The Key for Telco Leaders to Harness Ultra-Connectivity? Integrated Data Analytics

Today is World Telecommunication Day (May 17th), which serves as a reminder of the profound impact digital connectivity and innovation have on our lives. As we navigate through a new era of ultra-connectivity, propelled by lightning-fast speeds and the ultra-low latency of 5G technology, the importance of integrated data analytics cannot be overstated.

The telecommunications landscape is in the midst of a seismic shift, driven by the widespread adoption of 5G technology. Projections indicate that we will reach a staggering 5.9 billion connected devices globally by 2027, underscoring the transformative power of this technology. However, with this exponential growth comes a host of challenges for telecommunication IT teams tasked with optimizing its value.

The role of real-time analytics in 5G

The customer’s experience of using any 5G service or application will be critical to the company’s success. While operators should leverage the data generated by 5G for customer experience, they know that its footprint has increased exponentially since the days of 3G. The data contains vital information that enhances decision-making and operational efficiency, such as cell tower utilization, dropped calls, peak usage, and more. However, since this information is vast, you’ll need newer, more sophisticated analytics solutions to manage and leverage this data effectively.

In other words, it takes a mountain of data to deliver a quality customer experience in the 5G era. Massive data sets are generated by 5G networks, and they must be analyzed in real time to learn how customers use and adapt to the new services.

AIOps for proactive service

One big area that boosts the potential for any operator is AIOps, or Artificial Intelligence (AI) for IT Operations. AIOps encompasses the use of advanced analytics, including machine learning (ML) and other forms of AI, to monitor and manage the performance and reliability of applications and hardware systems, detect anomalous problems, adapt to changes in requirements, handle failures, and to adjust proactively or rapidly with minimal disruption of services.

AIOps tools collect data from multiple IT sources, including metrics, logs, traces, events, and telemetry. These tools then process the data and use machine learning to find useful information, and deliver the findings to IT operations. The output includes IT anomalies, patterns, correlations, and predictions.

Any operator can reap huge benefits from investing in AIOps to improve network reliability and, therefore, customer satisfaction, but successfully implementing this strategy often requires a completely new data management strategy.

Addressing 5G’s technical and operational challenges

The good news is that technologies are keeping up, and these data management strategies are available to everyone. The integration of cloud computing and AI with 5G technology marks a significant trend in the evolution of telecom networks. Some of the most promising innovations include as follows:

• Cloud-native architectures represent a fundamental shift in how telecom networks are built and managed. They leverage cloud computing principles to deliver flexible, scalable solutions tailored to the unique demands of 5G networks. These architectures enable telecom operators to dynamically allocate resources, scale infrastructure on-demand, and deploy services rapidly, thereby addressing the challenges posed by the massive data volumes generated by 5G networks. By embracing cloud-native architectures, operators can achieve greater agility, resilience, and cost-effectiveness, while also ensuring low-latency operations critical for delivering high-quality services to end-users.
• Kubernetes and object storage (AWS S3) is a new infrastructure that comes into play when building elastic storage. Being able to scale up and down allows operators to mitigate costs while handling peak workloads at the same time. Object storage solutions like AWS S3 provide elastic, scalable storage capabilities ideal for handling the vast amounts of data generated by 5G networks, including multimedia content, IoT telemetry, and user-generated data. By leveraging these new infrastructures, operators can achieve elasticity in their network resources, allowing them to scale up or down based on demand, thereby optimizing resource utilization and mitigating costs during peak workloads.
• NWDAF (Network Data Analytics Function) also plays a pivotal role in addressing 5G’s technical and operational challenges by providing advanced analytics capabilities tailored to the requirements of next-generation networks. As a key component of the 5G architecture, NWDAF collects, processes, and analyses network data in real-time, enabling operators to gain deep insights into network performance, user behavior and service quality. NWDAF’s integration into the 5G architecture enhances network management capabilities, enables predictive maintenance, and ensures high-quality service delivery, positioning operators to meet the evolving demands of the 5G era effectively.
• AI and machine learning are increasingly important for predictive maintenance and network management, ensuring high reliability and customer satisfaction. Through AI-powered analytics, operators can forecast network failures, identify performance bottlenecks, and optimize resource allocation in real-time, ensuring high reliability and customer satisfaction. Machine learning algorithms can analyze vast datasets to extract valuable insights, detect anomalies, and automate decision-making processes, thereby enhancing operational efficiency and reducing human intervention in network management tasks.
• Data lakes are new analytical engines that have emerged as powerful tools for addressing the many faces of 5G log data. Data lakes allow telecommunications companies to store vast amounts of structured and unstructured data in a centralized repository. Telecommunications providers can use data lakes to analyze this data more efficiently, improving network management, customer service, and business operations. Features such as in-database machine learning and advanced data management capabilities enable telecom providers to optimize network operations and enhance customer engagements.

Robust data and analytics insights are mission critical

As 5G continues to roll out globally, the telecommunications sector must adapt to the increasing complexity and opportunities of big data. The integration of cloud computing and AI with 5G technology will be crucial for telecom operators to harness the full potential of 5G.

The integration of these technologies not only ensures improved network performance and customer satisfaction but also fosters the emergence of innovative business models and services that were once inconceivable. As investments pour into advancing cloud-native architecture, in-database machine learning, data lakehouse, and other cutting-edge solutions, the evolution of 5G within the telecommunications sector appears increasingly promising.