When Too Much Data Becomes Too Big an AI Problem

As AIOps’ mission grows more vital to enterprises and service providers, so too does the demand for enormous volumes of data. However, with that avalanche of data comes a rising cost, a fact that can’t be ignored. In addition, operations teams frequently find themselves drowning in information, yet starving for insights.

Buried beneath the cost conversation is an urgent question that organizations need to be asking: Are you even looking at that data through the right lens? Specifically, should that data be examined from both a network and security perspective? Without that dual perspective, data silos, blind spots, and noise may be undermining AIOps investments.

Ensuring the quality of observability data will be key to AIOps success.

The Observability Paradox: More Data Often Means Less Clarity

The importance of observability can’t be understated. According to Mordor Intelligence’s 2026 AIOps Market Report, “Microservices generate tenfold more telemetry than monolithic stacks, and enterprise adoption of AI-powered monitoring grew from 42% to 54% between 2024 and 2025 as traditional rule-based alerts could not cope.”

That said, observability means different things depending on where you sit. Application teams approach it from the top down, tracking logs, metrics, and traces through the layers of a software stack. Network teams approach it from the bottom up, observing how traffic actually moves, how services are delivered, and what users experience in real time. Both viewpoints are valid. Neither is complete or sufficient on its own.

The real problem is that these perspectives have lived in separate silos for years. Metrics in one place, logs in another, traces in a third, with each owned by a different team, each telling an incomplete story. The simple fact of the matter is that you can’t divorce services, applications, and network from one another or examine them in isolation anymore. And security can’t be left out of the conversation.

Ultimately, when organizations try to feed siloed, disconnected data into an AI system, they are not giving AI a foundation for intelligent decision-making. They are giving it a puzzle with pieces missing.

Better Data Trumps More Data

We’ve all been taught that whenever something fails, the more information we can gather about the reasons behind the failure, the better off we’ll be in trying to solve the problem. However, more data is not always the answer. Observability platform costs can, and frequently do, exceed the cost of the infrastructure being observed. Add the token consumption demands of modern AI and large language model (LLM)-based systems, and the economics quickly become unsustainable.

If the data being fed to an AI platform is raw, uncontextualized, or sampled at the wrong moment, the AI cannot do its job. The output may lack accuracy. It may miss signals. It may send teams chasing false positives instead of real problems. The result is the exact opposite of what AIOps promised: more time spent, more money spent, and less confidence in the outcomes.

What is needed instead is better data. This means AI-ready data. To support AIOps, teams need high-fidelity, contextualized telemetry versus sampled data dumps. In this way, they can break down silos, thus integrating network and application insights for complete visibility.

What Makes Data AI-Ready

What makes data truly AI-ready comes down to a few key qualities.

• The data must be observed, not inferred.
• It must be continuous, not sampled.
• It must be contextualized, which means it is tagged to the application, service, or user experience it describes, so that AI can make meaningful connections rather than pattern-matching against raw noise.
• And, critically, it must capture what is happening at the application layer and across the network and security environment simultaneously.

This is where the network and security lens becomes essential. A performance issue can originate anywhere, such as in the application code, in the network, in the storage layer, or in a security event unfolding in parallel. Without a unified, high-fidelity view that spans all of these domains, AI is working blind. It can tell you something is wrong, but it can’t reliably tell you why.

Context Without the Cost of Noise

High-performing AIOps teams recognize that the key to success goes beyond maximizing data volume. What is needed is holistic observability that delivers curated, credible, and continuous intelligence that AI can actually trust and therefore act on with precision. This means achieving data reduction without loss of fidelity, and adding context without the cost of noise.

The high level of observability that fuels decision-ready intelligence is what NETSCOUT makes possible. The NETSCOUT Data Platform delivers visibility into every interaction, transaction, and experience driving business in real time, transforming comprehensive network activity into NETSCOUT Smart Data. This Smart Data is intelligence spanning observability, cybersecurity, and AIOps, ensuring no dimension of an environment becomes a blind spot.

The NETSCOUT solution provides a single source of truth, consumable everywhere AI needs it, and the data foundation that enables AI to deliver on its promise.

Recently, Omdia analysts Jim Frey and Torsten Volk, alongside NETSCOUT’s Heather Broughton, discussed the observability paradox and why more data often results in less clarity, and what AI really needs to provide meaningful, actionable insights. Listen to their conversation to learn more.

Listen to Omdia analysts Jim Frey and Torsten Volk’s conversation with NETSCOUT’s Heather Broughton to learn more about the observability paradox.