E-commerce Dictionary

Access control in a PIM sets who can view, edit, import or publish which product data, usually through user roles and permissions.

Attribute dependency management means setting rules so one attribute's value decides which other attributes or values apply, such as frame size only for bikes.

Attribute inheritance in a PIM means child products, variants or families automatically take attribute values or fields from their parent, unless you override them.

Product attribute management is defining, filling and maintaining the structured fields that describe products, such as color, size and material, for every channel.

Attribute scoping sets whether a product attribute holds one value everywhere or a different value per language, channel or market.

An attribute set is a named group of product attributes, such as size, color and material, that defines which fields a type of product has.

Attribute mapping links the product attributes in one system to the matching fields and values in another, such as a sales channel or marketplace.

Bulk edit means changing a field on many products or records at once, such as price, category or description, instead of editing each one separately.

Product compliance data is the legal and safety information a product must show or carry to be sold, such as CE marking, warnings, ingredients and the responsible person.

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.

Data consolidation is combining data from several sources, such as supplier files, an ERP and webshops, into one cleaned and consistent dataset in a single system.

Data governance is the set of rules, roles and checks that decides who owns which data, how it is created and changed, and what quality it must meet.

Data ingestion is the process of collecting data from sources such as supplier files, ERPs and APIs and loading it into a central system for storage and use.

A centralized repository for storing large volumes of raw, unstructured, and semi-structured product data from various sources before it's processed or structured.

Data lineage is the record of where a piece of data came from, which steps changed it and where it went, from source to final destination.

Data mapping rules are instructions that link each field in a source, such as a supplier file, to a field in a target system and say how to convert the value on the way.

A data model defines what data a system stores and how it relates; a PIM data model structures products, variants, attributes and categories.

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.

Data quality monitoring is the ongoing checking of data against rules for completeness, accuracy and consistency, with alerts when quality drops.

A data quality rules engine checks every record against rules you define, such as required fields, allowed values and formats, and flags or blocks data that fails.

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.

The principle that data is subject to the laws of the country or region where it is collected, stored or processed.

A data steward is the person responsible for the quality of a set of data: defining the rules, checking the data and fixing errors at the source.

Data stewardship is the day-to-day care of data: named people, called data stewards, keep it accurate, complete and consistent and fix problems when they appear.

Data transformation converts data from one format or structure into another, such as supplier files into the attributes and values a PIM or marketplace needs.

A way to integrate systems by querying data where it lives through a virtual layer, instead of copying it into another database.

Data import is the process of bringing data from an outside source, such as a CSV file, spreadsheet, feed or another system, into the software you work in.

Data integrity means data stays accurate, complete and consistent throughout its life, without unintended changes as it moves between systems.

Data manipulation is changing data to make it more useful, such as cleaning, converting, combining and restructuring product data in bulk.

Data normalization is bringing values that mean the same thing into one consistent format, such as writing every size, unit, color and brand name the same way.

Data validation is the automatic check that product data is accurate, complete and correctly formatted before it is saved, imported or sent to a sales channel.

A central system that stores all your e-commerce product information, from titles and specs to images and prices, and feeds it to every sales channel.

ETIM is an open classification standard for technical products that assigns each product a class with fixed features, values and units, so suppliers and wholesalers describe products the same way.

GS1 Digital Link is the GS1 standard that puts a product's GTIN into a web address, so one QR code works at the checkout and opens product information on a phone.

A ghost SKU is a product record that still exists in a system after the product itself is gone, discontinued or was never real.

GDSN is GS1's network of certified data pools through which suppliers share standardized product master data with retailers once, and keep it in sync.

A golden record is the single, trusted version of a data entity, such as a product or customer, built by combining and cleaning data from several systems.

Import validation checks product data against rules during import, so wrong prices, missing fields and invalid codes are caught before they reach your channels.

A product knowledge graph stores products, attributes and their relationships as connected facts that search, recommendations and AI assistants can follow.

Master data management (MDM) is the discipline and software for keeping one trusted version of core business data, such as products, customers, suppliers and locations, across all systems.

A metadata layer holds the definitions, types, rules and relationships that describe your data, so people and systems know what each field means and how to use it.

Metadata management is defining, organizing and governing the data that describes your data, such as field definitions, units, value lists and ownership.

PIM data model extensibility is the ability to add new attributes, product types and relations to a PIM without changing its code or breaking existing data.

A product attribute is a named characteristic of a product with a value, such as color, size, material or weight, used to describe, filter and compare items.