Real-Time Data vs System Load
Apply event-driven, tiered refresh frequencies aligned to business criticality, meeting NIS2 resilience and MAS TRM availability requirements without unsustainable infrastructure load.
CyberTRIZ analysis · Insurance contradiction DO022 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Real-time information can improve underwriting, fraud detection, claims triage, customer service, pricing, and operational monitoring. Continuously processing every data change, however, can create substantial infrastructure demand and integration traffic. Many insurance decisions do not require second-by-second updates, while others can lose value if information is delayed.
Insurance TRIZ Resolution
Data refresh frequency can be determined according to the time sensitivity of each decision. Fraud alerts, critical customer transactions, and selected risk signals may require immediate processing, while portfolio reporting, historical analysis, and stable reference information can update periodically. Event-driven architecture can trigger processing when meaningful changes occur instead of continuously refreshing unchanged information.
Applicable TRIZ Principles
Principle 19 – Periodic Action uses appropriate update intervals for information that does not require continuous processing.
Principle 15 – Dynamics changes refresh frequency according to business need.
Principle 23 – Feedback triggers processing when relevant events or conditions change.
Expected Outcome
Faster access to time-sensitive information
Lower infrastructure load
Reduced unnecessary processing
More scalable data architecture
Decision Indicators
Early indicators that this contradiction is limiting technology performance include:
Large volumes of data are refreshed despite little underlying change.
Infrastructure costs increase substantially as real-time capabilities expand.
Critical decisions still depend on stale information.
Business teams request real-time data without defined operational use.
System performance deteriorates during high-volume data events.
Monitoring these indicators helps insurers provide immediate information where timing creates value without making every insurance dataset continuously real-time.