The Data Preparation Framework
Preparing product data for AI involves four key phases: Audit, Structure, Enrich, and Maintain. Each phase builds on the previous to create a comprehensive, AI-optimized product database.
Audit
Assess current state
Structure
Organize data fields
Enrich
Add context & tags
Maintain
Keep data current
Phase 1: Audit Your Current Data
Before making changes, understand what you have. Export your current product data and assess:
Audit Checklist
- How many products do you have in total?
- What percentage have complete dimensions?
- How many have material specifications?
- Are certifications documented per product?
- Is pricing information available?
- Are images high-quality and consistent?
Phase 2: Structure Your Data
Transform marketing descriptions into structured fields. AI needs data in predictable formats with standardized values.
Before (Unstructured)
After (Structured)
{
"name": "Milano Chair",
"category": "Seating > Lounge Chairs",
"dimensions": {
"width_cm": 80,
"depth_cm": 75,
"height_cm": 82,
"seat_height_cm": 45
},
"materials": {
"upholstery": "full-grain leather",
"frame": "solid oak",
"origin": "Italy"
},
"colors": ["black", "cognac", "navy", "cream"],
"applications": ["residential", "commercial", "hospitality"],
"style": "contemporary"
}
Core Data Fields
Every product should have these fundamental fields:
Identity
- • Product name
- • SKU / Model number
- • Brand name
- • Collection (if applicable)
- • Category / Subcategory
Physical
- • Dimensions (W × D × H)
- • Weight
- • Materials
- • Colors / Finishes
- • Configuration options
Commercial
- • Price (or price range)
- • Availability status
- • Lead time
- • Minimum order quantity
- • Warranty terms
Media
- • Primary image URL
- • Additional images
- • Technical drawings
- • 3D model files
- • Spec sheet PDF
Phase 3: Enrich Your Data
Basic structured data gets you found. Enriched data gets you recommended. Add context that helps AI match your products to user needs.
Enrichment Categories
Application Context
- • Suitable environments
- • Use cases
- • Project types
- • User scenarios
Performance Data
- • Test certifications
- • Durability ratings
- • Safety compliance
- • Sustainability credentials
Style Attributes
- • Design style
- • Aesthetic mood
- • Era/period influence
- • Designer/architect
Relationships
- • Complementary products
- • Collection items
- • Alternative options
- • Accessories
Phase 4: Maintain Your Data
Data quality degrades over time. Products are discontinued, prices change, new items are added. Establish maintenance processes:
-
Monthly reviews
Check for discontinued products, price updates, availability changes.
-
New product onboarding
Process to add new products with complete data from day one.
-
Quarterly audits
Full review of data completeness and accuracy across all products.
Getting Started
Don't try to perfect everything at once. Start with your best-selling products and expand from there:
-
1
Start with top 20 products
Your best sellers or most strategic items.
-
2
Complete all core fields
No gaps in fundamental data.
-
3
Add enrichment data
Application contexts and performance data.
-
4
Submit to Fringe
Get these products into AI search.
-
5
Expand systematically
Add more products following the same process.