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Data Profiling

Data managementIntermediate Level

Data profiling is analyzing an existing dataset to see what it contains: how complete each field is, which values and formats occur, and where errors sit.

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What is Data Profiling?

Data profiling is the analysis of an existing dataset to see what it actually contains: how complete each field is, which values and formats occur, and where duplicates and outliers sit. For a product catalog, profiling (sometimes called product profiling) shows the state of your data before you clean, enrich or validate it. A profile answers questions like "how many products have no weight?" or "how many different spellings of navy are there?". It differs from data validation, which tests each record against fixed rules, and from data cleansing, which fixes the problems profiling reveals. Data profiling software and database profiling tools automate these counts across large tables. To get a quick first profile of your own catalog, try the free product data quality calculator.

Why Data Profiling matters for e-commerce

Most catalogs look fine product by product and reveal their problems only in aggregate: a quarter of products without a description, half without a category, a dozen ways of writing the same brand. Profiling makes those patterns visible, so you can decide what to fix first and estimate the work before a migration or enrichment project. Without a profile, teams guess, and they often spend time on fields that matter less than the gaps nobody counted.

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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 Profiling

  • 1Check: completeness, Field: Description, Finding: many products have no description at all
  • 2Check: value distribution, Field: Color, Finding: "Navy", "navy blue" and "Dark blue" used for the same color
  • 3Check: format, Field: GTIN, Finding: some codes stored with leading zeros removed
  • 4Check: duplicates, Field: Title, Finding: the same product imported twice under two SKUs
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How WISEPIM Helps

  • Analytics data quality: see completeness per field and category across your catalog.
  • Quality Guard rules: turn what profiling found into rules that flag the same issue on new products.
  • Attribute values page: spot spelling variants of the same value and clean them up.
  • Bulk editing and AI enrichment: fix the gaps a profile shows on many products at once.

Common mistakes with Data Profiling

  • Profiling once before a migration and never again, so new problems go unnoticed.
  • Looking only at completeness and ignoring formats, duplicates and value variants.
  • Profiling the whole database at once instead of per category, which hides category-specific gaps.
  • Collecting findings without deciding who fixes what.

Tips for Data Profiling

  • Profile per category, because required fields differ between a sofa and a sweater.
  • Start with the fields shoppers and channels use most: title, description, images, category and GTIN.
  • Repeat the profile after each big import to catch new issues early.
  • Read the data completeness guide to turn findings into targets.

Trends around Data Profiling

  • AI that explains profiling results in plain language and suggests fixes.
  • Continuous profiling on live data instead of one-off audits.
  • Profiling built into PIM and data quality dashboards rather than separate tools.
  • Profiling against the requirements of AI shopping assistants and marketplaces.

Tools for Data Profiling

  • WISEPIM: a PIM with data quality analytics and Quality Guard rules for product catalogs.
  • Talend Data Quality: data profiling software for databases and files.
  • Informatica Data Quality: enterprise profiling and cleansing tools.
  • OpenRefine: a free tool to explore and clean messy spreadsheets.
  • Great Expectations: an open source framework that profiles and tests data in pipelines.

Related Terms

Attribute CollisionCanonical Data ModelCircular Economy Data ModelCross-Border Trade (CBT) AttributesData ContractData Decay

Also Known As

Product profilingDatabase profilingData auditingData quality assessment

Frequently Asked Questions

Data profiling tools scan a dataset and report statistics per field, such as fill rate, distinct values, formats and duplicates. Examples range from free tools like OpenRefine to enterprise data quality suites and PIM dashboards for product data.

Typical examples are missing values, such as products without a description, inconsistent values, such as three spellings of one color, wrong formats, such as a GTIN without leading zeros, and duplicates. Profiling finds all four.

Profiling looks at the whole dataset to discover what is there and what is wrong. Validation checks each record against rules you already defined. You usually profile first and then write validation rules based on what you found.

In e-commerce, product profiling usually means profiling product data: measuring how complete and consistent your catalog is per field and category. The term is also used in retail for describing a product's target customer, which is a different practice.

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