Glossary D
Deutsch: Datenqualität / Español: Calidad de los Datos / Português: Qualidade dos Dados / Français: Qualité des Données / Italiano: Qualità dei Dati
Data Quality in the industrial context refers to the accuracy, completeness, consistency, reliability, and timeliness of data used in industrial processes, decision-making, and operations. High data quality is essential for optimising production, improving efficiency, ensuring regulatory compliance, and enabling data-driven decisions. Poor data quality can lead to incorrect conclusions, operational disruptions, safety risks, and financial losses, making it a critical component of modern industrial operations, especially in sectors like manufacturing, energy, and logistics.
Deutsch: Staubkontrolle / Español: Control de Polvo / Português: Controle de Poeira / Français: Contrôle de la Poussière / Italiano: Controllo della Polvere
Dust Control in the industrial context refers to the methods and technologies used to manage and reduce the release of airborne dust particles generated during industrial operations. Effective dust control is crucial for maintaining workplace safety, protecting worker health, ensuring regulatory compliance, and preventing environmental contamination. Industries such as mining, construction, manufacturing, and agriculture generate significant amounts of dust, which can pose serious risks if not properly controlled.
Deutsch: Datenerfassung / Español: Recolección de Datos / Português: Coleta de Dados / Français: Collecte de Données / Italiano: Raccolta Dati
Data collection in the industrial context refers to the systematic gathering of information from various sources within industrial operations, such as sensors, machines, processes, and personnel, to monitor performance, optimise production, ensure quality, and make informed decisions. This data can include metrics on production efficiency, equipment status, energy consumption, product quality, and more, forming the foundation for analysis, reporting, and continuous improvement.
Deutsch: Datensicherheit / Español: Seguridad de Datos / Português: Segurança de Dados / Français: Sécurité des Données / Italiano: Sicurezza dei Dati
Data security in the industrial context refers to the practices, technologies, and measures implemented to protect sensitive information and data assets from unauthorised access, theft, corruption, or disruption. This includes safeguarding both digital and physical data related to industrial operations, such as intellectual property, customer information, production data, and proprietary technologies.
Deutsch: Digitale Transformation / Español: Transformación Digital / Português: Transformação Digital / Français: Transformation Numérique / Italiano: Trasformazione Digitale
Digital transformation in the industrial context refers to the integration of digital technologies into all areas of an industry, fundamentally changing how businesses operate and deliver value to customers. This transformation involves adopting technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, cloud computing, and automation to improve efficiency, enhance productivity, and foster innovation.
Deutsch: Datenmanagement / Español: Gestión de datos / Português: Gerenciamento de dados / Français: Gestion des données / Italiano: Gestione dei dati
Data Management in the industrial context refers to the comprehensive process of collecting, storing, organising, protecting, and analysing data generated by industrial operations. This includes data from manufacturing processes, machinery, supply chains, logistics, and other operational activities. Effective data management enables companies to optimise production, improve decision-making, enhance product quality, and drive innovation. It is a critical component of modern industrial operations, especially with the rise of Industry 4.0, where data is a key asset for achieving operational excellence.
Deutsch: Datenanalyse / Español: Análisis de datos / Português: Análise de dados / Français: Analyse des données / Italiano: Analisi dei dati
Data Analytics in the industrial context refers to the process of examining, cleaning, transforming, and interpreting large sets of data to derive meaningful insights that can improve decision-making, optimise operations, and enhance overall efficiency in industrial settings. It involves the use of statistical tools, algorithms, and software to analyse data collected from various sources, such as machinery, sensors, and business processes, with the goal of identifying patterns, predicting outcomes, and making data-driven decisions.
Deutsch: Dänemark / Español: Dinamarca / Português: Dinamarca / Français: Danemark / Italiano: Danimarca
Denmark is a significant player in the industrial context, known for its advanced manufacturing sector, strong focus on sustainability, and innovation-driven economy. The country has built a reputation for being at the forefront of green technology, pharmaceuticals, maritime industry, and advanced machinery, making it a key industrial hub in Europe.