Product Data Quality and PLM Data Standardisation for Fashion Brands
One attribute dictionary for every market, validation rules at entry and a quality dashboard, so the same product means the same thing in every report.
Product Data Problems This PLM Service Solves
Colour written four ways
Free text and local habits make one attribute impossible to filter or report.
Season codes that disagree
Markets define the start of a season differently, so totals never match.
Missing fields discovered too late
Products reach e-commerce or reporting with empty compositions and categories.
Manual cleaning before every report
Days of fixing data each month before anyone can trust a category total.
What the Product Data Standardisation Work Includes
Four workstreams, from audit to monthly review.
Audit of product attributes per market
Every field mapped, counted and scored for completeness and conflict.
Attribute dictionary agreed with all markets
Colour, season, composition and category defined once, with translations.
Migration rules and PLM entry validation
Historic data remapped; new products blocked at entry if key fields are missing.
Data quality dashboard and monthly review
Completeness per attribute and market, with a named owner for each gap.
Results Clients Get from Product Data Standardisation
placeholder figuresattribute completeness after standardisation
time spent fixing product data before reporting
of monthly manual cleaning removed
Tools Used in PLM Data Projects
PLM system configuration
Attribute lists, validation rules and market permissions.
Excel and Power Query for mapping
Remapping of historic attributes.
Power BI data quality dashboard
Completeness by attribute, market and season.
Related Case Study: Standardising Product Data Across European and Asian Markets in a PLM System
Standardising Product Data Across European and Asian Markets in a PLM System
Multi-market fashion brand. One attribute dictionary replaced many local conventions.
Recommendation from a Fashion Retail Colleague on Product Data Work
Placeholder — real LinkedIn recommendations will be added with permission“Placeholder recommendation text. Two or three sentences from a colleague on the product data project, quoted with permission once the real recommendation is in place.”
Product Data and PLM Standardisation FAQ
The method is system-independent. It has been applied in enterprise fashion PLM systems and in simpler product databases.
Usually three to five weeks, most of it spent on decisions with the markets rather than on the data itself.
Yes. Migration rules remap old values to the new dictionary, and exceptions are listed for manual review.
Discuss a Product Data Project with Tatsiana Bandziuk
Placeholder note: send me three attributes that cause the most trouble. I will reply with how I would standardise them.
Tatsiana