Standardising Product Data Across European and Asian Markets in a PLM System
A fashion brand selling across Europe and Asia had product attributes described differently in every market. One shared attribute dictionary, validation rules at PLM entry and a data quality dashboard made the same product mean the same thing in every market’s reporting.
PLM Product Data Challenge Across European and Asian Markets
Each market had grown its own way of describing products: colour written four ways, season codes that disagreed on when a season starts, and compositions typed as free text.
As a result, consolidated reporting was rebuilt by hand every month, and nobody trusted a category total enough to act on it quickly.
- Several local attribute conventions inside one PLM system
- About three days of manual data cleaning before each monthly report
- Category totals that differed between markets and head office
What Was Done to Standardise the PLM Product Data
Product Attribute Audit in Every Market
Every field mapped, counted and scored for completeness and conflicts.
One Product Attribute Dictionary Agreed with All Markets
Colour, season, composition and category defined once, with translations.
Migration Rules and PLM Entry Validation
Historic values remapped; new products stopped at entry if key fields were missing.
Product Data Quality Dashboard and Monthly Review
Completeness by attribute and market, with a named owner for each gap.
PLM Product Data Standardisation Results in Numbers
attribute completeness, from 71% at project start
time spent fixing product data before reporting
attribute dictionary in place of several local conventions
of monthly manual cleaning removed from the reporting cycle
Tools Used in the PLM Product Data Standardisation Project
PLM Attribute and Validation Configuration
Attribute lists, mandatory fields and market permissions.
Excel and Power Query for Attribute Migration
Repeatable remapping of historic attribute values.
Power BI Product Data Quality Dashboard
Completeness by attribute, market and season, reviewed monthly.
Related Service: Product Data Quality and PLM Standardisation
The same method, adapted to your systems: attribute audit, dictionary, migration rules, validation at entry and a quality dashboard your team reviews.
The dictionary was the easy part — agreeing it was the project.
Related Retail Analytics Articles
PIM vs PLM for Fashion Brands: What Each System Does
PLM manages a garment from design to production; PIM prepares its data for selling. What each system holds, who owns it and when you need both.
Product Attribute Standards for Fashion PLM That Work Across Markets
Which product attributes a fashion brand needs in PLM, and how a shared dictionary keeps colour, season and composition consistent in every market.
PLM Product Data Standardisation Case Study FAQ
The project took nine weeks. Most of that time went into the attribute audit and into agreeing one dictionary with every market; migration rules and validation followed once the definitions were signed off.
An export of every product attribute from the PLM system for each market, including colour, season, composition and category values. The monthly reporting files showed where the differences caused problems. Historic values were needed to build the migration rules.
New products are now checked at entry, so missing or non-standard attributes are stopped before they reach reporting. The manual cleaning before each monthly report was removed, and a data quality dashboard with named owners is reviewed every month.
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