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May 18, 2026

DATA QUALITY IS MORE IMPORTANT THAN THE AI MODEL

Explains why poor telecom data quality limits the effectiveness of AI-driven RAN optimization more than the AI model itself.

Realistic telecom operations center showing engineers analyzing inconsistent RAN data, KPI dashboards, and AI-driven analytics across a complex multi-vendor network environment.

DATA QUALITY IS MORE IMPORTANT THAN THE AI MODEL

Everyone wants better AI models. Bigger models. Smarter algorithms. More automation. But in real telecom environments, the biggest limitation is often much simpler: Data quality.

Because even the most advanced AI system cannot make reliable decisions from unreliable network data. And this is where many AI discussions disconnect from operational reality.

From my experience in RAN optimization, telecom data is far from clean or consistent.

  • KPI Definitions Often Differ Across Vendors, making normalization and interpretation significantly more difficult.
  • Missing Or Incomplete Counters Can Distort Performance Analysis and lead to inaccurate optimization recommendations.
  • Time Synchronization Problems Across Systems create inconsistencies when correlating events and network behavior.
  • Different Granularity Levels Between Data Sources complicate root cause analysis and predictive modeling.

And here’s the key issue: AI models assume patterns are meaningful. But in telecom, many patterns are artifacts of poor data quality. Which means the model may confidently optimize the wrong thing.

  • A congestion issue may actually be a reporting inconsistency.
  • A mobility problem may be caused by missing neighbor data.
  • A traffic anomaly may simply come from delayed counter collection.

This is why I believe most AI problems in telecom are not really AI problems. They are data problems. And solving them requires more than data scientists.

It requires operational understanding of how networks behave in the real world. Because before AI can become truly autonomous… The network data itself must become trustworthy.

Otherwise, we risk automating incorrect decisions at scale. And in RAN, scale amplifies everything. Including mistakes.

This is part 3 of my series on AI in RAN. Next post: AI-ASSISTED RAN VS AUTONOMOUS RAN

What’s your experience?

How mature do you think telecom data infrastructure really is for AI-driven operations?

#AI #RAN #ORAN #SMO #DataQuality #RANOptimization #AIinTelecom #5G