sourcemeasure

Sourcemeasure is a metric used in data governance and information science to quantify the reliability and provenance of data as it enters an analysis workflow. It reflects metadata about the data’s origin, collection method, and calibration, as well as indicators of timeliness and integrity, to produce a concise assessment that accompanies the data product. The term emphasizes trust in data sources as a prerequisite for analysis.

Purpose and scope: Sourcemeasure supports data integration, quality control, and risk assessment by signaling whether a dataset should be trusted, weighted differently, or filtered. It is used by data stewards, scientists, and engineers to document data provenance and to guide decisions about reuse. It can be applied across domains such as sensor networks, surveys, and laboratory measurements.

Calculation and components: A sourcemeasure typically aggregates several provenance indicators, including source identity, collection method, instrument calibration status, update frequency, known biases, data integrity checks, and tamper-evidence. Some implementations express the result as a score (for example, 0 to 1) and may also emit qualitative tags such as high, medium, or low trust. The score is often maintained with versioned metadata to reflect provenance changes.

Limitations and considerations: Standardization is uneven, and sourcemeasure depends on accurate metadata. Different organizations may use different scales or definitions, which can impede interoperability. Privacy, security, and auditability are important concerns, particularly when provenance data itself contains sensitive information. Users should treat sourcemeasure as one of several inputs, not a sole determinant of data quality.

See also: Data provenance, Data quality, Metadata, Data lineage, Trust metric.