RealWeltDaten

RealweltDaten is a term used to describe data gathered from real-world sources and activities, as opposed to data produced in experiments or generated synthetically. It includes structured records, such as electronic health records or transactional logs, and unstructured material, such as text, images, or sensor streams. RealweltDaten can be collected in real time or retrospectively and may span multiple domains including health, finance, mobility, and consumer behavior.

Sources include IoT sensors, wearable devices, administrative databases, clinical records, survey responses, mobile apps, and public data from government and commercial providers. Data may be primary, collected directly for a purpose, or secondary, repurposed from existing datasets.

RealweltDaten are widely used to support real-world evidence, healthcare and regulatory decision making, and AI and machine learning model training and evaluation. They also enable analyses in urban planning, market analytics, risk assessment, and post-market surveillance. The real-world context they provide can improve external validity and applicability of findings compared to controlled trials or synthetic datasets.

Quality and challenges include biases, missing values, noise, and measurement errors. Representativeness and coverage gaps can affect validity. Provenance, data lineage, and governance are important to document collection methods, consent, and transformations. Data standardization and interoperability remain major concerns, with models and schemas evolving across domains.

Ethics and regulation emphasize privacy, consent, and data minimization. Anonymization or pseudonymization, access controls, and data-sharing agreements help mitigate risks. Compliance with laws such as GDPR or sectoral regulations is essential when handling personal information, and responsible data stewardship is increasingly emphasized in governance frameworks.