Network Analytics Market Trends

Key Emerging Trends in the Network Analytics Market

The Impact of Automation and Virtualization on Telecommunication Networks

In recent years, the widespread use of the internet has greatly affected the telecom industry, with over 45% of the global population accessing the internet in 2016. This surge in internet usage has led to the development of various mobile applications, including e-commerce, social media, and online videos. The evolution of telecom networks, such as LTE, WiMax, and HSPA, has played a crucial role in facilitating this growth, ultimately increasing web traffic. In response to these changes, mobile operators are increasingly focusing on automated solutions like AI, machine learning, and data analytics.

Automated solutions offer several benefits, such as enhancing operational efficiency and improving customer experience by understanding customer preferences. For example, recommendations and in-app advertisements on mobile applications are outcomes of automated solutions integrated with network analytics. Notably, telecom operators like Telefónica, Vodafone, Deutsche Telekom, and Telenor have introduced their chatbots and digital assistants to improve customer interactions. The growing adoption of automation and virtualization is expected to drive the network analytics market forward.

The demand for maintaining Quality of Experience (QoE) has risen alongside advancements in wireless infrastructure, including Wi-Fi access, 4G networks, VoLTE, edge computing, and carrier aggregation. These technologies, while enhancing network capabilities, pose challenges for telecom operators in delivering quality services. Users may encounter issues such as low-quality videos and extended buffer times for video streaming, affecting the overall service quality. Therefore, ensuring QoE and Quality of Service (QoS) has become crucial for managing networks, especially with the increasing number of smartphone users and improving network connectivity.

Telecom operators face the challenge of integrating new elements and technologies into their existing network infrastructure to enhance the user experience. With the rise in competition among service providers, end-users are becoming more discerning in selecting networks that offer an improved user experience. In this context, QoE becomes essential for telecom operators to expand their customer base. The adoption of network analytics, including predictive analytics and integration with machine learning and artificial intelligence, is viewed as a value-added feature. This advanced analytics infrastructure enhances data sources and employs customer-centric QoE-based techniques, expected to improve end-to-end network performance in the coming years.