Data Granularity vs Processing Complexity
Retain granular source data but precompute decision-level summaries so Solvency II risk models access necessary precision without burdening every operational process with full dataset complexity.
CyberTRIZ analysis · Insurance contradiction DO021 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Granular insurance data can improve underwriting, pricing, claims analysis, fraud detection, customer segmentation, and portfolio management by revealing patterns hidden within aggregated information. Increasing granularity, however, creates larger datasets, more variables, additional integration requirements, greater computational demand, and more difficult governance. Excessive aggregation simplifies processing but can remove distinctions that materially improve decisions.
Insurance TRIZ Resolution
Insurers can retain detailed source data while varying the level of granularity used for different decisions. Routine processes can operate on summarized or precomputed information, while detailed records are accessed when additional precision materially affects the outcome. Analytical architectures can therefore preserve granular information without requiring every process to manipulate the complete dataset.
Applicable TRIZ Principles
Principle 1 – Segmentation separates data according to the level of detail required for different decisions.
Principle 10 – Prior Action precomputes frequently required summaries and analytical features.
Principle 26 – Copying uses efficient representations of detailed information for routine processing.
Expected Outcome
Greater analytical precision
Lower processing complexity
Faster routine decisions
More efficient data utilization
Decision Indicators
Early indicators that this contradiction is limiting technology performance include:
Routine processes repeatedly analyze unnecessarily detailed datasets.
Aggregated information conceals material risk differences.
Processing time grows rapidly as additional variables are introduced.
Multiple teams independently create similar data summaries.
Analytical projects collect greater detail without demonstrating additional decision value.
Monitoring these indicators helps insurers preserve useful granularity while matching information depth to actual decision requirements.