Measuring route-adherence defects before changing production behavior
I investigated customer reports about route adherence instead of starting with a code change. Production queries and staging reproductions exposed an overly broad diagnostic rule; after narrowing it, I used the evidence to guide targeted fixes. The counts describe different populations, and the post-release trend was a team measure rather than an isolated result of my work.
Context
Customer reports pointed to route-adherence values that did not look right. Before changing production behavior, I wanted to know which records were actually wrong and which were expected product behavior.
Problem
The first query grouped together real defects, empty routes that correctly displayed zero, and internal test data. A later comparison also flagged values that would change without proving they were incorrect. A broad correction based on those results could have changed valid customer data.
Decision
I compared read-only production results with controlled staging reproductions. When a review of candidate cycles showed that my diagnostic rule was too broad, I changed the criterion and re-ran the analysis. For each reproduced defect, I wrote down the expected result first and used a control route to check that the rule itself was not causing the difference.
04Implementation
Only after the investigation did I split the work into small, single-cause fixes, each with a regression test. The proposal moved from a broad historical correction to targeted changes for defects we could demonstrate.
Impact
The later comparison covered 34,320 cycles; after reviewing 4,842 candidates against the narrower criterion, 88 demonstrable defects remained. Those counts describe that later analysis only and are not interchangeable with the separate 120-day review. A product analyst also reported a post-release team Data Quality improvement; the releases included other contributors, so I do not present that trend as my individual result.