Retail Data Is Messy. Your AI Should Handle That.
Missing values, inconsistent formats, 50-tab Excels — real retail data is nothing like clean demos.
Ersel Gökmen
March 10, 2026
Every AI demo uses clean, perfectly structured data. Real retail data looks nothing like that.
A typical retailer's data includes: an ERP export with German column names, a Shopify CSV with different product IDs, a supplier Excel with 50 tabs (one per brand), and a Google Sheet the buying team maintains manually. Nothing matches. Everything has gaps.
Why This Matters for AI
Most AI tools fail silently on messy data. They either refuse to work ("unsupported format") or produce garbage results without warning. Both are worse than useless — they erode trust.
Our Approach: Parse Everything, Validate Always
Mondian's extraction layer handles CSV, Excel (including multi-tab workbooks with formulas), PDF tables, JSON, and Parquet. It detects column types, handles missing values, and normalizes formats before any analysis begins.
More importantly, it tells you what it found: "48 sheets, 12,400 rows, 8 tables detected. 3 columns have >10% missing values." Transparency before analysis.