AI is rapidly becoming a strategic priority for communications service providers (CSPs). From autonomous operations and service assurance to customer experience management, AI promises to improve efficiency, accelerate decision-making, and reduce operational costs. But there is a critical obstacle standing in the way of AI success: data quality.

For years, organizations have operated under the assumption that more data leads to better outcomes. They built massive data lakes, collected endless telemetry, and stored every log and event possible. Today, many are applying the same strategy to AI, believing that feeding large language models (LLMs) more data will automatically produce better answers. The reality is far more complex.

Telecom networks generate enormous volumes of operational data across radio, transport, core, cloud, and service domains. While this information is invaluable, forcing AI systems to sift through raw telemetry to determine what happens creates significant challenges:

• Increased operational costs
• Slower response times
• Higher processing requirements
• Greater risk of inaccurate conclusions
• Increased likelihood of hallucinations and false insights

Most operational questions are not probabilistic. They are deterministic. Network teams need definitive answers to questions such as:

• Did a customer session fail?
• Did a call drop?
• Which cell site experienced degradation?
• How many subscribers were impacted?
• What has changed compared with normal behavior?

These answers should be established before AI enters the workflow. AI should reason with facts, within context, and not attempt to discover them.

NETSCOUT Smart Data is high-fidelity curated data that is scalable and supports customized AI playbooks.

Read the Forbes article “Why AI Needs Smarter Data Before It Can Fix Your Network,” by NETSCOUT CTO and Senior Vice President Bruce Kelley.

Learn more about NETSCOUT Smart Data