RAN Telemetry for AI: Data Quality, Timing and Feature Governance

A model cannot repair an undefined measurement

RAN telemetry becomes useful for AI only when its meaning and collection conditions are understood. A counter name alone does not establish whether it is cumulative, interval-based or reset by a software event. Record units, source versions, population and collection windows before building features. When several sites supply similarly named fields, verify that their semantics match. A well-formed dataset can still encode incompatible measurements, producing predictions that look precise while answering a different question from the one operators intended.

Preserve event time and arrival time

Event time describes when the underlying observation occurred; arrival time describes when a collector received it. Both matter for network decisions. Delayed transport, processing queues and inconsistent clocks can make an apparently recent dataset operationally stale. Carry timestamps through transformations and define how late events affect features. Do not silently rewrite history in a way that makes a retrospective experiment appear to have information that would not have been available at the actual decision moment.

Make missingness visible

Missing records do not automatically mean zero demand or healthy equipment. They may reflect a collector outage, permission change or failed export. Track completeness by source and interval, and expose quality flags to analytics consumers. Document whether missing values are excluded, imputed or treated as a separate state. Different choices can change the model's behavior. Test the pipeline with intentional gaps and duplicates so that expected quality warnings are demonstrated rather than assumed from configuration settings.

Prevent training and evaluation leakage

An offline model should not receive future observations or post-incident labels as if they were available beforehand. Split data in a way that respects the intended operating environment, including relevant time and site boundaries. Keep preprocessing rules fitted only on permitted training data. If records from the same event appear on both sides of an evaluation split, apparent generalization may be exaggerated. Evaluate whether the model handles a genuinely new period or location, not merely another copy of a familiar incident.

Govern feature changes as interface changes

A collector update can change the meaning of a feature without breaking its file format. Establish owners for feature definitions and a versioned contract for transformations. Record how counter resets, maintenance windows and late corrections are handled. A release check should compare both schema and statistical behavior against approved expectations. When a feature is retired or redefined, identify affected models and reports before deployment. Quietly changing the input while retaining the same model version undermines later investigation.

Collect only what the decision requires

Telemetry can include sensitive identifiers or information about user activity. Determine which fields are necessary, who may access them and how long they should remain available. Use appropriate security and legal review for the actual deployment. A capacity question may be answerable with aggregated data rather than detailed individual histories. Finish the first pipeline review by tracing one prediction back to its source records and quality status. If that trail cannot be reconstructed, improve the data process before granting the model operational authority.

Questions for the project review

For the first data-quality exercise, select one approved feature and calculate it independently from retained source records. Compare the result with the production pipeline over a known interval. Investigate any difference in filtering, timestamps or missing-value treatment. The purpose is not to prove that every feature is perfect, but to establish a repeatable method for checking definitions before they become dependencies of automated network decisions.