The increasing complexity of networking environments
Network managers face the challenge of managing increasingly complex networking environments. The telecoms industry has undergone a fundamental transformation with the decline of traditional landlines and the rapid evolution of technologies such as smart devices, Voice over Internet Protocol (VoIP), the Internet of Things (IoT), 5G, and cloud computing. In parallel, modern enterprise networks have evolved from being mainly hardware-driven to a software-driven fusion of virtual and physical networks. Network managers now have sophisticated control of their networks and need to finely balance the sensitive, real-time traffic of voice and video alongside the data demands of applications such as ERP workloads, email, and reporting.
Connectivity is now a basic need
We live in a data-driven era and our inexhaustible demand for data is driving changes in the way enterprises manage their infrastructure. Connectivity is now a basic need. Not only do we not tolerate down-time, but we have an expectation of consistent fast bandwidth and low latency. With keen competition in the market, customer experience management is now a vital focus for telecoms and unified communications providers.
Growing from reactive, to proactive, to cognitive
IBM describes the increasing maturity level of operational support as growing from reactive, to proactive, to cognitive 1. In the past, network operations were managed reactively with the support team called into action when an issue was reported. The analysis and troubleshooting only began after the service delivery was interrupted, and the focus was purely on restoring service. The time to investigate and understand the cause of the issue was limited. Monitoring tools have enabled operations centers to become proactive by using key performance indicators (KPI) to measure performance and predict problems before the quality of service is impacted. The evolution to cognitive networking takes things a step further.
Cognitive networking involves the application of artificial intelligence and automation to network operations and network service virtualization. Two key focuses are network optimization and preventative maintenance. Cognitive networks apply sophisticated algorithms to a vast array of historical data and use machine-learning techniques to anticipate future issues and automate the optimization of the network. Over time, cognitive networks can learn to anticipate issues, propose solutions to support technicians, and even automatically apply fixes before any degradation in service.
Voice traffic is particularly sensitive to degradations in service as conversations happen in real-time. Corrective adjustments that were originally made in a reactive mode, such as gain adjustment, buffer size adjustment, Quality of Service (QoS) priority setting, and route selection, can now be done in a preventive mode with advanced monitoring. In cognitive mode, a self-regulating network will respond to changing demand patterns by making these types of adjustments automatically in real-time - enabling better conversations based on machine learning and AI responses.
Will cognitive networks eliminate the need for monitoring tools?
No. Monitoring tools are still vital for measuring performance and detecting changes over time. Cognitive network solutions need to understand the network conditions and rely on the historical data and alerts generated by monitoring tools. The key benefit is that cognitive computing can turn data into knowledge by leveraging data from different sources, identifying patterns and correlations, and developing expertise over time. It all begins with the data, so access to good quality, insightful data is crucial.
Faced with managing complex networks under intense demand from consumers, network managers are on a journey moving from reactive, to proactive, to cognitive management of their infrastructure. In the future, the application of cognitive computing could help providers achieve zero downtime and consistent quality of service for consumers, but effective monitoring tools will still be required to create the vital knowledge base for machine learning and decision-making processes.
Use Spearline as part of your armory in the transition to cognitive management of your network. Spearline gathers vital call quality data about connection success, audio quality, post dial delay (PDD), touch tone (DTMF) success, and latency. Our comprehensive call records enable you to form a complete picture of the performance history of your contact numbers. Our analytics facilitate the identification and root cause analysis of issues, and our reports allow you to benchmark the performance of your telecoms network. Spearline can give you the crucial insights you need not only to proactively manage your network but also to accelerate improvements in your customer experience.
New to Spearline?
If you are new to Spearline and would like to find out more about how you can benefit from our platform, we would love to speak to you! Please send us a brief message and we will be in touch with you shortly.
Spearline is the leading network intelligence company in the telecommunications industry. Our platform enables enterprises and telecommunications service providers to test connectivity and quality on global telecoms networks, testing automatically at volume. If you are interested in our platform, or would like more information, please get in touch with us.
1 Cognitive Network and Service Operations, IBM Telecommunications, Media and Entertainment