Telecom operators face challenges in leveraging network data to optimize network operations. With the increasing complexity of 5G networks, operators must navigate new services, more devices, and evolving networks. This complexity leads to an abundance of data, making it difficult to find relevant information. Lack of standardization and inefficient data management further complicate the process.
Telecom operators are realizing that manual management of networks is no longer feasible. This is where AI technology comes in, automating processes and reducing complexity. However, operators must also improve their handling of network data. There is a need for consistent strategies for data persistence and exposure, as well as open standards to facilitate access to multi-vendor networks.
Operators use network data for various purposes, but the top three are customer experience management, security/fraud, and revenue assurance, and cloud & IT operations. AI and machine learning are playing a role in optimizing and automating networks, but the industry is still in its early days. Some areas where AI is showing promise include network evolution, deployment, optimization, healing, and providing a foundation for automation.
The potential of AI and ML in telecom networks is exciting, and there are real-world examples of its practical use. For example, AI can enhance network planning, improve performance, and optimize customer experience. It can also handle network deployment and resolution of incidents, ensuring high availability.
In conclusion, the telecom industry is navigating the challenges of leveraging network data to optimize operations. AI and ML technology offer potential solutions, but operators must improve their data handling and adopt open standards. The future of telecom networks lies in AI-native networks that fully systemize network operations.
What do you think about the role of AI and ML in telecom networks? How do you see it shaping the future of the industry? Comment below and let us know your thoughts!
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