Data integrity

What is Data integrity?

Data integrity means that data stays accurate, complete and consistent throughout its life, from the moment it is created or imported until it is used, changed or deleted, without unintended or unauthorized changes. In ecommerce it means the price, stock, GTIN or size a shopper sees is the same value your systems hold. Integrity rests on rules: required fields, valid formats, unique identifiers, correct links between records (such as a variant that always belongs to an existing product) and control over who may change what. Data integrity and data quality overlap but are not the same. Integrity is about data staying correct and unchanged as it moves between systems; quality is about whether the data is fit for its purpose, for example whether a description is complete and helpful. A product can keep perfect integrity and still have a poor description. The data validation guide shows which checks protect integrity.

Why Data integrity matters for e-commerce

Product data travels a long way: from supplier files into an ERP or PIM, then to a webshop, marketplaces and feeds. Each step is a chance for values to break. A decimal separator flips a price, an import cuts off a GTIN's leading zero, or a variant loses the link to its parent product. When integrity fails, the damage shows up quickly. Shoppers see wrong prices or sizes, marketplaces reject listings with invalid identifiers, and orders arrive for products that are no longer in stock. Each error costs support time and, in the worst case, trust. Protecting integrity is mostly about prevention: validating data at import, keeping one source of truth, controlling who can edit which fields and keeping a record of changes. Checks that catch an error before it is published are far cheaper than fixing it after customers have seen it.

Examples of Data integrity

• 1Field: GTIN, Integrity rule: 13 digits with a valid check digit, leading zeros kept, Risk if broken: Listing rejected by Google Shopping
• 2Field: Price, Integrity rule: Decimal number in EUR, same value in PIM and webshop, Risk if broken: Product sold at the wrong price
• 3Record link: Variant to parent product, Integrity rule: Every variant has an existing parent, Risk if broken: Orphan variants without content
• 4Field: SKU, Integrity rule: Unique across the catalog, Risk if broken: Stock and orders booked on the wrong product
• 5Process: Supplier import, Integrity rule: Validate types and units before saving, Risk if broken: Weights in grams stored as kilograms

Tips for Data integrity

• Choose one source of truth per field and let other systems read from it.
• Validate identifiers such as GTINs and SKUs at import, not after publishing.
• Store numbers with fixed units and decimal formats so systems never have to guess.
• Limit who can change critical fields like price, GTIN and SKU.
• Compare a sample of products between your PIM and each channel every month.

Trends around Data integrity

• Automated validation at every import and export instead of periodic manual checks.
• Stricter integrity requirements for compliance data, such as product safety and EU product passport fields.
• Monitoring that compares values between the PIM and each channel and reports differences.
• AI-assisted detection of outliers, such as a price or weight far outside the normal range for a category.

Tools for Data integrity

• PIM systems such as WISEPIM, Akeneo and Salsify, which validate product data and act as the single source for channels.
• Data quality platforms such as Informatica Data Quality, for profiling and monitoring data.
• Data integration tools such as Talend, which validate data as it moves between systems.
• Validators such as a GTIN check digit tool, for quick checks on single identifiers.

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