Proactive Monitoring Using Streaming Telemetry for Enterprise Network Resilience

Abstract

Polling-based monitoring architectures impose a structural measurement ceiling on enterprise networks that hybrid infrastructure environments consistently exceed. Simple Network Management Protocol collection, operating at five-to-fifteen-minute intervals, cannot resolve the transient anomalies and short-duration control-plane events that cause a disproportionate share of service degradation in distributed manufacturing and corporate environments. This article argues that streaming telemetry, when architecturally integrated with machine learning-based anomaly detection, automated root cause analysis, and closed-loop remediation workflows, produces operational outcomes that polling-based models cannot approximate, and that the integration architecture connecting these components is the primary determinant of those outcomes. Analysis of enterprise hybrid deployment patterns suggests that telemetry pipeline design, adaptive baseline modeling, and remediation governance must be treated as interdependent architectural decisions rather than sequential technology adoptions. The resulting observability-driven operational framework substantially reduces mean time to detection and supports movement toward predictive and autonomous network operations at scale. This article is presented as an architectural and conceptual synthesis of the enterprise network observability literature rather than as an empirical study. It does not report pilot deployment measurements, controlled experiments, or benchmark data; its claims are intended to establish a research and practice agenda, and readers should evaluate them accordingly (see Section VII, Limitations).