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Databehandling, or data processing, refers to the collection, organization, storage, transformation, analysis, and dissemination of data to support operations, decision making, and knowledge creation. It encompasses activities that move data from raw inputs to usable information.

It covers processes across the data lifecycle, including data capture, storage, cleansing, integration, analysis, reporting, sharing, archiving, and deletion. Databehandling involves people, processes, and technologies such as databases, data pipelines, analytics tools, and machine learning models, and it often requires coordination across departments to ensure consistency and accessibility.

Key concepts in databehandling include data governance, data quality, metadata management, and data security. Core principles include accuracy, integrity, confidentiality, and availability. Data minimization aims to collect only what is necessary, while data stewardship and clear ownership help coordinate responsibilities. Interoperability and standardization facilitate data exchange between systems.

Legal and regulatory context is central to databehandling when personal data are involved. In many jurisdictions, organizations acting as data controllers or processors must comply with laws that address lawful bases for processing, purpose limitation, data subject rights, security measures, breach notification, and accountability. Contracts such as data processing agreements define roles and responsibilities and establish controls for data handling, retention, and deletion.

Applications of databehandling span business, science, and government, enabling reporting, decision support, operational automation, and research. Common challenges include protecting privacy and security, ensuring data quality, breaking down data silos, and managing governance at scale. Emerging practices emphasize data lineage, auditability, transparency, and privacy-preserving technologies to balance utility with protection.