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5G connectivity and Internet of Things (IoT) devices are here, and the challenge for CSPs is to manage these technologies to milk the best out of them. Although tedious, these tasks present an excellent opportunity to transform network operations so that they are autonomous, proactive, and contain minimal human intervention.
FREMONT, CA: Innovative new technologies have made running telecom operations more complex than ever. It presents a wide array of challenges such as legacy, multi-vendor, multi-RAN (2G, 3G, 4G, 5G), multi-technology (wireless, wireline etc.), as well as different billing and proprietary hardware-based solutions. These operations often are spread across various organizational segments of a Communication Service Provider (CSP), such as technical, marketing, customer care, product planning and strategy, and billing.
5G connectivity and Internet of Things (IoT) devices are here, and the challenge for CSPs is to manage these technologies to milk the best out of them. Although tedious, these tasks present an excellent opportunity to transform network operations so that they are autonomous, proactive and contain minimal human intervention. However, as network complexities increase due to the rise of 5G and IoT devices, it will become impossible to manage them without the use of cognitive technologies.
Cognitive technologies try to mimic human brain functions by utilizing technologies like natural language processing, data mining, and pattern recognition. They are considered to be a sub-branch of the much broader Artificial Intelligence (AI) paradigm and are capable of performing tasks which are often performed by humans.
The introduction of robotic process automation in telecom networks will make network operations autonomous and enable decisions that were earlier made by humans. It can also help in reducing the Mean Time to Repair (MTTR). However, there is no one size fits all cognitive framework that will suit telecom operations. This is mainly because the technologies used to capture information about the different environments are diverse, domain-specific and use-case dependent frameworks. For instance, natural language processing enables interaction with human users, while robotic process automation performs repeated tasks that were earlier done by humans. Over time, as these systems learn and evolve from historical patterns, they will be able to identify potential bottlenecks and degradation in service easily.
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