A central platform for managing, organizing, and assigning rights for digital media (images, videos, documents). It automates processes such as approvals and conversions for consistent brand communication.

A visual interface for clearly displaying and managing data and processes. It bundles information from different sources and enables quick decisions through interactive elements.

The consolidation of information from different sources in a central system. The goal is to bundle data sets for a more comprehensive view in analyses, reports, or downstream processing.

The structured consolidation and harmonization of scattered data from different sources into a consistent overall picture. It creates the basis for reliable analyses and reports.

A central environment for collecting, linking, and evaluating large volumes of data from systems such as CRM or ERP. It provides insights for optimizing strategies and processes.

The structured capture and organization of information from diverse sources (e.g., interfaces, sensors, inputs). It is the basic prerequisite for all downstream analyses and data-driven processes.

The ability to connect systems and data sources seamlessly via interfaces or APIs and exchange data reliably. It breaks down data silos and forms the technical foundation for integrated real-time data flows.

The state in which data remains free of contradictions, correct, and up to date across all connected systems, formats, and points in time. It ensures that identical information has the same value everywhere and enables reliable analyses.

Data consolidation refers to the merging, cleansing, and standardization of data into a central, consistent database.  This reduces redundancies, improves data quality, and enables uniform data use.

The holistic network of all data sources, systems, processes, and actors that jointly create, process, and exchange data. It provides the framework for efficient data flows and collaboration along the entire value chain.

The targeted addition of supplementary information (e.g., images, attributes, metadata) to existing data sets. This increases completeness and usability for analysis and personalization.

The structured transfer of data from a system into a defined target format (e.g., XML, JSON, CSV) for external downstream processing or provision to partners.

The targeted extraction of structured or unstructured data from sources such as databases or websites. It is the first step in the ETL process for later analyses and downstream processing.

A tool for reducing data volumes based on defined criteria (e.g., attributes, time periods). It hides irrelevant information and simplifies analysis and presentation.

The systematic creation of new data, manually, automatically, or synthetically (e.g., for simulations, software tests, or AI training) when real data is unavailable.

The standardizing preparation of different data sources into a consistent structure. It cleans up differences in formats and naming for smooth system integrations.

Data harmonization refers to the standardization of differently structured or formatted data into a consistent schema. This makes data comparable, analyzable, and reliably usable across systems.

The full set of technical systems, processes, and interfaces used to collect, store, and provide data as the foundation for data-driven decisions.

The structured connection and standardization of data from different source systems (e.g., PIM, ERP, CRM) into a central “single source of truth”. It prevents redundancies and ensures high data quality along the entire value chain.

The full set of rules, structures, and dependencies according to which information is organized and processed within systems. It defines validation criteria and logical links to ensure reliable results in business processes.

The targeted planning, management, and provision of data flows at the right time, in the right place, and in the required format. It supports the entire data lifecycle from collection to distribution and prevents silo structures.

The logical linking and assignment of data fields from different source and target systems. It serves as a structured translation to bridge format differences seamlessly during migrations or ETL processes.

The structured, loss-free transfer of data between systems, formats, or storage locations. The process uses ETL logic (extraction, transformation, loading) and validation to ensure data integrity and compatibility in the target system.

The structured representation of information through entities, attributes, and their relationships. A granular model breaks down data into the smallest units in order to control complex details precisely and deliver them flexibly across different channels.

Data protection, General Data Protection Regulation (GDPR): The protection of personal data against unauthorized processing, misuse, and loss in order to safeguard privacy. Legal requirements such as the GDPR demand principles such as purpose limitation, data minimization, and strict access controls.

The targeted cleansing, enrichment, and structuring of raw data to increase data quality and utility. It turns simple data into valuable information for analyses, automation, or personalization.

A highly secure digital storage space for the controlled exchange of confidential information between partners or project teams. It offers GDPR-compliant access rights and complete activity logs for secure collaboration or audits.

A cloud service for the automated generation of up-to-date, CI-compliant product PDFs directly from PIM or ERP systems, globally scalable and without local software.

Data source (seamless integration of different data sources); (synchronized data source): The point of origin or storage location of data. Seamless integration and synchronization of different sources (e.g., PIM, ERP, CRM) via interfaces creates a consistent “single source of truth” with data that is always up to date.

Uniform specifications for the structure, format, and meaning of data (e.g., FAB-DIS) that ensure error-free exchange between different systems and partners. They increase data quality and simplify process automation.

Software for the automated distribution of centrally maintained product data to different platforms, shops, and marketplaces. It ensures up-to-date, correctly formatted content with minimal effort.

The targeted conversion of data into another format or a new structure to ensure system compatibility. It includes steps such as cleansing, enrichment, and aggregation within ETL and integration processes.

The systematic checking of data for accuracy, completeness, and rule compliance during input or before import. It prevents data errors and guarantees high data quality in downstream systems.

The continuous, automated monitoring of systems and key metrics using AI and algorithms. It is used to identify patterns, risks, and deviations at an early stage.

Data first places structured data at the beginning of all processes, enabling automation and a short time to market. Data last integrates data only late in the process shortly before publication, which leads to media discontinuity and additional manual work.

The automated creation of publications (e.g., catalogs, price lists) based on structured data. Content is transferred from databases directly into predefined, CI-compliant layouts using rules.

The systematic identification, analysis, and correction of errors in program code. Tools such as logs or breakpoints are used to ensure the stability and correct functioning of software.

A subfield of machine learning based on deep artificial neural networks. It independently recognizes complex patterns in large data volumes and is used mainly in speech, image, and text processing.

An AI-supported translation service that uses deep learning models to translate texts into other languages with a high degree of contextual accuracy and precision.

The provision and transition of developed software or AI models from the test environment to a productive live system (local, cloud, or mobile) for reliable use.

Structured evaluation rounds with stakeholders to review concepts, drafts, or system architectures. They serve to identify weaknesses early, safeguard quality, and make well-founded decisions for next steps.

A software or hardware solution that supports users with tasks, decisions, or processes. Through process automation and data provision (e.g., via AI or dashboards), it increases efficiency and reduces workload.

A structured digital data set that bundles all essential product information across the entire lifecycle, from manufacturing to recycling. It creates transparency, meets EU regulations, and supports sustainable decisions and the circular economy.

Data-driven software for the automated creation and delivery of content across digital and analog channels. It connects structured data (e.g., from PIM or DAM) with layouts for media-neutral, CI-compliant publications at the push of a button.

A data-supported approach for continuously analyzing and optimizing a company’s product presentation on online sales platforms. By evaluating metrics such as visibility, content quality, and prices, it systematically increases conversion in e-commerce.

The role responsible for strategically managing and optimizing digital product presentation across all online channels. Tasks include content maintenance and the use of analytics to increase visibility and sales.

All digital points of contact along the customer journey where customers interact with a brand (e.g., website, shop, social media, app). They shape brand perception and significantly influence purchasing decisions.

Digital transformation strategy; digital transformation (of product communication): The structured transition of a company toward digital processes and business models. In product communication specifically, it replaces analog silos with data-driven, automated processes (e.g., via PIM/PXM) for consistent delivery across all channels.

A business model in which manufacturers sell their products directly to end customers without intermediaries (e.g., through their own webshops). It enables a direct customer relationship and full control over brand presentation and data.

The structured initial phase before a project starts, in which requirements, problems, and potential are systematically identified through analyses or workshops. It serves as a sound basis for decision-making before implementation.

User-friendly applications that allow people without professional design skills to create visual content independently (e.g., for social media or print). They offer intuitive drag-and-drop functions, templates, and AI support.

Software for the central storage, versioning, access control, and audit-proof archiving of digital documents. It makes information available quickly, supports compliance with legal requirements, and optimizes collaboration.

Defined specifications for automatically checking and ensuring data quality (e.g., in terms of completeness, format, or consistency). They identify data errors early and secure a reliable foundation for data-driven processes.

An operating method for visual interaction in which content (e.g., modules or media) is selected, moved, and placed using a mouse or touch. It significantly simplifies the handling of layout tools and systems.

Automatically personalized marketing messages that adapt to target groups, user behavior, or context based on rules and data. The process dynamically varies advertising materials to increase relevance, efficiency, and conversions across channels.

Adaptable networks of companies, platforms, and technologies that use synergies together through open interfaces and collaborative processes. They enable flexible responses to market changes and drive innovation.

The automated, data-based creation and target-group-specific delivery of marketing and publication content in real time. Using templates and rules, layouts for print and digital are generated dynamically and in personalized form.