on operational and business need, tailors a suite of next best remedial actions closing the loop without human input required for detection, triage, and repair.