Observability vs. Data Volume

Business Context

Modern telecommunications networks generate large volumes of telemetry from radio systems, transport networks, core functions, cloud platforms, applications, infrastructure, security systems, and customer-facing services. Greater observability can improve fault diagnosis and performance analysis, but collecting every available metric, log, event, and trace creates substantial storage, processing, transport, and analytical requirements. Excessive data can also make important signals harder to identify.

Telecommunications TRIZ Resolution

Observability should prioritize useful information rather than maximum data collection. Telemetry can be filtered, aggregated, sampled, and processed close to its source, while detailed information is retained or generated when abnormal conditions require deeper investigation. Context-aware collection policies can increase visibility around affected services without maintaining maximum telemetry depth across the entire network continuously.

Applicable TRIZ Principles

Principle 2 – Taking Out removes redundant or low-value telemetry before it consumes downstream resources.

Principle 15 – Dynamics changes collection depth according to network conditions and diagnostic requirements.

Principle 23 – Feedback uses detected conditions to determine which additional information should be collected.

Expected Outcome

Stronger operational observability

Lower telemetry storage and processing requirements

Faster identification of relevant information

Reduced monitoring infrastructure cost

Decision Indicators

Early indicators include:

Telemetry volume grows substantially faster than operational value.

Large amounts of collected data are rarely queried.

Storage and analytics costs become major observability expenses.

Operators struggle to locate relevant signals within large datasets.

Every network element retains the same telemetry regardless of criticality or condition.