Three Data Analytics Trends in Telecom for 2024

Millions of subscribers generate petabytes of data daily—calls, messages, internet traffic, online purchases, and geo-tags. Processing this vast amount of information and extracting valuable business insights is challenging. As a result, the telecom industry is continually exploring new approaches and tools. Vladimir Borkovsky, Product Manager at EW DataFlow, shares his insights on the emerging data analytics trends for telecom operators.

Data Democratization

Data democratization means that access to data is granted not only to data scientists and data engineers but also to employees from other departments, such as security, marketing, and sales.

Why It Matters

Data professionals are often tasked not only with analytics and ML model building but also with maintaining the data environment. This work consumes time that data scientists and engineers could otherwise use to solve business challenges in other departments. Delegating infrastructure maintenance to other specialists is challenging due to the high entry barrier for managing Hadoop clusters and the fragmentation of its tools.

The solution lies in using data platforms that provide low-code tools, pre-configured processes, and a role-based access system. For example, our platform, EW DataFlow, offers a unified and intuitive interface for assigning access rights, launching data processing tasks, allocating computing resources, and monitoring execution failures. This enables data professionals to focus more on solving business problems and working directly with the data rather than managing its infrastructure.

How Operators Can Benefit

Broader access to data accelerates the work of the data department and helps identify and optimize inefficient processes across various departments. For instance, the support team can pinpoint frequent customer issues and develop methods for resolving them quickly, improving customer satisfaction and reducing costs.

Implementing MLOps

MLOps (Machine Learning Operations) integrates model development and production processes, enabling faster deployment, testing, and retraining.

Why It Matters

Without automation in machine learning, telecom operators encounter several challenges. For instance, the development and deployment of models can take weeks or even months, slowing down the response to subscriber behavior and market changes. Without automated pipelines, repetitive tasks must be done manually each time, reducing efficiency and a higher risk of errors.

MLOps helps solve these problems. Various open-source tools, such as MLflow and Kubeflow, are available for implementing this approach. However, these tools require specialized expertise and can be difficult to integrate into an operator's infrastructure. At EW DataFlow, we aim to provide users with greater convenience and efficiency when working with data, so we're also enhancing our MLOps capabilities.

How Operators Can Benefit

Implementing MLOps allows telecom operators to automate and optimize the development, testing, and deployment of ML models. On a broader scale, MLOps enables operators to introduce innovations more quickly, helping them stay competitive in the market.

Real-Time Data Processing

Leveraging real-time data creates new opportunities for telecom operators to enhance service quality and improve marketing communications.

Why It Matters

Historically, batch data processing sufficed for operators. This method optimizes the use of computing resources and provides high throughput. Many have built their data processing infrastructure using tools like Hadoop MapReduce, Apache Hive, and Apache Spark. However, the drawback of this approach is that it doesn't allow for real-time insights.

The focus is shifting toward real-time data processing, utilizing tools like Apache Kafka and Apache Flink. While these new tools require the development of infrastructure and team skills, they offer operators the ability to deliver more personalized services based on current data, helping retain customers. For example, we assist our partners in meeting market challenges and regularly provide training for client teams in big data and MarTech.

How Operators Can Benefit

Unlike batch processing, the real-time approach enables operators to instantly respond to subscriber actions and network events, significantly improving customer interactions. For instance, if a subscriber is near one of the operator's branches, such as a store in a shopping mall, the system can determine their location in real-time and send a personalized offer. This could include information about ongoing promotions on smartphones available at that specific branch, such as discounts on certain models, the possibility of trading in an old device for a new one, or favorable installment plans.

Conclusion

In 2024, telecom operators should closely monitor these emerging trends in big data analytics.

• Data democratization enables access to data across various departments, accelerating and optimizing business processes.
• MLOps allows for automating tasks related to machine learning models, leading to more efficient resource utilization.
• With real-time data processing, an operator can instantly respond to subscriber activities and network events, outpacing the competition.

These trends enable operators to adapt to market changes and enhance customer satisfaction swiftly.

Which of these trends are you already implementing in your company? Share your thoughts in the comments!