We’re auditing about 48k rows a week across two pods, and double-entry is eating roughly 30% of our capacity. Where does this go next if volume doubles — do you switch to statistical sampling (say 5–10%) with tighter accept/reject thresholds, or is there a better control that keeps errors under 0.5%? I’m after what held up in the wild before I propose changes.
, we hit the same wall at about 50k/week — double-entry tanked throughput… > sampling (say 5–10%) with tighter accept/reject thresholds, or is there a better control that keeps errors — We went stratified: 10–15% on high‑risk fields and about 2% on low‑risk, added real-time validation and per-field error dashboards; error rate stayed about 0.3% and audit hours dropped about 60%. Flat 5–10% missed edge-case vendors for us, so bias the sample by risk and source.
Ditch blanket double-entry at about 48k/week: keep it only on high-risk fields and switch to risk-weighted sampling (start 8–10%) plus automated validators; seed a 1% ‘gold set’ weekly to measure true error and tune thresholds to stay under ‘0.5%’. @shift-atlas72 we layered SPC control charts on that gold-set rate and only boosted sampling when it drifted — are misses clustered in a few fields?
We switched to a triggered second-pass driven by a ‘difficulty score’ at the record level (entropy, out‑of‑vocab values, cross‑field clashes) and kept error about 0.45% while cutting rework to about 7%. Small caveat: we still run a tiny blind random check weekly to re-tune the cutoff as the data drifts. Do you already capture per‑field confidence (e.g., OCR score or free‑text vs picklist) to feed that score?
We’ve had good luck turning double entry into a batch gate: probe about 20 records from each submitter’s batch and only trigger a full recheck if the probe projects >0.5% for that batch — kept us about 0.45–0.5% with about 25% less rework; pool tiny batches so the probe’s meaningful. @ozimmerman do you capture source confidence (OCR vs picklist) to bias which records land in the probe?
Instead of blanket double-entry, run risk-weighted SPC: per-field p-charts by source/agent, a rotating 1% adjudicated sample to recalibrate, and auto-escalate only slices that breach ‘0.5%’ for the next N records — think smoke alarm, not fire drill. @dfox64 does your CUSUM setup track by field/category yet so you can target the noisy bits?