vermissing

Vermissing is a term used in data management and information governance to describe the process or state of verifying and recording the absence of data in a data collection. It is used to distinguish different causes of missing values, such as nonresponse, data collection error, or intentional redaction, from mere data absence by coincidence. A vermissing event is typically logged so that analysts can understand why data are unavailable and how the missingness may affect analysis.

Origin and status: Vermissing is a neologism that has circulated mainly in informal data science and governance discussions in the 2020s. It does not have a formal standard or universal definition, and its interpretation can vary by organization. In practice, it is often used to emphasize the verification and documentation aspects of missing data rather than treating missingness as a purely incidental nuisance.

What vermissing records include: A vermissing entry usually contains metadata such as the timestamp of the observation, the field name, the stated reason for missingness, the method by which the absence was determined (manual check, automated flag, user nonresponse), the data source, and a confidence or quality indicator. This structure supports traceability and downstream handling, including appropriate imputation or data quality reviews.

Applications and relation to other concepts: Vermissing supports transparency in data governance, bias assessment, and informed imputation. It complements traditional missing data concepts (such as MCAR, MAR, MNAR) and data quality practices by providing a documented rationale for each missing value. Critics note the lack of standardization, urging alignment with existing data quality frameworks.