dataforarbejdningsproces

Data processing, known in Danish as dataforarbejdning, refers to the collection, organization, transformation, storage, and analysis of data to produce information that supports decision making, reporting, and automated systems. It encompasses both manual and automated activities across data that originate from various sources.

Core activities include data collection, cleaning and normalization, integration from multiple sources, transformation (such as aggregation and feature extraction), and loading into storage and analytics systems. These steps are often implemented as ETL (extract, transform, load) or ELT (extract, load, transform) processes, depending on where transformations occur. Real-time or near-real-time processing may use streaming architectures, while batch processing runs at scheduled intervals.

Technologies and architectures commonly involved include databases, data warehouses, data lakes, and data platforms. Data pipelines, orchestration tools, and metadata management support end-to-end workflows. Cloud-based services and on-premises infrastructure are both used, often forming hybrid environments.

Data processing is governed by data governance, quality control, and privacy considerations. Organizations implement access controls, encryption, and auditing to protect data, and must comply with relevant regulations such as GDPR. Data provenance and lineage help explain how results were produced and support accountability.

Applications span business intelligence, analytics, machine learning, and operational systems. Key challenges include ensuring data quality and interoperability, eliminating silos, scaling pipelines, and balancing data utility with privacy and security.