Everything downstream reads it
Product data is not just what appears on the page. It feeds your filters, your internal search, your marketplace listings, your advertising feeds and any recommendation logic.
Inconsistent data therefore breaks several things at once, and each of them fails silently — a filter that returns nothing, a feed item rejected, a search that misses.
What consistency means
- Same attribute names across products — not Colour, Color and Shade
- Same units — millimetres or centimetres, decided once
- Same value formats — Blue, not blue and BLUE
- Complete — every product has every attribute that matters for its type
- Accurate, which sounds obvious and frequently is not
Three variations of a colour name mean your colour filter shows three entries for the same thing, and shoppers filtering by one of them miss two thirds of the range.
Fixing an existing catalogue
Export everything to a spreadsheet, sort by each attribute, and the inconsistencies become obvious immediately. That single exercise usually finds several hundred small problems.
- Standardise the attribute names first
- Then the value formats
- Then fill the gaps, prioritising products with traffic
- Then re-import and verify the filters improved
Prevent it recurring
New products should be created from a template with the required attributes present. Anyone adding a product should be filling in fields, not deciding what fields to have.
That discipline is what keeps a catalogue clean over years rather than requiring another cleanup annually.
Where AI helps
Extracting attributes from supplier descriptions and datasheets, and matching inconsistent values to your standard set, are both jobs models do well at scale.
The standard set itself should be decided by a person who understands the products. The tedious application of it can be automated.