Signal vs. Noise: Why More Data Often Means Less

Part 3 of a 3-part series on bringing manufacturing reliability principles to modern IT operations

A Tale of Two Production Lines

At a semiconductor fabrication plant, a critical etching process monitors hundreds of parameters every second. Yet operators focus on just three key measurements that truly indicate quality issues. When these signals deviate, production stops immediately. Meanwhile, in a modern IT operations center, an alert storm of 500+ notifications floods the screens. Critical customer-impacting issues are lost in the noise, detected only when users complain.

The Fundamental Problem

We're drowning in data but starving for signals. Consider these realities from typical enterprise environments:

• Alerts spread across multiple observability tools (Datadog, New Relic, CloudWatch, etc.) without unified view
• Critical service alerts buried under monitoring noise
• Up to 40% of alerts auto-resolve within 5 minutes, indicating noise
• Teams struggling to identify most frequent vs most important alerts

The problem isn't lack of data—it's too much data.

Manufacturing vs. IT: The Semiconductor Parallel

Let's examine how a modern semiconductor fab maintains quality through signal processing:

1. Signal Definition

Semiconductor Manufacturing:

• Three critical measurements per process step
• Clear acceptable ranges for each parameter
• Automated distinction between process variation and true defects
• Immediate action on real quality signals

IT Equivalent:

• Golden signals for each service (latency, errors, saturation)
• Alert categorization by integration type and service
• Analytics-driven alert thresholds
• Auto-classification of alert importance

2. Noise Filtering

Semiconductor Manufacturing:

• Background variation handled automatically
• Normal process fluctuations filtered out
• Focus only on actionable deviations
• Clear separation of signal types

IT Equivalent:

• Automatic filtering of short-lived alerts (<5 minutes)
• Categorization by application service
• Focus on persistent vs transient issues
• Clear alert analytics dashboards

3. Signal Processing

Semiconductor Manufacturing:

• Real-time correlation of multiple parameters
• Automatic noise cancellation
• Pattern recognition for emerging issues
• Early warning system for quality drift

IT Equivalent:

• Integration-type based alert correlation
• Service-based alert grouping
• Trend analysis of frequent alerts
• Pattern detection across services

The Key Insight

In semiconductor manufacturing, signal processing ensures:

• Only meaningful deviations trigger alerts
• Each alert requires specific action
• Noise is filtered automatically
• Patterns inform future responses

The Fundamental Shift Required

We must move from:

"Collecting everything" to "Measuring what matters"

• Define golden signals for each service
• Implement intelligent alert aggregation
• Measure signal-to-noise ratio through analytics

"Alert volume" to "Alert value"

• Filter out noise automatically
• Correlate related alerts
• Focus on actionable signals

"Manual filtering" to "Automated processing"

• Implement Anomaly detection
• Automate known response patterns
• Enable context-based aggregation

For Leaders Reading This

Ask yourself:

• Have you identified your critical signals among the noise?
• Are your alert thresholds based on business impact?
• Do your teams have clear signal processing protocols?
• How effectively are you filtering out alert noise?

Series Conclusion

This series has explored how manufacturing principles can transform our approach to IT operations. From understanding the reliability crisis to implementing control points and managing signals, we've seen how decades-old manufacturing wisdom remains relevant to modern technology challenges.

The path forward isn't about more tools or more data. It's about better control, clearer signals, and automated responses. Because in the end, watching things fail better isn't the same as making them work reliably.

About the author

Mohan Narayanaswamy Natarajan is a technology executive and entrepreneur with over 20 years of experience in operations and systems management. As co-founder and CEO of Temperstack, he focuses on Site Reliability Engineering (SRE) process automation. His career includes leadership roles at ITC, Inmobi, Pinelabs, Practo & Amazon,  Mohan has also worked as a consultant at The Boston consulting group (BCG),  He has experience in implementing large-scale systems, leading teams, and establishing business resilience mechanisms across various industries.

‍