Big data in today’s world is one of a business’s most powerful assets, and this is definitely the case for Telcos. Big data, specifically big data analytics, offers a Telco the ability to improve quality of service (QoS), which resultantly improves user experience, reduces churn and enhances overall profitability. With the global adoption of 5G, big data analytics are set to play an even more significant role in data-driven network optimisation in the future. We unpack this subject in more detail below.
Understanding Big Data-Driven Mobile Network Optimisation
Defining what this term means is essential in understanding the significance of big data-driven mobile network optimisation. Big data-driven mobile network optimisation is a process that utilises the power of big data analytics to enhance the performance, efficiency, and quality of mobile networks.
The current 3G, 4G, and 5G mobile networks are complex and complicated systems with billions of devices connected worldwide. How many devices? According to Statista, the number of mobile devices operating worldwide stands at an estimated 15 billion and is expected to reach 18.22 billion by 2025. The number of smartphone mobile network subscriptions worldwide also increased and reached nearly 6.4 billion in 2022 and is forecast to exceed 7.7 billion by 2028. Optimising the mobile networks available to these devices is critical to providing reliable, high-quality mobile services to users.
The question remains: how do Telcos optimise these networks? The answer is in big data-driven mobile network optimisation, which includes the following processes:
- Data Collection – Telcos collect vast amounts of data from various sources within their network infrastructure. This data includes everything from information about network traffic, signal strength, device location, call quality, data usage, and more.
- Data Processing – Once the data is collected, it is processed and stored in a centralised data lake or warehouse. In this data suppository, the data is cleaned, aggregated, and structured for analysis.
- Data Analysis – Big data analytics tools and techniques are applied to the collected data to gain insights into network performance and user behaviour. This analysis is used to identify network bottlenecks, congestion areas, device usage patterns, and other factors affecting network performance.
- Network Optimisation – Based on the insights gained from data analysis, Telcos can make informed decisions to optimise their network infrastructure. This can include adjusting the allocation of network resources, fine-tuning network parameters, adding or upgrading equipment, and implementing load-balancing strategies.
- Predictive Maintenance – Big data analytics can also be used for predictive maintenance of network equipment. By analysing historical data, operators can predict when network components are likely to fail and proactively replace or repair them to minimise downtime.
- Quality of Service (QoS) Improvement – Network optimisation can improve QoS metrics such as latency, data throughput, call drop rates, and coverage. With this insight, Telcos can improve customers’ mobile usage experience.
- Capacity Planning – Big data-driven insights can aid in long-term network planning by helping Telcos understand future demands and trends. This information can guide decisions on expanding network capacity to meet growing user needs.
One of the main goals of big data-driven mobile network optimisation is to enhance the overall user experience. By optimising network performance and reducing issues such as dropped calls and slow data speeds, Telcos can increase customer satisfaction and retention. This, therefore, improves profitability and increases revenue.
To implement big data-driven mobile network optimisation effectively, Telcos are investing in advanced analytics solutions that utilise machine learning, data infrastructure and artificial intelligence to pull accurate and relevant data, which is interpreted to deliver valuable and insightful insights. These insights then enable Telcos to make data-driven decisions around network improvements. Key to this is the ability to pull data from numerous data sources, which we examine in more detail below.
Big Data Sources For Data-Driven Optimisation
In the Telecommunications industry, there are various types of big data sources. Data is collected from user equipment (smartphones and other devices) and mobile networks. This data provides complete visibility across the network and its users and is valuable for network optimisation, user experience improvement, and business intelligence decision-making. Here are some of the key types of big data collected in Telecommunications:
- Call Detail Records (CDRs) – CDRs contain information about voice calls, text messages, and data sessions. They include details such as the calling and receiving parties, call duration, timestamps, and location information. Analysing CDRs can provide insights into network traffic patterns and user behaviour.
- Location Data – Mobile devices constantly report their location to the network. This data can track users’ movement, analyse foot traffic in specific areas, and optimise network coverage.
- Network Performance Data – Includes data on signal strength, latency, packet loss, and other performance metrics. Monitoring network performance helps identify areas with network congestion or poor connectivity.
- User Behaviour and Usage Data – Mobile apps and services collect data on how users interact with them. Among these things are information on which apps are used, how often, and for how long. This data can be used for targeted advertising and improving user experiences.
- Billing and Payment Data – Telcos collect data related to billing, payment history, and usage patterns. Analysing this data helps in revenue assurance and fraud detection.
- Subscriber Profile Data – This includes demographic information about subscribers, such as their age, gender, and location. It can be used for marketing purposes and customer segmentation.
- Network Equipment Data – Data from network infrastructure components, such as routers, switches, and base stations, is crucial for monitoring and maintaining the network. It includes hardware health, traffic load, and error rates.
- Customer Support and Interaction Data – Comprises of data from customer support interactions, such as call centre records and chat transcripts. It can be used to improve customer service and resolve issues more efficiently.
- Security and Threat Data – Data related to network security threats and vulnerabilities, including intrusion detection logs and cybersecurity incident reports, is crucial for protecting the network and user data.
- IoT Device Data – With the growth of the Internet of Things (IoT), Telcos can collect data from various IoT devices, including sensors and smart appliances. This data can be used for IoT network management and service optimisation.
- Quality of Service (QoS) Data – QoS data includes information about the quality of services provided to users, such as call quality, data speed, and video streaming performance.
Through analysing these types of big data sources, Telcos can optimise their networks to provide better services for customers and make informed business decisions related to all aspects of the business, from operations to product offerings and services, as well as customer services.
Data Collection and Analytics Techniques for Network Optimisation
From the above, it is clear that big data analysis relies on several big data sources, but it also depends on various techniques for collecting and processing big data. This includes both active and passive data collection.
Passive data is collected without the customer’s involvement, while active data refers to information gathered with the customer’s consent. Examples of active mobile data collection include surveys filled in by customers, customer support transcriptions, or social media inquiries. Passive data collection often refers to the data insights gained and generated from Telco-related data sources such as CDRs and other network event data.
Once the data is collected through passive and active methods, several different data analytics techniques can be used to provide a Telco with the network optimisation insights required. These include the following:
- Descriptive analytics – Descriptive analytics helps Telcos understand the behavioural patterns of customers. Telcos can use these analytics to gain insights into their subscribers’ usage patterns and behaviours to improve products, services, network performance, etc. Â
- Diagnostic analytics – Diagnostic analytics address why something happened by analysing data. It also helps to identify trends and explain customer behaviour. Using diagnostic analytics, Telcos can make future decisions based on hard evidence and improve business performance and profitability.
- Predictive analytics – Predictive analytics is an analytics tool that utilises algorithms, data mining, machine learning, artificial intelligence, and business intelligence (BI) technology to convert big data into actionable insights. This process analyses current and historical data to provide insight into creating new business applications, services, and product offerings, identify recent trends or risks, detect factors affecting productivity and profitability and more.
- Prescriptive analytics – Prescriptive analytics provides a Telco with the calculated steps to be taken next. This technology utilises applied statistics, deep learning, computer vision, and other advanced methods.
From the above, it is clear that big data and the tools used to extract big data are becoming a superpower for Telcos by providing insights into all elements of the business to drive profitability and efficiency. As technology advances, there are more opportunities for big data-driven network optimisation, especially concerning network performance. One of these opportunities is the adoption of 5G and this next-generation network’s capabilities. Let’s expand on this below.
Integration of 5G Mobile Networks and Big Data Analytics
5G technology has the potential to significantly improve and amplify data collection and real-time analytic capabilities, which will revolutionise how Telcos utilise advanced analytics for network optimisation. This is made possible by the following components of 5G:
- Higher Data Speeds – 5G offers dramatically faster data speeds than 4G and 3G networks. This means more data can be transmitted in less time, enabling real-time data collection from various sources, including sensors, IoT devices, and mobile apps.
- Lower Latency – One of the key advantages of 5G is its ultra-low latency. This low latency allows for almost instantaneous data transmission, making it ideal for applications that require real-time analytics, such as network optimisation.
- Increased Device Density – 5G networks are designed to support significantly more connected devices than previous generations. This enables simultaneous data collection from many devices and sensors in a given area. This provides Telcos with a significant amount of data available for analysis.
- Edge Computing – 5G networks can work with edge computing, allowing data processing to occur closer to the data source. This reduces the need to transmit large amounts of raw data over long distances, reducing latency and enabling faster real-time analytics.
- Enhanced Security – With the increased speed and capacity of 5G networks, there is a potential for more advanced security measures. This can make real-time analytics more secure, especially when dealing with sensitive Telco-related data.
- Data Monetisation – Telcos can monetise the real-time data generated by 5G networks. They can analyse this data to gain insights into customer behaviour, preferences, and trends, which can assist Telcos in creating more personalised services for customers, developing targeted marketing strategies and product development.
From the above, it is clear that the 5G capabilities can improve data collection and real-time analytics by providing faster, more reliable and scalable connectivity. This will allow Telcos to act faster, make more proficient changes, monitor the network and optimise it more effectively. The knock-on effect of this will be that customer experience will improve, which will reduce customer churn and increase profitability.
Conclusion
From the above, one thing is clear: big data analytics is no longer a nice to have but a necessity for Telcos, especially where network optimisation is concerned. With these big data analytics, Telcos can improve quality of service (QoS), user experience, network performance, operational efficiency and more. This space is about to get even more exciting with the adoption of 5G and the capabilities that this next-generation network offers. This will revolutionise data collection and real-time analytics as we know it. With big data analytics reshaping the Telecommunications industry, this is an exciting space to watch.
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I’m the Advanced Analytics Solution Stream Owner at Adapt IT Telecoms. With my years of experience in the ICT industry, I have gained skills in various areas including Sales, Strategy, Professional Services, Management, Cloud, and Digital Transformation. My team and I specialise in providing niche large data solutions to markets such as mobile network, education, and regulatory. As the Solution Stream Owner, I’m responsible for ensuring that we deliver top-notch services and innovative solutions to our clients. My journey in this field started as a technical engineer and over the years, I have moved into pre-sales, business development, consulting, analytics, commercial, and operations management. This has given me a diverse set of skills, which allows me to have conversations that lead to innovative solutions. What I’m most passionate about is the integration between software and people, both within society and organisations. It’s a challenge, but one that I find extremely rewarding. I’m committed to ensuring that we continue to provide cutting-edge solutions that help our clients stay ahead of the competition.
