Goal: remediate catalog issues fast without risking bad mass changes. Bulk Fix Workflows combine precise targeting, safe previews, approvals, and rollbacks.
1) Target precisely
- Segment filters: category, brand, attribute missing/invalid, price/stock anomalies, broken media.
- Saved segments: re-runable queries for recurring clean-ups (e.g., “TVs missing diagonal”).
2) Preview & diff
- Before/after view: show exact field-level changes; highlight risky fields (price/stock).
- Dry-run counters: how many SKUs affected, errors expected, time estimate.
3) Approvals & guardrails
- DuaI-control: maker-checker for high-impact changes; require reason and ticket link.
- Rate limits: cap per minute to avoid downstream throttling; pause/resume safely.
4) Automation rules
- Reusable fixers: taxonomy mapper, unit converter, image validator/resizer, price normalizer.
- Scheduling: run off-peak, respect sync windows and destination SLAs.
5) Rollback & audit
- Change sets: every run persisted with diff; one-click revert if metrics regress.
- Observability: alerts on error spikes; link incidents to the responsible change set.
Playbook examples
- Image Fixer: detect broken/low-res heroes, auto-replace from supplier gallery or placeholders with alt text.
- Taxonomy Fixer: remap vendor categories by rules and synonyms.
- Price Fixer: normalize VAT/rounding, enforce MAP floors, flag negative margins.
- Validation Sweep: fix booleans, units, and enum drift; quarantine unfixables.
Outcome
Bulk fixes become safe and repeatable: fewer incidents, faster catalog improvements, and measurable lift in search/merch KPIs.
What's next
Next up: Data Contracts with Destinations — schema guarantees, backward compatibility, and contract tests.