Turning a Flood of 5G Data into Rocket Fuel for AIOps

With the growth in 5G standalone networks has come a data explosion unlike anything in previous generations. For example, a network with 10 million subscribers can be expected to generate roughly 9 petabytes of data every day. On its own, this deluge is a liability: expensive to move, store, and process. But buried inside is an enormous strategic asset—the raw material needed to power the next generation of artificial intelligence for operations (AIOps), network automation, and ultimately, autonomous networks. The challenge is not collecting the data; it is transforming it into something AI can actually use. When done correctly, this data becomes rocket fuel for AIOps success.

Today’s modern network operators are focused on harnessing AI to manage the immense complexity of 5G standalone. However, the traditional approach, which relies on alarm data, is not sufficient to drive the next-generation AIOps environments. Alarms tend to be event driven and lack the contextual depth required to pinpoint the root cause of an issue, especially when network functions are heavily loaded. All too often, alarms are not even generated, leaving subscriber experience at risk.

By contrast, granular, real-time packet-level data provides full visibility into every transaction across the network. However, raw packet data presents a paradox. It is rich with insight but fundamentally unsuited for direct consumption by AI models. Feeding unprocessed records into an AIOps engine overwhelms it with an enormous volume of largely irrelevant data points. In many cases, this results in vast compute resources being wasted to conduct basic cleansing and preparation instead of higher-value analytics and decision-making.

The Need for a Disciplined Data Curation Strategy

To unlock the real value, operators need a disciplined data curation strategy that systematically refines raw feeds into structured, AI-ready intelligence. Data curation bridges the gap between network packets and AIOps. It normalizes, enriches, filters, and labels data so that it becomes consumable and valuable to AI engines.

Curated data has a lot of benefits for operators, not the least of which is helping to lower direct operating costs, while slashing GPU utilization, memory needs, and processing time. This results in faster inference, shorter model training cycles, and the ability to deploy and iterate on more use cases without being constrained by infrastructure costs.

Curated data streams have proven highly effective at enabling operators to support sophisticated applications such as anomaly detection, predictive maintenance, capacity planning, and customer experience analytics at scale. But achieving these results requires more than after-the-fact processing.

An effective 5G data curation strategy must be embedded at the source via intelligent pipeline architecture. In this model, packet data from diverse environments, such as 4G, 5G, Internet of Things (IoT), edge, cable, fixed networks, and private wireless, is captured by sensors that perform initial processing. This “Smart Data” is then fed into a dedicated curation engine, where advanced feature extraction, aggregation, labeling, and triggering occur before the information ever reaches an analytics or AIOps platform. In this way, raw traffic is turned into context-rich intelligence that AIOps platforms can immediately act upon. A powerful concept within this framework is the use of “playbooks” to define targeted data feeds. Playbooks can be customized, extended, and even used to create new metrics by modifying existing formulas and trigger conditions. This gives operators fine-grained control over what data is exported to external AIOps engines and under what circumstances.

The result is a dramatic reduction in data. A single curated feed can be as small as one one-hundredth the size of the original raw input. Even running 10 distinct feeds for different use cases may represent only one-tenth of the initial footprint.

On the Path to Monetization

Curated outputs can be invaluable for a number of purposes. Whether for traditional analytics platforms, monetization applications, or next-generation architectures that support use cases such as 5G slicing assurance, this intelligent data can be a game-changer.

Smart strategies that optimize sensor architectures can deliver significant performance improvements over traditional approaches when full session tracing is not needed, further reducing the barrier to adding user-plane monitoring. Such strategies can dramatically lower the cost of comprehensive visibility.

The Importance of Observability

In the end, visibility alone is not enough. What operators truly need is observability: the ability to understand not just what is happening in the network, but why. High-quality curated data is the foundation for trustworthy AI. Without it, AIOps systems are prone to noise and hallucinations. With it, they gain clarity, speed, and confidence. By embedding data curation at the source, correlating user and control planes, and delivering tightly scoped curated feeds, operators can transform overwhelming 5G data streams into high-octane fuel for automation, closed-loop control, and the long-term vision of autonomous networks.

NETSCOUT's Omnis AI Sensor collects and processes data in real time, ensuring relevance and accuracy. This powerful solution leverages scalable deep-packet inspection (DPI) to extract, enrich, and provide granular data from the network traffic, enabling deeper observability. Ultimately, this ecosystem minimizes noise and maximizes clarity, empowering AIOps to make smarter and faster decisions.

Learn more about how to turn 5G data into rocket fuel for AIOps.