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Data Quality Score

Data management and qualityIntermediate Level

A data quality score is a number, often 0 to 100, that shows how complete, correct and consistent a product record or catalog is against the rules you set.

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What is Data Quality Score?

A data quality score is a number, usually 0 to 100, that shows how well a product record or a whole catalog meets your data rules. It is calculated by checking each product against criteria such as completeness of required fields, valid formats, consistent units and the presence of images and descriptions, then weighting the results. A data quality scorecard is the report that shows those scores broken down by dimension, category, channel or supplier, so you can see where to fix first. A product data completeness score is one part of it: it only measures how many required fields are filled, not whether the values are right. The score is only as useful as the rules behind it, so base them on what your channels actually require. You can get a quick score for your own catalog with our free product data quality calculator.

Why Data Quality Score matters for e-commerce

A single number makes data quality visible and lets you track it over time, per category or per supplier. It turns a vague feeling that the data is messy into a ranked list of what to fix. At import, product data is often weak. In WISEPIM's State of Product Data 2026 report, the median catalog scored 56.6 out of 100 before enrichment, with thin descriptions and many products missing images or a category. A score per product shows where enrichment has the most effect.

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 Score

  • 1Dimension: Completeness, Check: title, description, brand, GTIN, image and category filled, Score impact: each missing required field lowers the score.
  • 2Dimension: Validity, Check: GTIN has a valid check digit, weight is a number with a unit, Score impact: invalid values count as missing.
  • 3Dimension: Consistency, Check: color values come from one list (Navy, not navy blue and dark blue), Score impact: off-list values flagged.
  • 4Dimension: Channel readiness, Check: product meets Google Shopping requirements, Score impact: separate score per channel.
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How WISEPIM Helps

  • Rules you control: Quality Guard rules define what a complete, valid product looks like and flag every product that falls short.
  • Per channel: Channel Readiness shows which products meet each channel's requirements and which fields block them.
  • Fix at scale: AI enrichment fills missing descriptions, attributes and specs, and bulk editing corrects values across many products.

Common mistakes with Data Quality Score

  • Scoring only completeness, so a filled but wrong value counts as good.
  • Using one score for every channel when requirements differ.
  • Weighting all fields equally, so a missing GTIN counts the same as a missing secondary color.
  • Tracking the score without assigning anyone to fix the lowest products.

Tips for Data Quality Score

  • Base required fields on what your main channels demand.
  • Weight fields by impact: identifiers, title, image and price first.
  • Show the score per category so owners can act on their own part.
  • Fix the lowest-scoring products with the most traffic first.

Trends around Data Quality Score

  • Scores per channel instead of one global number.
  • Readiness for AI shopping assistants as a new scoring dimension.
  • Scores checked automatically at import, before products reach a channel.
  • Supplier scorecards that compare incoming data quality per vendor.

Tools for Data Quality Score

  • Spreadsheet checks: count empty cells per column for a first rough score.
  • Data quality tools: profile datasets and report on validity and duplicates.
  • Merchant Center and marketplace diagnostics: show item-level issues per channel.
  • WISEPIM: Quality Guard rules, Channel Readiness and a free product data quality calculator.

Related Terms

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

Also Known As

Data quality scorecardProduct data quality scoreCompleteness scoreData quality rating

Frequently Asked Questions

A data quality scorecard is a report that shows quality scores broken down by dimension (completeness, validity, consistency), category, channel or supplier. It tells you where the problems are and who should fix them.

Each product is checked against a set of rules, such as required fields filled and values in the right format. The passed checks are weighted by importance and turned into a number, often 0 to 100. A catalog score is the average or median of the product scores.

It measures how many required fields a product has filled in. It is one part of a data quality score; it does not check whether the values are correct or consistent.

They are the rules your products must meet: required fields per category, valid identifiers like GTIN, consistent units and value lists, and minimum image and description requirements. Channel requirements, such as Google Shopping's, are a good starting point.

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