Datahenting

Datahenting is the automated collection of data from multiple sources, typically performed by software that gathers, aggregates, and stores information for analysis or other uses. The term combines data with henting, meaning fetching or retrieving, and is used in various languages to describe data collection practices conducted at scale. Datahenting can involve web scraping, API-based data access, log ingestion, and the use of cookies, device identifiers, or sensors to capture behavioral, transactional, or contextual information. Data sources include websites, mobile apps, social networks, databases, public records, and sensor networks.

Datahenting yields data of differing sensitivity and granularity, ranging from anonymized or aggregated statistics to personal data such as identifiers, locations, and preferences. Use cases include analytics and business intelligence, market research, targeted advertising, fraud detection, risk assessment, and product optimization.

Ethical and legal considerations are central to datahenting. Many jurisdictions require transparency about what data is collected and for what purposes, along with consent or other lawful bases for processing. Regulations such as the European Union's General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and corresponding national laws govern collection, storage, cross-border transfer, and user rights. Data governance practices—data minimization, purpose limitation, and security controls—are commonly applied to mitigate privacy risks. Techniques like anonymization, pseudonymization, and differential privacy are used to reduce identifiability, though re-identification remains a concern in some contexts.

While datahenting can underpin legitimate aims such as research and optimization, it also raises ethical and legal questions about surveillance, consent, and ownership of information. Clear purposes, robust governance, and respect for user privacy are essential components of responsible data collection.