Datasupported

Datasupported is a term used to describe processes, decisions, or analyses that are explicitly supported by verifiable data. The concept emphasizes traceability of data sources, the methods used to analyze them, and the presentation of results with their uncertainties, rather than relying solely on expert opinion or anecdotal evidence.

Distinction from data-driven: while data-driven decisions rely on data to guide choices, datasupported focuses on documenting and validating the data and methods, ensuring reproducibility and auditability.

Key components include data provenance, data quality controls, methodological transparency, statistical validation, and clear reporting of uncertainty. Implementations often involve data governance, reproducible workflows, and dashboards that expose data lineage and assumptions.

Applications span business analytics, public policy, science, and journalism. In practice, a datasupported approach may accompany decisions with artifacts such as data dictionaries, model validation reports, and sensitivity analyses.

Benefits include increased trust, accountability, and the potential for reproducibility. It helps reduce bias by exposing data limitations and enabling external review. Challenges include ensuring data privacy, managing data quality across sources, and maintaining access to data and tools.

Critics argue that data quality and model limitations can still mislead, and that overreliance on data may overlook contextual or qualitative factors. A mature datasupported practice requires governance, ethical considerations, and continual improvement.

See also data-driven decision making; evidence-based practice; data provenance.