PD026
Begin process discovery on the most recent, cleanest data subset first, extending backward only where data quality supports confident conclusions.
CyberTRIZ analysis · Process contradiction PD026 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Faster Legacy Process Discovery vs. Better Data Quality
Business Context. Discovering how a legacy process actually operates often requires mining years of historical system data, but that data is frequently incomplete, inconsistent, or recorded in outdated formats that slow analysis.
Process TRIZ Resolution. Rather than waiting to clean the entire historical dataset, organizations should discover the process using the most recent, cleanest subset of data first, then extend the analysis backward only as far as data quality allows confident conclusions.
Applicable TRIZ Principles
Principle 1 (Segmentation) starts discovery with the cleanest recent data subset rather than the entire history.
Principle 3 (Local Quality) applies different confidence levels to conclusions drawn from data of varying quality.
Principle 21 (Skipping) moves past low-quality historical data quickly rather than attempting to fully clean it.
Expected Outcome
Faster discovery
Reliable conclusions
Reduced data cleansing burden
Clear confidence boundaries
Decision Indicators
Discovery projects stall waiting for historical data cleansing.
Conclusions are drawn from data known to be unreliable.
Legacy systems produce inconsistent or missing records.
Data quality issues are discovered only late in the project.
Teams cannot state how far back their conclusions are valid.
If several of these indicators are present, the contradiction is likely active and the Process TRIZ resolution above should be evaluated.