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Data Transformation

Data management1/5/2026Intermediate Level

Data transformation is the process of converting data from one format or structure into another, often necessary for data integration and syndication.

What is Data Transformation? (Definition)

Data transformation is the process of changing information from its original form into a format that fits a specific purpose. It helps businesses organize raw data so other systems can read and use it correctly. This process often involves several key tasks: * Cleaning data to remove errors or duplicates. * Mapping fields to match data from one system to another. * Enriching information by adding missing details. * Converting formats such as changing currency or date styles. Companies use data transformation when they combine information from different suppliers or prepare product lists for marketplaces. Each platform has its own rules for how data must look. Transformation ensures the data meets those requirements before it goes live. Tools like WISEPIM automate these changes to save time and prevent manual entry errors.

Why Data Transformation is Important for E-commerce

Data transformation is the process of changing product information from one format into another. In e-commerce, your data usually starts in internal systems like an ERP or PIM. However, every marketplace and webshop has its own specific rules for how information must appear. Data transformation adjusts your data to meet these requirements automatically. Without this process, you would need to manually edit product details for every platform. This often leads to mistakes, inconsistent listings, and slow product launches. WISEPIM handles these changes by mapping your data to the correct fields for each channel. This ensures your product information stays accurate and professional across all your sales platforms.

Examples of Data Transformation

  • 1A PIM system changes product measurements from centimeters to inches for international buyers.
  • 2You connect an internal label like 'color_code' to a marketplace label like 'variant_color_name' to ensure the data fits.
  • 3The software groups multiple image links into one single field to create a clean product feed.
  • 4The system turns internal codes like '0' into readable words like 'In Stock' for different online stores.

How WISEPIM Helps

  • Flexible Data Export: WISEPIM converts your product data into the exact format each sales channel needs. This makes it easy to send information to different webshops and marketplaces.
  • Automated Mapping Rules: Use automated rules to change product details and structures for specific platforms. These rules handle the technical work so your data always fits the destination.
  • Improved Data Compatibility: This process makes sure your product information works across all your different software systems. It removes technical barriers so data moves smoothly between internal and external platforms.

Common Mistakes with Data Transformation

  • Not setting clear data rules for each sales channel. This leads to messy or wrong results.
  • Changing data by hand instead of using automation. Manual work is slow and leads to human errors as your business grows.
  • Forgetting to clean data before you transform it. If the original information is wrong, the new version will be wrong too.
  • Not keeping a record of changes to your transformation rules. This makes it hard to fix errors or go back to an older setup.
  • Making transformation rules more complex than they need to be. Unnecessary steps slow down the system and make it harder to manage.

Tips for Data Transformation

  • Create clear rules for who owns the data and what quality standards it must meet before you start changing it.
  • Check your data for errors before you transform it. This step helps you find and fix mistakes early so your information stays accurate.
  • Use a PIM system to keep all your product data in one place. This makes it much easier to change and format data for different sales channels later.
  • Write down every rule you use to change your data. Keeping these records helps your team stay consistent and makes it easier to update the process later.
  • Start with the most important data changes first. You can add more complex rules as your business grows or as new sales channels require different formats.

Trends Surrounding Data Transformation

  • AI-powered data mapping and enrichment: AI algorithms automate the identification of data relationships and suggest enrichment opportunities, reducing manual effort.
  • Automated data pipelines: Increased use of automated workflows for data ingestion, transformation, and distribution, minimizing human intervention and accelerating time-to-market.
  • Real-time data transformation: Demand for immediate data availability drives solutions that transform data as it arrives, supporting real-time analytics and dynamic content updates.
  • Headless PIM and API-first approaches: Data transformation becomes critical for feeding diverse front-ends and applications via APIs, requiring flexible and scalable transformation layers.
  • Sustainability data integration: Transforming environmental impact data from various sources into standardized formats for reporting, compliance, and consumer transparency.

Tools for Data Transformation

  • WISEPIM: A comprehensive PIM solution offering robust data transformation and syndication capabilities for seamless multi-channel publishing.
  • Akeneo PIM: Provides advanced data enrichment and transformation features to tailor product information for specific e-commerce platforms and marketplaces.
  • Salsify: A Product Experience Management (PXM) platform with powerful tools for data transformation, governance, and syndication across various sales channels.
  • Informatica PowerCenter: An enterprise-grade ETL (Extract, Transform, Load) tool designed for complex data integration and transformation projects.
  • Talend Data Integration: Offers open-source and commercial solutions for building data pipelines, including extensive functionalities for data transformation and quality.

Related Terms

Also Known As

data mappingdata cleansingdata restructuring