Product Data Management
What is product data management?
Product Data Management (PDM) is the process of organizing, storing, and maintaining all product-related information in a centralized system. This includes attributes, descriptions, images, pricing, and metadata. In eCommerce, PDM ensures that product data is accurate, consistent, and easily accessible for teams across merchandising, marketing, and operations.
Product Data Management in Fashion eCommerce
In fashion eCommerce, product data management in fashion eCommerce helps retailers track, update, and standardize information for thousands of SKUs, like clothing, footwear, and accessories. Proper PDM ensures product descriptions, sizes, colors, and other attributes are correct across all channels. It also supports catalog updates, seasonal collections, and global expansion, making it easier to manage large inventories efficiently.
PIM vs PDM
While both PIM (Product Information Management) and PDM systems deal with product data, PIM focuses more on marketing and distributing product information across channels. PDM is broader, covering the complete lifecycle of product data, including internal tracking, versioning, and accuracy. Together, they help retailers maintain high-quality, consistent product data across systems.
Why Use PDM in eCommerce?
Implementing a PDM system helps eCommerce teams:
- Ensure accurate and consistent product data across all channels.
- Track product data changes and maintain version control.
- Reduce errors and returns caused by incorrect product information.
- Accelerate product launches and time-to-market.
- Manage large catalogs efficiently and scale operations as inventory grows.
Benefits of PDM System:
- Centralized product information for easier access and updates.
- Improved catalog accuracy and reduced operational errors.
- Better coordination between merchandising, content, and marketing teams.
- Enhanced customer experience through accurate product descriptions and attributes.
Supports data-driven decisions by tracking and analyzing product data performance.