datagap
Datagap refers to the absence, incompleteness, or inaccessibility of data needed to support analysis, decision making, or evidence generation. The term is used across fields such as data science, research, business analytics, and public policy. Datagaps can be structural, meaning they reflect missing areas of coverage or granularity, or quality-related, arising from errors, inconsistencies, or privacy constraints.
Common types include coverage gaps (missing populations or variables), timeliness gaps (data lag), granularity gaps (data
Datagaps can bias results, reduce statistical power, hinder reproducibility, and lead to misguided decisions or policies.
Identification and assessment usually involve data inventories, data catalogs, gap analyses, and data lineage tracing. Metadata,
Mitigation strategies include improving data governance, harmonizing standards, expanding data collection, enabling data sharing through agreements,