Abstract
Data quality is personal/subjective and situational. The only way to measure the quality of data is to create a well-defined norm in the form of data quality requirements. These requirements may address different data quality dimensions such as completeness, accuracy, and timeliness. Several data quality dimensions are discussed in-depth based on detailed examples. When data is not conforming to the norm, then data quality management processes help to remedy the situation.
Cite
CITATION STYLE
van Gils, B. (2023). Data Quality. In Enterprise Engineering Series (Vol. Part F1214, pp. 167–173). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35539-4_17
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