Solving the Data Challenge of Telecom AIOps

Artificial intelligence (AI) is set to transform almost every industry on Earth. Its effect can be even more profound on a critical, diverse, and complex industry such as telecom. In fact, it can improve efficiency and performance as well as reduce cost in almost every aspect of the telecom business, from customer management to network operations to wireless technology itself. AI is not new to telecom; it has already been used for Self Organizing Networks (SON). However, the current market emphasis on AI, especially Generative AI (GenAI), will take its role to a new level. It is even more important for wireless operators when revenues are flattening, and the return on their significant investments in 5G is not yet realized.

AIOps run on data. The telecom workload is unique; and AI models need valid data to work, be it training or inferencing. But acquiring and curating that data is a major challenge, as there is too much data, not in the right format, and the tools often don’t capture an end-to-end view and customer experience. That’s why a comprehensive data management approach is absolutely necessary to acquire relevant, sanitized, and verified data and curate it to AIOps in an ingestible format.

In this whitepaper from RCR Wireless and NETSCOUT, learn about the data challenge for communication service providers (CSPs) in AIOps including:

• The Role of AI in Telecom Networks
• Optimizing Network Operations with AI
• Challenges in Acquiring and Processing Network Data
• Solving the AI Data Challenge
• Data Curation for AI Models

NETSCOUT offers solutions that enable cellular operators to employ a comprehensive approach and solve this data challenge of AIOps. Forward-leaning operators are already utilizing these solutions for real-life use cases. A couple of such use cases have been discussed in this whitepaper, and there is a huge scope for many more.

Get the whitepaper: