Data in fashion
fashion data: sell-through and size/color analytics, trend and demand sensing, returns, and supply-chain visibility.
Apparel and fashion generate distinctive data streams spanning SKU-level sales, size and fit, returns, seasonality, and supply chain traceability from fiber to finished garment. This block equips you to work with the datasets that actually drive decisions in the sector: POS and e-commerce transactions, PLM and inventory records, wholesale sell-through, and consumer behavior signals. You will learn to assess data quality across fragmented systems, apply governance suited to a global, multi-brand supply base, and interpret analytics benchmarks specific to merchandising and demand. You will also navigate privacy rules governing customer profiles and loyalty data, plus the practical checks and audits that keep fashion data trustworthy across markets and channels.
What you'll master
- Map and prioritize the key apparel data sources, from POS and e-commerce to PLM, wholesale sell-through, and supply chain traceability records
- Evaluate data quality and governance using sector-relevant metrics such as SKU completeness, size-attribute accuracy, and returns data integrity
- Apply analytics benchmarks for merchandising and demand, including sell-through rate, size curve accuracy, and forecast error by category
- Run practical privacy and governance checks on customer, loyalty, and supplier data across multiple markets and channels