reaalidatan

Reaalidatan is a neologism used in information studies and digital epistemology to describe data that purports to reflect the real world with high fidelity. The term blends "real" and "data" and is used across academic, industry, and policy discussions to distinguish authentic-appearing data from synthetic or simulated data. There is no single formal definition, and usage varies by community.

In many usages, realiadatan refers to data with traceable provenance, documented collection methods, and verifiable alignment with observable phenomena. It emphasizes data integrity, reproducibility, and accountability for how the data were gathered, stored, and processed. Risks include biases, tampering, sampling errors, and overconfidence in claimed authenticity.

In practice, realiadatan is discussed in contexts such as artificial intelligence training, sensor networks, and digital twin environments, where provenance and quality controls influence trust and decision-making. In journalism and analytics, it is used to distinguish data that can be independently verified from data whose authenticity may be uncertain.

Critics note that the term is informal and context-dependent, and may conflate authenticity with data-quality controls. Some scholars prefer more precise terms like provenance, verifiability, or authenticity when describing data. See also data provenance, data quality, authenticity, digital twin, and epistemology of data.