Solution enables CSPs to streamline operations and deliver a better customer experience on 5G, SDN/NFV and IoT networks
San Jose, CA — August 28, 2018 — With the move to 5G and growth in SDN/NFV and IoT, the complexity of communications service provider (CSP) networks is growing exponentially as is the corresponding number of network alarms CSPs must contend with every day.
Guavus today announced a new product, called Guavus Alarm IQ, that harnesses the power of artificial intelligence (AI) and advanced analytics to automatically classify and predict which network alarms will lead to incidents and, as a result, reduce the number of alarms network operators need to pay attention to by more than 90%. This dramatic shift enables network operations center (NOC) teams to prioritize their resources and deliver a much higher level of service to their internal and external customers at a reduced cost.
“Most NOCs receive a minimum of one alarm per second, about 86,400 a day – yet only 2-3% actually lead to true incidents or problems. The rest are simply noise that can and should be ignored,” said Stephen Spellicy, Guavus Vice President of Products and Marketing. “The problem is distinguishing the noise from the signal. Standard alarm de-duplication and management tools classify 10-20% of the alarms as critical, which is still too many to handle in one day. In actual field trials of Alarm IQ, network operators were able to reduce the number of alarms by more than 90%.”
Said Adaora Okeleke, Senior Analyst at Ovum Research, “CSPs must identify efficient ways to manage network alarms with a view to resolving the most critical network issues. Alarm IQ uses AI-based analytics to get a better understanding of which network alarms are service-impacting, enabling CSPs to transform the way network issues are addressed and improve customer experience. It also helps reduce the time NOC teams spend on troubleshooting network failures, so they can focus on more strategic initiatives.”
Using AI to Beat Alarm Fatigue, Focus on What’s Most Important
Using advanced AI and machine learning algorithms, Alarm IQ automatically analyzes streams of alarms, classifies them, and predicts which alarms will lead to incidents so NOC operators can focus on a much smaller subset of relevant alarms and confidently ignore the rest.
In addition, Guavus Alarm IQ provides:
- An 84%+ reduction in Mean Time to Understand (MTTU) issues.
- Alarms correlation and grouping based on root issue to reduce alarm overload.
- Automatic periodic model re-training and model adjustment for accurate incident prediction.
- Integration with leading OSS tools such as IBM Tivoli Netcool Omnibus and leading incident management products such as BMC Remedy. No training of NOC staff is required as Alarm IQ processes alarms prior to display in a NOC’s existing UI.
Alarm IQ is built on the Guavus Reflex® AI-powered analytics platform, which supports a wide range of applications as well as customizable ‘self-service analytics.’ CSPs can take advantage of Alarm IQ for alarm management and easily expand to leverage Reflex® AI-based analytics for advanced network planning and operations, mobile traffic analytics, marketing, customer care, security and IoT – or to build their own custom applications.
Guavus is at the forefront of AI-based big data analytics and machine learning innovation, driving digital transformation at 6 of the 7 world’s largest telecommunications providers. Using the Guavus Reflex® solution, customers are able to analyze big data in real-time and take decisive actions to lower costs, increase efficiencies and dramatically improve the end-to-end customer experience – all with the scale and security required by next-gen 5G and IoT networks.
Guavus enables service providers to leverage both customizable ‘self-service analytics’ and out-of-the-box analytics applications for advanced network planning and operations, mobile traffic analytics, marketing, customer care, security and IoT. Discover more at www.guavus.com and follow us on Twitter and LinkedIn.
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Guavus PR & Analyst Relations