E-commerce Dictionary

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.

A data contract is an agreement between the producer and the consumer of data that defines its structure, allowed values, quality rules and ownership.

Data decay is the gradual loss of accuracy in stored data, such as product details that become outdated as prices, specs and suppliers change.

Product data deduplication finds and removes duplicate product records by matching on identifiers, names and attributes, so each product exists once.

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 mesh is a decentralized data architecture where business domains own and publish their data as products, following shared governance rules.

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

Data orchestration in e-commerce coordinates how data moves between ERP, suppliers, PIM and sales channels, in the right order and format.

Data productization is turning raw data into a documented, owned and quality-checked data product that other teams and systems can use directly.

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.

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.

Data residency is the physical or geographic location where an organization's data is stored, often chosen to meet laws, contracts or customer expectations.

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.

A data stewardship council is a cross-department group that sets and enforces the rules for a company's data, including who owns which product data.

Data syndication is sending product information from one central source to webshops, marketplaces and retailers, in the format each channel requires.

A data syndication platform is software that sends product data from one source to many sales channels and formats it to each channel's rules.

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 distribution in e-commerce is sending product data from one central source to every webshop, marketplace and feed you sell through.

Data feed management is creating, optimizing and maintaining the product feeds you send to shopping channels, marketplaces and comparison sites.

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.

Demand forecasting in ecommerce is estimating how many units of each product customers will buy in a coming period, so you can plan stock, purchasing and promotions.

Digital asset management (DAM) is software for storing, tagging and distributing product images, videos and documents from one central library.

Digital asset rendition automation is software that creates the sizes and formats each channel needs from one master image or video, without manual editing.

Digital asset renditions are alternate versions of one master file, such as a product photo resized, cropped or converted for a specific channel or device.

A digital catalog is an online, searchable version of a product catalog with images, specs, prices and links to buy, shared as a web page, viewer or app.

A Digital Commerce Platform (DCP) is the comprehensive technology stack that enables businesses to sell products and services online, encompassing various e-commerce functionalities.

Digital product asset management (DPAM) stores and links product photos, videos, 3D models and manuals to the right product, variant and channel.

Digital Product Experience (DPX) refers to the entire digital customer journey and the role of rich product information in shaping it across all touchpoints.

A digital product twin is a complete digital version of a physical product, with its data, specs, media and, in industry, live data from the item in use.

Technology and rules that control who may access, copy or use digital content such as images, videos, software and e-books.