The Audit Process
A thorough product data audit follows five steps: Export, Analyze, Score, Prioritize, and Plan. Each step builds toward a clear action plan for improvement.
Export
Analyze
Score
Prioritize
Plan
Step 1: Export Your Data
Start by exporting all product data from your current systems. You need a complete picture, not a sample.
Data Sources to Export
- Website/E-commerce backend
- PIM (Product Information Management) system
- ERP/inventory system
- Spec sheets and PDFs (if data is only there)
- Image asset libraries
Step 2: Analyze Completeness
For each field, calculate the fill rate—what percentage of products have that data point?
| Field | Example Fill Rate | Status |
|---|---|---|
| Product name | 100% | Good |
| Dimensions | 72% | Needs work |
| Materials | 45% | Critical |
| Fire rating | 12% | Critical |
Step 3: Score Quality
Beyond completeness, assess data quality. Even filled fields may have issues:
Consistency
Are dimensions always in cm? Are material names standardized?
Accuracy
Spot-check: do the dimensions match the actual products?
Currency
When was data last updated? Are prices current?
Structure
Is data in separate fields or buried in descriptions?
Step 4: Prioritize Gaps
Not all gaps are equal. Prioritize based on impact:
Priority Matrix
High Priority
- • Missing for >50% of products
- • Frequently searched attributes
- • Required for filtering
Medium Priority
- • Missing for 20-50% of products
- • Nice-to-have for searches
- • Enrichment opportunities
Step 5: Create an Action Plan
Based on your priorities, create a realistic action plan:
Sample Action Plan
Immediate: Add dimensions to 150 products missing them
Owner: Product team
Short-term: Standardize material names across catalog
Owner: Data team
Medium-term: Add application context tags to all products
Owner: Marketing team
Ongoing: Establish data quality checks for new products
Owner: Operations
Common Audit Findings
Based on audits we've conducted, here are the most common issues:
- 1. Dimensions in descriptions: "Approximately 80cm wide" instead of structured width field
- 2. Missing certifications: Products have certifications but they're not in the data
- 3. Inconsistent categories: Same product type in multiple category names
- 4. Outdated images: Low resolution, watermarked, or showing discontinued variants
- 5. No application context: No indication of where products are suitable