Inside A Signal Audit #004: What the BIRCH Algorithm Revealed About Production Telemetry
Most engineering teams do not suffer from a lack of telemetry.
They suffer from a lack of clarity.
Modern production systems generate metrics, logs, traces, events, alerts, dashboards, notifications, incidents, and reports at a pace that exceeds human capacity to process them effectively. The result is a familiar pattern:
More data.
More dashboards.
More alerts.
More confusion.
The question is no longer:
"What data do we have?"
The question is:
"What deserves our attention?"
The Problem
During production operations, teams often treat telemetry as a single stream of information.
In reality, not all signals carry the same operational value.
Some signals are noise.
Some represent healthy system behavior.
Some indicate temporary spikes.
Others represent persistent degradation that may eventually become a customer-facing incident.
Without a framework for classification, engineers are forced to manually determine which signals matter and which can be ignored.
This increases investigation time and slows operational decision-making.
The Approach
To better understand production behavior, a cluster-based machine learning model was applied using the BIRCH algorithm within Splunk Machine Learning Toolkit (MLTK).
Rather than generating additional alerts, the objective was to identify behavioral groupings within telemetry.
Signals were classified into categories such as:
Noise
Low-value signals that did not require investigation.
Baseline
Expected operational behavior.
Spiky Signals
Short-lived anomalies that returned to normal conditions.
Persistent Degradation
Signals demonstrating sustained performance deterioration.
Critical Signals
Patterns requiring immediate operational attention.
The Finding
The most valuable outcome was not anomaly detection.
The most valuable outcome was prioritization.
Once telemetry was grouped according to behavioral characteristics, engineers could quickly determine:
Which signals required investigation.
Which signals represented normal system behavior.
Which patterns were worsening over time.
Which signals could be safely ignored.
The model created a decision-making framework rather than simply another monitoring layer.
The Signal Audit Lesson
Organizations often believe they need more monitoring.
In many cases, they already have enough telemetry.
What they lack is a system for determining which signals deserve attention.
Signal Audit was built around this principle.
The objective is not to collect more data.
The objective is to transform telemetry into operational decisions.
Signal over noise.
Interested in understanding what your production systems are actually telling you?
Book a Signal Audit.
