datafletting

Datafletting is a term used to describe a data governance and analytics practice that focuses on curating data by selective extraction and enrichment from raw sources for downstream analysis. The term is not widely standardized and may be used variably, but most definitions view datafletting as a disciplined process that combines filtering with context-preserving augmentation to improve data quality and analytical utility.

Typical steps include data ingestion from source systems, application of filtering criteria to remove noise or identify relevant records, deidentification or pseudonymization to protect privacy, and enrichment through measured augmentation (such as adding metadata or derived features) where appropriate. Datafletting also emphasizes documentation, versioning, and reproducibility, with audit trails that record why and how data were selected or filtered.

In practice, datafletting is used in analytics pipelines, business intelligence, and research that require manageable, representative data subsets. It is especially common in streaming data, IoT, and social-media analytics, where raw data volumes are large and rapid filtering is required.

While datafletting can improve efficiency and privacy when done responsibly, it raises challenges related to bias, representativeness of filtered samples, and loss of potentially valuable information. Poorly defined filtering criteria can distort analyses, and insufficient documentation can hinder reproducibility. Ethical and legal considerations, such as data minimization, consent, and compliance with data protection regulations, are central to responsible practice.

See also: data cleaning, data filtering, data governance, privacy-preserving data mining, data sampling.