Dataleggers

Dataleggers is a contemporary term used in some online communities to describe individuals who systematically collect, curate, and analyze large-scale data sets to support investigative reporting, research, or policy analysis. The term is not widely standardized and does not appear in major dictionaries or formal data science texts; usage tends to be informal and context-dependent.

In practice, dataleggers may design data collection strategies, source data from public records, APIs, or web scraping, and apply statistical or machine learning methods to derive insights. They emphasize reproducibility, data provenance, and ethical considerations, and they may work within teams that include data engineers, analysts, and researchers.

Applications include journalism data investigations, government transparency projects, corporate analytics, or academic research. Dataleggers are often engaged with privacy, consent, and harm-minimization considerations, and many adopt open data principles and documentation to allow others to validate results.

Criticism and challenges include the term’s vagueness, potential overlap with unethical data collection, and the risk of conflating legitimate data work with illicit activity. Legal and policy constraints on data access, scraping, and use can also limit what dataleggers can do, and practitioners are expected to navigate these constraints responsibly.

See also: data science, data journalism, open data, data ethics.