How Curated Data Unlocks the Full Potential of AIOps

The wrong data can be worse than no data at all. Across sectors, operations teams are dealing with an unprecedented volume of information from applications, networks, clouds, and security systems. For artificial intelligence for IT operations (AIOps) to deliver reliable results, that data must be refined and contextualized.

According to an IDC forecast, roughly 78 percent of stored data is unstructured, meaning it lacks a predefined format or organization. Successful AIOps initiatives rely on accurate, context-rich inputs, while observability requires a consistent, reliable view of system behavior. In both cases, data quality matters as much as quantity, and curating unstructured data is critical to maintaining that quality in dynamic systems.

Curated data is information that has been selectively captured, enriched with context, and organized for immediate use. It combines relevant metrics, logs, and deep system and network intelligence into a unified dataset that is accurate, complete, and aligned with operational objectives. Curated data improves analytics pipeline accuracy by preserving protocol and session context from ingestion to export, clarifying what happened, where, and why, so teams can act with precision.

Understanding Data Readiness in AIOps

Fortune Business Insights predicts the global AIOps market will reach $8.64 billion by 2032, with North America holding the largest share. In such a rapidly growing market, the real value of data comes from how it is applied and how immediately actionable it is.

When applied in AIOps workflows, curated data enables precise event correlation by connecting multiple seemingly unrelated signals to a single root cause. This can include analyzing historical usage patterns to predict future resource needs, optimize allocation, and prevent over-provisioning—but several challenges often stand in the way:

• Signal lost in noise: Critical alerts get buried beneath irrelevant events, reducing the ability to quickly identify, prioritize, and resolve problems, which leads to slower response times and increases the risk of affecting users.
• Missing context: Data without clear relationships between components, service impact, or user behavior limits analysis and delays effective response, making it harder to pinpoint and address underlying issues.
• Scaling complexity: Rising demand, wider reach, and distributed hybrid and multicloud environments require enriched, high-quality datasets for accurate observability and reliable automation.

Curating the “Last Mile” Between Data and Insights

Overcoming these challenges starts with ensuring data provides the fastest route to solving a problem. Replacing raw inputs with curated insights, especially from packet-level analysis, delivers unparalleled detail into network and service behavior. Enriched with protocol-level details, session awareness, and real-time behavioral context, curated data offers a unified view of system health across applications, infrastructure, networks, and remote environments, while giving security teams a stronger foundation for investigating anomalies.

With this level of precision, teams can sift through alerts faster by filtering out low-value, false-positive, or duplicate ones and correlate telemetry across tools and layers to pinpoint underlying issues. These capabilities also support automated remediation via custom-built machine learning (ML) models or prebuilt AI agents from third-party platforms. When escalation is needed, incidents can be routed with full context to the right experts, accelerating resolution and minimizing downtime.

Navigating the Data Plane with Deep Packet Inspection

NETSCOUT’s deep packet inspection (DPI) is a data processing technique that inspects the full contents of each packet, not just the header, to create high-fidelity metadata that identifies protocols, extracts performance metrics, and reveals detailed insights into traffic behavior. Unlike traditional monitoring methods that rely on logs or flow records, DPI enables structured, real-time analysis of data in motion, making it possible to detect and resolve issues quickly.

This enables more accurate security and AIOps workflows by feeding pipelines with actionable intelligence that minimizes unnecessary retention of low-value data and reinforces operational resilience, unlocking the full potential of AIOps.

Download our latest guide, “Solving Problems Faster with the Best Data Available,” to see how DPI generates high-fidelity metadata that strengthens AIOps initiatives and automation.