Telemetry Depth vs. Data Processing Cost
Apply adaptive, condition-triggered telemetry collection with edge pre-processing to minimise personal-data exposure and storage costs while retaining NIS2-required network visibility.
CyberTRIZ analysis · Telecommunications contradiction TA034 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Detailed telemetry improves fault diagnosis, AI models, performance analysis, and automation by providing richer information about network behavior. Increasing sampling rates and metric depth, however, also increases bandwidth, storage, processing, and analytical cost. Continuous high-resolution collection across the complete network can become economically and operationally inefficient.
Telecommunications TRIZ Resolution
Telemetry resolution should vary according to network condition, service importance, and analytical need. Routine operation can use summarized data, while anomalies trigger higher-frequency or more detailed collection. Local processing can also reduce the volume sent to central platforms by extracting relevant features near the data source.
Applicable TRIZ Principles
Principle 2 – Taking Out removes unnecessary raw data before centralized processing.
Principle 15 – Dynamics adjusts telemetry depth according to operating conditions.
Principle 23 – Feedback increases detail automatically when detected behavior requires deeper analysis.
Expected Outcome
Sufficient analytical visibility
Lower processing and storage cost
Reduced telemetry transport requirements
More efficient data platforms
Decision Indicators
Early indicators include:
Telemetry costs increase faster than network scale.
High-resolution data is retained but rarely used.
Every network element uses identical collection policies.
AI or analytics programs require large infrastructure primarily to process raw telemetry.
More data does not materially improve operational decisions.