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Data Quality

Data management and qualityBasic Level

Data quality is how accurate, complete, consistent and current your data is. In e-commerce it covers specs, attributes, images and values on every channel.

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What is Data Quality?

Data quality is how accurate, complete, consistent and current your data is for the job it has to do. In e-commerce that mostly means product data quality: whether every product has correct specs, the required attributes, good images and the same values on every channel. In a PIM, data quality is measured with rules per category and channel. Typical dimensions are accuracy (values are true), completeness (required fields are filled), consistency (same units and spelling everywhere), validity (formats such as GTINs and dimensions are correct) and timeliness (prices and stock are up to date). Data quality is the result you measure; product data governance is the set of rules, owners and checks that keeps that result stable over time. Product data quality standards for ecommerce usually start from the requirements of your channels, such as Google Shopping. Get a quick baseline with the product data quality calculator.

Why Data Quality matters for e-commerce

Shoppers decide from the product data in front of them. Salsify's 2025 consumer research found that 54% of shoppers dropped a purchase because of conflicting information. Marketplaces and Google Shopping also reject or hide listings with missing or invalid data. Most catalogs start from a low baseline. In our State of Product Data 2026 report, the median catalog scored 56.6 out of 100 at import, before enrichment. Measuring data quality per category shows where to start.

Free tool

Score one product's data quality

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 Quality

  • 1Dimension: Completeness, Field: Material, Problem: Empty for most jackets, Rule: Required in Outerwear
  • 2Dimension: Validity, Field: GTIN, Problem: Wrong check digit, Rule: GTIN check digit validation
  • 3Dimension: Consistency, Field: Color, Problem: "Navy", "navy blue" and "Dark blue" used for the same color, Rule: Fixed list of allowed values
  • 4Dimension: Accuracy, Field: Weight, Problem: Entered in grams in one channel and kilograms in another, Rule: One unit stored centrally
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How WISEPIM Helps

  • Write data quality rules per category and channel with Quality Guard in WISEPIM and see which products fail them.
  • Check whether products meet each channel's requirements with Channel Readiness.
  • Fill missing attributes, specs and descriptions with AI enrichment and review the results in the Review queue.
  • Standardize values in bulk, for example from the Attribute values page, and undo bulk changes with Operation history on paid plans.
  • Track quality over time in analytics and fix the weakest categories first.

Common mistakes with Data Quality

  • Running a one-off cleanup without rules that stop the same errors from coming back.
  • Using one standard for every category instead of required fields per category.
  • Allowing free text where a fixed list of values is needed, such as colors and sizes.
  • Fixing data in a channel instead of in the central source.
  • Not measuring quality, so nobody knows if it improves.

Tips for Data Quality

  • Start with a baseline: measure completeness and validity per category.
  • Define required attributes per category based on what your channels ask for.
  • Use fixed value lists for attributes such as color, size and material.
  • Validate data on import, not only before export.
  • Name an owner for each category who is responsible for its quality score.

Trends around Data Quality

  • AI that fills and corrects attributes, with human review for sensitive fields.
  • Product data prepared for AI shopping assistants that read structured attributes.
  • Quality scores per product that teams track like a sales metric.
  • Regulation such as the EU Digital Product Passport that requires more structured product data.

Tools for Data Quality

  • WISEPIM: PIM with Quality Guard rules, Channel Readiness and AI enrichment for product data quality.
  • Akeneo: PIM with completeness tracking per channel.
  • Google Merchant Center: Reports product data issues for Google Shopping.
  • OpenRefine: Free tool for cleaning and standardizing messy data.
  • Great Expectations: Open-source framework for data validation tests.

Related Terms

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

Also Known As

Product data qualityPIM data qualityProduct data quality governanceInformation quality

Frequently Asked Questions

Product data quality is how accurate, complete, consistent and current the information about your products is: titles, descriptions, attributes, identifiers, images, prices and stock. High product data quality means a shopper or marketplace can rely on every value without checking it elsewhere.

A standard lists the required fields per category, the allowed formats and units, the minimum description and image rules, and channel requirements such as a valid GTIN and a Google product category. Write it per category, because a shoe needs different attributes than a lamp, and turn each line into a rule your PIM can check.

A PIM keeps one central record per product, applies completeness and validation rules, and sends the same values to every channel. That replaces scattered spreadsheets where errors are copied and fixed in one place but not the others.

Product data quality is the measurable state of your data. Product data governance is the framework that keeps it there: standards, owners, checks and regular reviews. Governance without measurement is guesswork, and measurement without governance slips back over time.

Good product data answers a shopper's questions without extra contact: a clear title, a description with use and benefits, filled attributes for size, material and compatibility, a valid identifier, several images and correct price and stock. It is the same on every channel.

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