EP016

Greater Cross-Process Performance Visibility vs. Simpler Data Architecture

Business Context. Visibility into how performance in one process affects downstream processes provides valuable enterprise-wide insight, but connecting performance data across many processes requires a more complex data architecture than tracking each process in isolation.

Process TRIZ Resolution. Rather than building bespoke connections between every pair of related processes, organizations should adopt a common data model and shared identifiers across processes, allowing cross-process visibility to emerge from a consistent architecture rather than custom integration work.

Applicable TRIZ Principles

Principle 33 (Homogeneity) adopts a common data model and shared identifiers across processes.

Principle 5 (Merging) consolidates what would otherwise be bespoke cross-process connections into a unified architecture.

Principle 6 (Universality) designs the data architecture to serve cross-process analysis broadly rather than case by case.

Expected Outcome

Rich cross-process visibility

Simplified data architecture

Reduced custom integration effort

Scalable enterprise analytics capability

Decision Indicators

Cross-process analysis requires bespoke integration work for each new pairing.

No common data model or shared identifiers exist across enterprise processes.

Data architecture complexity has grown with each new cross-process analysis need.

Analysts spend more time reconciling data than generating insight.

Enterprise-wide performance questions cannot be answered without extensive manual effort.

If several of these indicators are present, the contradiction is likely active and the Process TRIZ resolution above should be evaluated.