Product data enrichment is filling in the attributes a catalogue is missing — composition, category, colour, fit — so products can be searched, filtered, and merchandised.
Oct 5, 2026
Product data is assembled under deadline from whatever the factory, the designer, and the sample room produced. The fields needed to sell a garment are rarely the fields needed to make one, so the selling attributes get added last, or not at all.
The cost shows up later and somewhere else. A range with inconsistent colour names cannot be filtered by colour, a range with no composition fails retailer onboarding, and both are discovered while a buyer is waiting rather than while the data was being entered.
Enrichment used to mean a person working down a spreadsheet, which is why it was usually skipped. Models now do the first pass from images and supplier files, proposing category, colour, and attributes across a whole range at once.
It works as a draft rather than an authority. A model will categorise most of a catalogue correctly and be confidently wrong about the rest, so the useful setup is proposals a merchandiser accepts or corrects, not values written straight into the live catalogue.
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