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Data Quality Management (DQM) involves strategies and methods for monitoring and improving data quality. The goal is to optimize decision-making processes and increase efficiency within organizations.
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Data quality management uses rules, measurements, and improvement cycles to keep data fit for its purpose over time.
The approach applies quality-management methods to data as a resource. DAMA-DMBOK and ISO 8000 connect accountability, quality requirements, measurement, and correction in a continuous management cycle.
It works like a maintenance loop: define requirements, measure data, prioritize deviations, fix causes, and check again.
A quality level defined for a specific use.
Collecting indicators about data quality.
An action that fixes a deviation and its cause.
Data quality management makes quality work repeatable and planable. It needs business priorities and accountable owners; a metric alone does not improve data.
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