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Data Cleansing

Data management and qualityIntermediate Level

Data cleansing is finding and fixing errors, duplicates, gaps and inconsistent formats in data. For ecommerce, it means cleaning product data before it reaches your channels.

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What is Data Cleansing?

Data cleansing (also called data cleaning) is finding and fixing errors in a dataset: wrong values, duplicates, empty fields and inconsistent formats. In ecommerce, product data cleansing means getting supplier files and old catalog data into a state you can publish, with one spelling per color, one unit per measurement and no duplicate SKUs. Cleansing fixes what is already wrong. Data validation stops new errors at the door, and data enrichment adds what is missing, such as descriptions or attributes. Most teams combine all three: clean the backlog once, then add validation rules so it stays clean. Ecommerce data cleansing services do the first pass by hand or with scripts, while a PIM keeps it clean after that. Get a first view of your catalog with the free product data quality calculator.

Why Data Cleansing matters for e-commerce

Dirty product data shows up in front of customers. Filters miss products because one says Navy and another says navy blue, feeds get rejected because a GTIN has a typo, and shoppers return items because a size was wrong. Akeneo's 2025 Consumer Returns Report found that about 60% of shoppers had returned a product due to inaccurate or misleading descriptions. The problem grows with every supplier file and every new channel. Cleaning once before a migration or channel launch is common, but without rules the same errors come back with the next import.

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Enter a title, description, meta fields and attributes. You get an overall score out of 100, a score per category and the first fixes to make, using your industry's preset.

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Examples of Data Cleansing

  • 1Field: Color, Before: navy, Navy Blue, NAVY, dk blue, After: Navy
  • 2Field: Weight, Before: 1kg, 1000 g, 1.0 KG, After: 1000 g
  • 3Field: SKU, Before: two records with SKU TS-104, After: one record, duplicate flagged for review
  • 4Field: Title, Before: NIKE AIR MAX 90 WIT MT 42!!!, After: Nike Air Max 90, White, Size 42
  • 5Field: GTIN, Before: 871234567890 (check digit wrong), After: flagged and corrected from the supplier file
  • 6Field: Description, Before: supplier HTML with broken tags, After: clean text with the same content
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How WISEPIM Helps

  • Rules that keep data clean: Quality Guard rules flag empty fields, wrong formats and invalid values after every import.
  • Fix in bulk: bulk editing and the Attribute values page let you rename and standardize values across thousands of products.
  • Safe imports: Rollback Import undoes an import that brought in bad data, and Operation history undo covers bulk changes on paid plans.

Common mistakes with Data Cleansing

  • Cleaning data once and adding no rules, so errors return with the next import.
  • Deleting suspected duplicates without checking which record is correct.
  • Fixing values in the webshop instead of at the source, so the next sync overwrites them.
  • Standardizing values without agreeing on one list of allowed values first.
  • Starting with the whole catalog instead of the products that sell most.

Tips for Data Cleansing

  • Profile your data first: count empty fields, distinct values and duplicates per attribute.
  • Agree on one allowed list per attribute, such as colors and units, before you clean.
  • Clean at the source system, not in each channel.
  • Keep a backup or export of the data before every large cleanup.
  • Turn every recurring error into a rule that catches it on the next import.

Trends around Data Cleansing

  • AI suggests standard values and spots likely errors, with a person confirming the fix.
  • Cleansing moves from one-off projects to rules that run on every import.
  • Teams measure data quality with a score so they can see whether cleanup work pays off.
  • Marketplaces and AI shopping assistants punish inconsistent attributes, which raises the bar for clean data.
  • Supplier onboarding includes a data check before files enter the catalog.

Tools for Data Cleansing

  • WISEPIM: PIM with Quality Guard rules, bulk editing and Attribute values for cleaning and standardizing product data.
  • OpenRefine: free open-source tool for cleaning and transforming messy datasets.
  • Akeneo PIM: PIM with completeness checks and data governance features.
  • Talend Data Quality: data profiling and cleansing for larger data teams.
  • Excel or Google Sheets: fine for small one-off cleanups with formulas and filters.

Related Terms

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

Also Known As

Data cleaningData scrubbingProduct data cleanup

Frequently Asked Questions

It is fixing errors in your product catalog: inconsistent values, wrong units, duplicate SKUs, broken text and invalid identifiers. The goal is product data that filters, feeds and marketplaces can use without rejections.

A service makes sense for a large one-time backlog, such as before a migration. For ongoing quality you need your own rules and tools, because new supplier files bring new errors every week. Many teams use a service once and a PIM afterward.

Data cleansing fixes errors that are already in your data. Data validation checks new data against rules and stops errors before they get in. You need cleansing to clear the backlog and validation to keep it clear.

Do a full cleanup before big changes, such as a migration or a new channel. After that, check on every import with rules, and review the quality score of your catalog every month.

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