Data quality
Bad data should be called out early.
Coodra should identify missing costs, stale stock, duplicate products, and connector gaps before the queue treats them as truth.
Bad data should not hide inside a pretty queue
Retail data gets messy because stores are real places. Products are renamed, costs go missing, stock counts drift, suppliers change timing, and duplicate catalog records sneak in. Coodra should detect those problems early and explain how they affect confidence.
Common data quality issues
Issue
Effect on Coodra
Missing cost
Weakens margin recommendations and pricing review.
Duplicate SKU
Splits sales movement across records that should be one product.
Stale stock count
Makes stockout and reorder timing less reliable.
Unclear supplier
Makes lead-time and reorder accountability harder to explain.
Inactive products still selling
Creates confusing candidates that need catalog cleanup first.
How Coodra should behave
If a recommendation depends on weak data, the product should say so. Sometimes the correct recommendation is not reorder this product. Sometimes it is fix the product record before trusting the reorder math.