Skip to main content
Back to E-commerce Dictionary

Data Transformation

Data management and qualityIntermediate Level

Data transformation converts data from one format or structure into another, such as supplier files into the attributes and values a PIM or marketplace needs.

Image by · CC BY 4.0

What is Data Transformation?

Data transformation is the process of converting data from one format, structure or set of values into another so a target system can use it. For product data, it means turning supplier files and ERP exports into the fields, units and values your PIM, webshop and marketplaces expect. Typical steps are mapping source columns to target attributes, converting units (centimeters to inches), standardizing values ("navy blue" and "Navy" become one value), splitting or combining fields, and shortening text to channel limits. Data normalization is one part of it: it only makes values consistent. Transformation is also the middle step of ETL, between extracting and loading. In a PIM, it happens twice: on import and again on export per channel. Automations can run those steps on a schedule.

Why Data Transformation matters for e-commerce

Every source and every channel uses its own structure. A supplier sends a color code, your webshop wants a color name, and Google Merchant Center wants a value from its own list. Without transformation, someone retypes or reformats the data by hand for each channel. Good transformation rules make imports repeatable and exports predictable. When a supplier sends a new file, the same mapping and conversions apply. When a marketplace changes a requirement, you change one rule instead of editing every product. Rules also protect data quality: values that do not fit show up as errors instead of being published.

Examples of Data Transformation

  • 1Source: length_cm = 120, Rule: convert to inches, Target: length = 47.2 in
  • 2Source: color_code = 04, Rule: look up in code table, Target: color = Navy
  • 3Source: title of 180 characters, Rule: shorten to channel limit, Target: Amazon title of at most 75 characters
  • 4Source: stock = 0, Rule: map to availability value, Target: availability = out_of_stock
Image by · CC BY 4.0

How WISEPIM Helps

  • Product import in WISEPIM maps CSV columns from suppliers to your attributes and can run as a scheduled import through automations.
  • Content Logic and Template Strings build channel values from existing fields, such as a title made of brand, model and color.
  • Pricing rules per channel and Optimize for Platform adjust prices and content to what each channel expects.

Common mistakes with Data Transformation

  • Writing rules per product instead of per attribute, so every new product needs manual work.
  • Transforming dirty data. Clean up duplicates and wrong values in the source first.
  • Converting units without keeping the original value, so you cannot convert again later.
  • Not documenting rules, so nobody knows why a value changed when a channel rejects it.
  • Overwriting source data with channel-specific versions instead of keeping both.

Tips for Data Transformation

  • Agree on one target value list per attribute before writing rules; see attribute standardization.
  • Keep the raw import file so you can rerun transformations.
  • Test rules on a small sample before running them on the full catalog.
  • Name rules after the channel and attribute they serve.
  • When a channel rejects products, fix the rule, not the single product.

Trends around Data Transformation

  • AI that suggests column-to-attribute mappings for new supplier files.
  • Channel requirements published as machine-readable specs that rules can follow.
  • Low-code rule builders that let product teams change mappings without developers.
  • Structured attributes prepared for AI shopping agents, not only for web pages.

Tools for Data Transformation

  • WISEPIM: a PIM with import mapping, Content Logic and channel exports.
  • Akeneo: a PIM with attribute mapping and export profiles.
  • Informatica PowerCenter: an enterprise ETL tool.
  • Talend: data integration and transformation pipelines.
  • Channable: feed rules that transform product data per channel.

Related Terms

Access Control (PIM)Attribute Dependency ManagementAttribute InheritanceAttribute ManagementAttribute mappingAttribute Scoping

Also Known As

Data conversionData mappingData restructuringETL transformation

Frequently Asked Questions

Product data transformation converts product information from a source format into the format a target system needs. Examples are mapping supplier columns to attributes, converting units and shortening titles for a marketplace. It usually happens on import into a PIM and again on export to each channel.

On import, the PIM maps columns from CSV files or connections to its attributes and cleans the values. On export, it builds each channel's version: different field names, value lists, title lengths or prices. Rules keep both steps repeatable.

Normalization is one type of transformation: it makes values consistent, such as one spelling for a color. Transformation also covers changing structure, mapping fields, converting units and creating new values from existing ones.

No. ETL stands for extract, transform, load, so transformation is the middle step. You can transform data without a full ETL pipeline, for example with mapping rules on a CSV import.

Still have questions?

Can't find the answer you're looking for? Please get in touch with our team.

Contact Support

Keep exploring

Hand-picked next steps to go deeper.