dqr

DQR commonly stands for Data Quality Report, a document produced within data governance programs to assess and communicate the quality of data assets. It serves as a formal artifact that aggregates metrics, findings, and actions related to data quality for stakeholders across the organization.

Purpose and scope: A DQR provides a structured view of data quality across domains, systems, and time periods. It helps stakeholders understand whether data is fit for its intended uses, identify quality issues, and prioritize remediation efforts.

Contents: Typical elements include data quality dimensions (such as accuracy, completeness, timeliness, consistency, validity, and uniqueness), profiling results, data quality rules, detected exceptions, data lineage, proposed remediation actions, data owners, and the sufficiency of data for reporting and decision-making.

Process and use: DQRs are generated from data profiling and quality checks, and are refreshed on a regular schedule or after data migrations and major changes. They are used by data stewards, compliance teams, and executive sponsors to monitor quality KPIs, inform governance decisions, and drive improvement initiatives.

Standards and frameworks: DQR practices often align with data governance frameworks like DAMA-DMBOK and data quality standards such as ISO 8000. They may be embedded in data governance dashboards and integrated with broader data management tooling.

Other uses: The acronym DQR is not unique to one concept and may refer to other terms or organizational projects depending on context. When encountered, the intended meaning should be clarified by domain or organization to avoid ambiguity.

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