AIOps Outcomes Depend on Data Quality, Not Algorithms

Conversations about artificial intelligence (AI) in operations tend to focus on the brain: the algorithms. New models promise better automation, faster correlation, and more accurate prediction, yet outcomes may not improve in meaningful ways. The issue is usually not model sophistication, but the quality and structure of the telemetry feeding the analytics pipeline.

Artificial intelligence for IT operations (AIOps) effectiveness begins upstream. The industry has moved from basic monitoring toward advanced observability, yet sophistication alone has not cured the “garbage-in, garbage-out” (GIGO) problem. If anything, it’s raised the stakes. A simple rule can survive a messy data point, but complex algorithms are far more sensitive to poor input, as seen in the way the relationship between data and logic has evolved:

1. Early 2000s: Operational tooling relied on threshold alerts and deterministic rules. In this era, bad input data typically produced a false positive that was easy to spot.
2. 2010s: Statistical correlation techniques expanded visibility as data volumes increased. Garbage in evolved into a “correlation storm,” where a single bad data point triggered cascading alerts and misleading signals across dashboards.
3. Mid-2010s: The term AIOps emerged (coined by Gartner in 2016) to describe using machine learning (ML) to interpret operational data. GIGO evolved into a “black box” problem, where models learned incorrect patterns from inconsistent datasets.
4. Today: Platforms use predictive modeling to anticipate network and service behavior, but the business consequences of poor data have grown significantly more severe.

Automation is the engine of AIOps, but it should only “shift into gear” if confidence is high. When telemetry is inconsistent, automated actions become risky, forcing workflows back to human-in-the-loop review and manual escalation. This same data gap blurs cause and effect, stalls troubleshooting, expands mean time to knowledge (MTTK), delays mean time to resolution (MTTR), and can lead teams to incorrect conclusions. In the end, poor data becomes the real bottleneck, because data readiness matters more than algorithmic power.

Why Telemetry Quality Matters for AIOps and ITOM

Modern hybrid environments rarely behave within neat architectural boundaries. Hybrid infrastructure, remote locations, and cloud-native workloads create interaction paths that cross multiple domains. Metrics, logs, events, and traces (MELT) each provide useful perspective, but on their own rarely preserve full interaction context. This fragmentation does not just affect analytics. MELT sources produced by different vendors often vary in structure, completeness, and context, and these gaps introduce uncertainty that directly affects operational execution and resilience.

AIOps platforms may surface correlations or predictions, but IT operations management (ITOM) workflows must translate those insights into action through event normalization, dependency mapping, and automated remediation. Traditional MELT telemetry reflects the perspective of individual systems or devices rather than the behavior of the communications path itself. User experience, however, is shaped by round-trip service interactions across that path. Observability derived from deep packet inspection (DPI) captures that interaction-level behavior, providing context that conventional telemetry alone cannot preserve. When telemetry isn’t consistent, correlation becomes noisy, service maps drift from reality, and automation hesitates to act decisively.

Seeing how services interact preserves timing and dependency context across environments. Delivering curated datasets downstream improves analytical interpretation and gives IT operations workflows the context to automate actions based on actual service conditions.

Preparing Telemetry for Reliable Operational Automation

Improving AIOps outcomes is about ensuring telemetry arrives with enough context and integrity to support actions. Organizations moving toward confident automation increasingly focus upstream, strengthening practices that preserve behavioral accuracy throughout the ecosystem. Common priorities include:

• Capturing telemetry based on real service interactions rather than abstract health indicators
• Maintaining temporal synchronization to preserve causal sequencing
• Preserving cross-domain dependency context across network, infrastructure, and application layers
• Reducing analytical noise through dataset refinement before modeling
• Delivering structured telemetry suited for AIOps interpretation and ITOM workflow execution

These practices narrow interpretation gaps, increase confidence in analytical outcomes, and reduce reliance on manual validation. As environments grow more complex, telemetry readiness becomes critical for scaling AIOps initiatives.

NETSCOUT’s Advantage

Improving observability data upstream helps break the GIGO cycle, allowing analytics platforms to generate reliable insights and giving ITOM workflows the context to act precisely. NETSCOUT’s Omnis AI Sensor and Omnis AI Streamer enable this by delivering curated datasets derived from NETSCOUT Smart Data to partner platforms, improving interpretation and automation. By prioritizing data integrity, organizations move beyond GIGO limitations and strengthen AIOps outcomes.

Stop the “garbage-in, garbage-out” cycle. Learn how NETSCOUT Omnis AI Insights integrates with Splunk and ServiceNow to deliver actionable intelligence for AIOps and other initiatives.