Dealer Data Transformation Pipeline

The Problem

Dealer data arrived in chaos. Different formats from different systems. Missing fields. Inconsistent naming. Duplicates everywhere.

The operations team spent hours every week just cleaning data before they could analyze anything useful.

The Solution

An automated transformation pipeline that:

1. Ingests data from multiple sources
2. Validates against business rules
3. Normalizes formats and naming conventions
4. Deduplicates using fuzzy matching
5. Enriches with additional data points
6. Outputs clean, analysis-ready datasets

The Impact

• 260+ hours saved annually in manual data cleaning
• Data quality improved from ~60% to 98%
• Faster decisions - analysis starts immediately
• Audit trail for compliance requirements

Technical Details

The system uses a multi-stage validation approach. Each record passes through business rule checks, format validation, and duplicate detection.

Custom fuzzy matching algorithms handle variations in dealer names and addresses that would slip past exact matching.

BigQuery integration provides historical analysis and trend detection.