Telecommunications is a fast-paced field that changes constantly. It is more than a full-time job just keeping up with all of the emerging innovations that affect this industry. One of the fascinating aspects of this evolution is the fact that telecom relies heavily on companion technologies, emerging practices, and new methodologies that enable novel applications with seemingly science-fictionesque capabilities and functionality.
In this article, we talk about some of the most important technologies and methodologies that are changing the telecom landscape and which are expected to continue to do so in the coming years.
Some trends revolutionizing telecom
There are many emerging technologies in telecom that many consider revolutionary, such as 5G, WiFi6, the cloud, IoT, and advanced collaboration services, to name a few. But these are all specific technologies that have been developed and can be applied. There are other trends that are arguably as important or even more important, that are less technological and more procedural, such as a set of clearly defined practices and architectures that enhance telecom to levels never seen before.
It is these types of innovations that we will explore here. Specifically, we’ll take a look at how DevOps, edge computing, and AI and machine learning have begun to play a major role in telecom in general, and in particular, to business-oriented telephony systems.
Quick intro to DevOps
The word DevOps is derived from “software development” (Dev) and “IT operations” (Ops). It is defined as a set of practices that combines these two aspects of the systems development lifecycle for the purpose of shortening that lifecycle to provide continuous delivery of high-quality software.
The application of DevOps essentially involves a paradigm shift in the organizational culture of companies that transform the way in which operations, software developers, and testers collaborate during the development and delivery process. The result is a more streamlined procedure by which software is developed, tested, deployed, and upgraded.
DevOps in telecom
Telecom used to be focused primarily on hardware, with physical devices deployed to perform specific tasks. If something broke or had to be upgraded, it was either repaired or physically removed and replaced. Telecom has undergone a vast shift from primarily hardware to primarily software. This can be appreciated by simply perusing all of the cloud-based services available to businesses today.
It is primarily because of this increased use of virtualization platforms that DevOps has become more meaningful to telecom. Cloud-based telephony systems are a prime example of how DevOps can streamline and improve their deployment to the end user, as well as their maintenance and administration throughout their life cycle. Telecom providers of all types are looking to generate greater operational and network efficiencies by combining software development and telecom systems across their work environments. Historically, where changes in network operations took place over decades, they can now occur on a software development timescale, on the order of weeks or months. And if DevOps principles are adhered to, this can happen without risking security, performance, or reliability.
One of the characteristics that you may want to examine when it comes to choosing your cloud-based telephony provider is whether the provider has adopted a DevOps operational culture. Asking such questions not only helps you evaluate their services, but it also shows you know what you’re talking about when you come to the table.
Edge computing
Network services are becoming more demanding, and split-second response times are becoming all the more necessary. As the need for speed is continually increasing, it becomes obvious that simply delivering more computational power is not always a solution. We come to the point where the speed of light itself becomes the limiting factor! More precisely, the limiting factor is the speed with which information traverses the medium, be it wireless, copper, or fiber optics. This speed does indeed approach the speed of light but can still be too slow for some of today’s most advanced applications. This transmission delay on a network is known as latency.
Edge computing resolves this problem by bringing the computational power physically closer to the end-user device. In such a scenario, the physical distance that data must travel from the system processing it to the device expecting it (or more simply put, from the server to the client), is dramatically reduced, and so is the latency of that transmission. In addition, this reduces bandwidth demand in the core network, since data traverses a smaller section of the network.
Computational power has traditionally been thought of as a discrete resource, available within a CPU found within a server. Edge computing involves a paradigm shift that spreads that resource out among multiple devices in different parts of the network to achieve dramatically lower latencies. For this reason, edge computing is often referred to as fog computing, giving the impression that the computational resources dynamically extend themselves to the areas of the network that need it, much like fog can move over and cover large swaths of land.
Edge computing in telecom
An important application of edge computing is in conjunction with 5G, where some of the most cutting-edge applications requiring ultra-low latency, including vehicular autonomy and remote medicine, can benefit.
But edge computing is also something that we’ll see incorporated into more common cloud-based applications, such as business telephony systems, simply because it reduces delay, reduces the bandwidth load, and delivers reliability that surpasses even the legendary uptime of the traditional PSTN. The result is a highly resilient, efficient, and ultra-fast network that can serve even the most demanding applications.
AI and machine learning
We’ve already spent some time talking about AI and how it is poised to revolutionize various areas of telecommunications, and in particular, video collaboration systems and business phone systems.
But the application of AI and machine learning in telecommunications is enormously broad, and it is expected to vastly increase in the coming months and years. Some of the most notable trends that result from applying AI to telecommunications include:
- Automated network maintenance and optimization – As both enterprise and provider networks become more intricate and multifaceted, the complexity of their administration and optimization also increases. Fixing network malfunctions can become time-consuming and costly, while optimizing such networks requires continual analysis of ever-changing traffic patterns and network requirements. AI delivers intelligence to the network that is able to respond proactively to detected conditions, thus delivering predictive maintenance that resolves problems before or as they occur. In addition, dynamic network optimization can take place to ensure that a network is operating at peak efficiency regardless of the continually fluctuating traffic patterns.
- Support services – AI is now smart enough to enhance all types of support services, including help desks, contact centers, and online chatbots. AI can respond to some of the most basic queries customers may have using speech recognition in a contact center environment, or in the form of chat bots that can interact with an online user using text messages. AI is also at a point where VoIP assistants can enhance collaboration sessions and can serve in various capacities, taking care of some of the most mundane tasks that receptionists and personal assistants often handle.
- Fraud detection – Telecom fraud of all types has cost network operators billions of dollars a year. AI, with its extraordinary analytical and learning capacities, can detect and prevent the vast majority of telecom fraud. AI-powered fraud analytics is an area that is expected to become mainstream very soon, as many services are employing it to fortify their networks and applications.
- Process automation – Telecom companies have a lot of back-end overhead with processes and procedures that are not directly related with the technical aspect of company operations. These include things like data entry, billing, order processing, maintenance schedules, and other tasks that take time and require manual work. AI can automate many of these operations, streamlining processes and reducing costly man-hours.
Conclusion
We are at the beginning of what is expected to be a decade that will bring about unprecedented innovation in telecommunications. These emerging trends, along with other pioneering tech, are expected to create an extraordinary integrated telecommunications ecosystem, the results of which, at this point, can only be imagined.
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