The world’s voracious appetite for data is showing no signs of letting up. According to recent reports, the “volume of data created, captured, copied, and consumed worldwide is projected to rise to 181 zettabytes by 2025.” That’s a lot of data!

The reasons for collecting this data are as varied as the volume of the data itself. But for many enterprise IT professionals, data is the key to understanding how networks are working and uncovering problems when they occur. There has been an evolution in how these professionals have viewed data collection.

Centralizing MELT Data

This evolution began with a “collect everything approach,” which involved centralizing metrics, events, logs, and traces (MELT) into “always-hot” clusters and software-as-a-service (SaaS) data lakes. This strategy was based on the belief that a higher volume of data would lead to deeper insights, faster troubleshooting, and more-effective business decisions.

It was easy to see why this approach was taken. It could consolidate MELT data from complex, distributed systems into a single platform, which would then allow for easier access to information on system performance. However, the data lake “collect everything” approach proved to be problematic. The data that could provide answers to the cause of network problems was often hidden under tons of other information. Finding the exact source of the problems was nearly impossible with so much information having to be correlated and deciphered.

Without proper governance, processing, and analysis tools, a data lake can turn into a “data swamp,” where it is difficult to find useful information. In such a centralized model, it can be challenging to maintain consistent data quality and standards across all sources, which can compromise the accuracy of analysis. “Always-hot” clusters are efficient but, by the same token, can be expensive. Ensuring these clusters scale efficiently to handle the massive, continuous streams of MELT data can be a significant operational hurdle. In addition, integrating data from multiple sources into a single platform can prove to be complex.

The Next Phase: Data Tiering and Pipeline Phase

Driven by the shortcomings of the centralized MELT data approach, many IT teams shifted to a “data tiering and observability pipeline” strategy to manage the growing volume of telemetry data generated by modern, distributed systems. These pipelines would route logs, metrics, and traces to various analytics and monitoring tools, while applying transformations such as enrichment, normalization, and down-sampling to keep data costs and storage under control.

However, this approach also presented problems. IT professionals often voiced concerns that these aggressive filtering practices—especially when driven primarily by cost constraints—unintentionally removed key signals. When data is dropped or sampled too early in the pipeline, the resulting blind spots obscure the causal relationships between components, making it significantly harder to detect, diagnose, and understand incidents. In other words, the very controls put in place to tame complexity limited the ability to see the full story behind system behavior.

Instead of looking for a needle in the haystack, this approach simply collected less hay/data. The net result was that IT ended up with holes in the data that left them with the wrong impression of what was happening. Making matters worse, this approach could leave IT looking foolish because they were not even able to identify the problem.

The Future of Observability

Because traditional MELT streams alone cannot satisfy both cost and completeness, an artificial intelligence (AI)-ready data approach is called for. By complimenting MELT-centric deployments with deep packet inspection (DPI)-enhanced, AI-ready telemetry, IT professionals are able to gain the full picture without gathering too much data. This is akin to filling in the holes of a Swiss cheese.

As enterprises push observability and AIOps deeper into hybrid and distributed environments, they increasingly find that traditional MELT alone cannot deliver both cost efficiency and complete insight. NETSCOUT solves this gap by enhancing MELT with packet-level intelligence through its patented, scalable DPI technology. Instead of relying solely on summary telemetry, NETSCOUT extracts Smart Data directly from live traffic—high-fidelity, protocol-aware metadata that preserves critical signals while dramatically reducing volume. This enables IT teams to see exactly how services communicate, where issues originate, and what users actually experience, without ballooning data costs.

By integrating DPI-derived Smart Data into NETSCOUT solutions, it has become possible to obtain real-time, complete, and efficiently curated data. The result is observability that supports faster root-cause analysis today and provides structured, enriched, AI-ready data for tomorrow—proving that DPI-enhanced MELT is the path to visibility without compromise.

To learn more or speak to an expert, please visit our observability solutions page: Omnis AI Insights | NETSCOUT.