Machine Learning to the Rescue: Facilitate Data Management Needs of Telecommunications Industry

Konrad Fulawka - September 10, 2021

Machine Learning to the Rescue: Facilitate Data Management Needs of Telecommunications Industry

The importance of the telecommunications business has been necessary for the development of humankind for decades. But only the year 2020 and the emergence of the COVID-19 pandemic - a pandemic that changed the perception of the world we live in - made people clearly realize that technology plays a crucial role in our life’s from now on and for the future. During these difficult times, we got to know ourselves more; we struggled with our weaknesses and tried to continue our lives as we had before the pandemic.

All this would not be possible without technology - the telecom industry in particular. Because how could we contact each other when we were locked up in our homes? How could we organize meetings at work if we didn’t go to work? How could we conduct videoconferencing if our internet connections were not adapted to the transmission of such a large amount of data?

Technology and telecommunications companies played a vital role during the pandemic to ensure that we are all set and always connected.

Both technology and telco companies showed their human face, came to our aid so that we could function, and our everyday life would have its continuity.

Telecom Industry Evolution

Before the emergence of the Internet (around the year 2000), we had to deal with the era of providing information, not necessarily in digital form. After this phase (around 2006), the telco industry realigned itself around horizontal solutions to focus on cloud solutions where a new world of opportunities has opened up.

the industry evolution with new telecommunication standards
The industry evolution with new telecommunication standards

Now, this era is unstoppable, and there is no escape, but in addition to opportunities, the telco industry faces many challenges. New technologies like the Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), microservices and more, are leading to exponential new data generation.

The Current State of Telecommunications Business

Today, the telecommunications industry is going through a transformational phase of development – to acclimate itself per the new technological and cloud trends. Improving their digital customer experience, networks, IT, and most importantly, their way of working, including culture and skill set, but at the same time, they have to keep their existing systems running to prevent the looming crash.

There are many named challenges in the telco business, which on the one hand, are challenges and, on the other hand, opportunities. These include the Internet of Things (IoT) impact, leading to explosive growth in connected devices. This growth generates billions and trillions of new data sources to be analyzed and pushes for data analytics mechanisms. It is expected that this growth will push the data to be handled by networks to zettabytes per year.

The aforementioned phase of transformation in telco businesses impacts global digitization and enables the development of civilization for the next generations.

Digital Transformation AKA Data-Driven Transformation

Digital transformation is emerging as a key driver of sweeping change in the world around us. It can significantly improve consumer lives and create broader societal good while providing businesses with new opportunities for value creation and capture. The next decade of digitization will look markedly different from the past, and companies across the industry will need to be well prepared to take advantage of the sweeping transformation in consumer lives, enterprises, and the broader economy.

The telecommunications industry, including vendors and communications service providers(CSPs), are at the forefront of this transformation, both as an industry witnessing a large-scale change in its market environment and as a key driver of worldwide digitization.

The Importance of Data Analytics

Digitization and telecommunications are strongly connected and have the power to disrupt traditional business models. The growing demand for connectivity is pressuring companies to upgrade their telecommunications infrastructure.

Network transformation is imperative for innovative businesses, allowing them to address changing customer expectations. But achieving customer satisfaction won’t come easily. Therefore, in addition to the known and used problem-solving methods, telecommunications companies have used intelligent methods, directly resulting from the vast real-time and historical data pool.

Telecommunication companies are now using Artificial Intelligence (AI) and Machine Learning (ML) to improve customer service. With these novel technologies relied on data science, big data analytics, they can identify leads, analyze customer data and develop better products.

Some companies, for instance, use advanced AI algorithms to help customers navigate through TV channels. In contrast, others have cutting-edge IoT infrastructures that monitor data centers remotely and detect security breaches.

Properly processed data can also be used to analyze anomaly detection in the network, while live collected data - ensuring visibility of the operation and optimization of live network performance and helping to build predictions and scenarios for the future.

Data Science Role in the Telecommunications

Almost half of the telecom companies are primarily working on utilizing current infrastructure, and most of them(over 60%) are investing in AI-driven systems and platforms.

The key driver for AI growth in the telco industry is an increasing demand for autonomously driven network solutions. The networks of the telecommunications industry expand at a rapid pace, becoming more complex and difficult to manage. By using AI-powered network solutions, CSPs can reduce network congestion and improve network quality, enhancing the customer experience.

Data science applications and benefits for telecom
Data science applications and benefits for telecom

Being an umbrella term, AI is divided into different technology segments, such as Machine Learning(ML), Deep Learning( DL), Natural Language Processing(NLP), and many more. However, a central role in the telecommunications industry belongs to the three mentioned technologies.

Telecom Data

As demand for more services grows, telecom companies need to deal with more data streams called Big Data. Big Data is a combination of structured and unstructured data that needs advanced statistical and instructional methods. Big Data combines information from diverse sources to create knowledge, make better predictions, and tailor services.

The potential of Big Data for telco providers, however, poses a challenge: how can a companies store, utilize data to increase revenues and profits across the value chain, spanning network operations, product development, marketing, sales, and customer service?

Currently, Data Management across telecommunication companies and vertical businesses is continuously preparing appropriate structures, algorithms, and proper processing of collected data.

Big Data Analytics

According to many reports published recently, mobile data traffic grew few years in a row, and the last decade has witnessed a never-ending growth in the global mobile data traffic.

This year, data volume compared to the entire global data produced in 2005 increased already 23 times. The International Telecommunication Union (ITU) predicted that the trend of exponential growth would continue, and by 2030, the overall mobile data traffic will reach astonishingly 5 zettabytes (ZB) per month. Thus, for telecom companies, the fundamental challenges of Big Data applications will be exploring large volumes of data and extracting useful information or knowledge for future decision making.

AI Applications in Telco Industry

Big Data analytics has begun supporting a wide range of potential telco applications and use cases. To name a few:

Robotic Process Automation

Robotic Process Automation (RPA) uses AI for business process automation. RPA can bring excellent efficiency to telecommunications by allowing telecoms to manage their operations more effectively and the massive amounts of repetitive and rules-based methods.

By streamlining the execution of once tricky, labor-intensive, and time-taking procedures such as billing, data entry, employee management, and order satisfaction, RPA disengages back-office staff for a higher value-add operation.

Virtual Assistants

Telecoms have already shifted to virtual assistants to help with the significant number of installation, troubleshooting, and maintenance applications. Instead of human resources involved, using AI, telecoms can perform self-service capabilities that instruct consumers to install and administer their own devices.

Automatic Service Tickets Resolution

Handling customer complaint tickets in real-time is both an inevitable and resource-heavy undertaking for any CSP. Employing an ML-based Automatic Service Tickets Resolution platform is paramount to improving complaints resolution efficiency. Specialized algorithms learn and detect patterns across the information gathered from complaints about time and automatically act upon them. On the top, such a platform minimizes human interaction and time wasted by engineering and operations teams handling repetitive tasks. It improves response times to customers while moving the organization to a service-centric approach.

Sentiment Analysis for Customer Service

Sentiment analysis tools are a natural extension of customer service solutions in telecom. They offer telecom companies a way to automatically analyze customer sentiment and get insights into their attitudes towards products, services, or company offerings. Sentiment analysis is just one of the use cases for natural language processing for telecom data. NLP has multiple applications in the telecommunication industry as the aforementioned chatbots, virtual call centers, or text processing models. For more info on this machine learning technology head to our definitive guide to NLP.

Predictive Preservation

Predictive Preservation is widely known as part of the Network and Service Operations domain. One of the automation use cases in this domain concerns Predictive Network Maintenance.

Automating the maintenance process and moving from reactive practices to the predictive approach can avoid serious networking issues that can be hard to track down in complex networks.

This approach’s key benefits for telecom companies are obvious - minimizing malfunctioning risk and operations consistency assurance while maximizing customer experience.

Data Management

The year 2020 has brought many challenges, not least of which is the need to stay connected. The recent reliance on digital services to keep families, friends, and businesses in touch is an added pressure and opportunity for the telco industry who are already taking on the challenges presented by the aforementioned IoT, and what is more vital for industry, the rollout of 5G Network.

Along with these new challenges and opportunities comes a time for the telco companies to ensure one thing moves up in the list of priorities - Data Management (DM). DM is an effective process that provides analytical information and helps drive operational decision-making and strategic planning by corporate executives, business managers, and other users.

Data is increasingly seen as an asset that can make more informed business decisions, improve marketing campaigns, optimize business operations, and reduce costs, all to increase revenue and profits. But a lack of proper DM can saddle any company with incompatible data silos, inconsistent data sets, and data quality problems that limit their ability to run Business Intelligence (BI) and analytics applications or, worse, lead to faulty findings.

Data management processes in the telecommunications industry
Data management processes in the telecommunications industry

ML/AI as an Innovation Enabler

While in today’s business environment, the analytics capacities of the telecom industry are highly dependent on the workforce, research says that in 2030 analytics will be highly automated, driven by cheap computing power and advanced algorithms. This is made possible by the aforementioned technologies: Artificial Intelligence (AI), Machine Learning ( ML), Cognitive Analytics (CA), Robotics Process Automation (RPA), and Bots that can take over tasks that today require human intelligence.

Sentiment analysis prompts that the advancing automation, thanks to the mentioned technologies, will play an innovation enabler. Thanks to its capabilities, it will even force telecoms and other enterprises to progress towards virtualization and cloudification. It will enable the emergence of innovative solutions that are not known today and thanks to which humanity will fully enjoy its benefits.

Key Opportunities for Growth

In the coming year, however, telecommunications companies should play an even larger role as 5G wireless technology begins to gain traction among enterprises and consumers alike. In particular, 5G promises to provide enterprises with unprecedented, real-time visibility, insights, and control over their assets, products, and services. It can also provide new opportunities to radically transform how they operate and deliver new products and services.

AI-based services in the customer-first approach to telecoms
AI-based services in the customer-first approach to telecoms

Because 5G will likely trigger innovative business models that gain large-scale adoption, telecom providers are on the way to help enterprise customers gain first-mover advantages in defining and developing the innovative business models that can disrupt their industries.

This will likely involve exploring and developing new operating and business models that require collaboration with third-party partners to deliver end-to-end enterprise applications that can meet the disparate needs of specific industries.

Closing Remarks

Telecommunication companies have emerged as one of the essential industries due to the pandemic and will most likely stay that way for the foreseeable future. Bringing people together, enabling professional and social communications, allowing businesses to continue delivering products and services by migrating to the cloud, and showing admirable resilience amid the COVID crisis are among telco’s highlights for the past year and expected them to build on from this foundation.

This year, the spotlight is on 5G deployments and the massive boost in connectivity, speed, and business transformation it brings along. But projects on 6G are already underway.

6G projects landscape with real time data processing
6G projects landscape with real time data processing

Future 6G data-driven telco networks will be empowered by Artificial Intelligence (AI) at almost all levels, from network orchestration and management to coding and signal processing in the physical layer, manipulation of smart structures, and data mining at the network and device-level for service-based context-aware communications, etc.

AI-embedded technologies will be useful in the next telco generations, i.e., 6G and others xG’s. Implementation of AI by telco companies will result in the development of highly personalized products, improved fulfillment processes, and enhanced network management, allowing telecommunications operators to provide their customers with more attractive services and improve their customer retention.

About the author

Konrad Fulawka

Strategic Advisor and Telco Expert

Konrad Fulawka graduated from the University of Technology in Wroclaw and has almost 20 years of experience in the Telecommunications Industry.
For the last 11 years, he works for Nokia. Over the time, Konrad was responsible for leading international and multicultural teams working on many complex telecommunication projects, delivering high-quality software worldwide. During the last few years, he is heading the Nokia Garage - Innovation Hub, which helps Nokia drive cutting-edge innovative projects.
At nexocode, Konrad acts as a strategic advisor and Telco Expert with unparalleled insight into global business trends and best practices across all verticals. He loves DIY (Do It Yourself) activities besides Political Economy and Financial Services Markets.

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