Data Transformation

Definition

Data transformation is a crucial step in data management where raw data is converted into a suitable format or structure for its intended purpose. This process can involve various operations such as cleaning data, mapping fields, aggregating values, or enriching information. It is commonly applied when integrating data from disparate sources or preparing data for export to different systems or channels, where each destination may have unique requirements for data format and content.

Why It's Important for E-commerce

In e-commerce, data transformation is indispensable for multi-channel selling. Product data often originates from various internal systems (ERP, PIM, DAM) and needs to be adapted for numerous external platforms (e-commerce websites, marketplaces, comparison shopping engines), each with specific data schemas and content requirements. Without effective data transformation, product information would be inconsistent, incomplete, or incompatible, leading to errors, manual rework, and poor customer experiences.

Examples

• Converting product dimensions from centimeters to inches for an international marketplace.
• Mapping a PIM's internal attribute 'color_code' to a marketplace's 'variant_color_name' attribute.
• Aggregating multiple image URLs into a single structured field for a product feed.
• Transforming a product's 'availability' status from an internal code (e.g., '0' for in stock) to a channel-specific string (e.g., 'In Stock' or 'Available Now').

How WISEPIM Helps

• Flexible Data Export: Transform PIM data into any required format for various e-commerce channels and external systems.
• Automated Mapping Rules: Define complex transformation rules to automatically adapt attributes, values, and structures for specific endpoints.
• Enhanced Data Compatibility: Ensure seamless data flow between internal systems and external platforms by handling diverse data requirements efficiently.

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