The Five Most-Cited Data Quality Dimensions

Across data quality literature, five dimensions appear most frequently:

1. Accuracy: the degree to which data correctly represents the real-world entities it models
2. Completeness: the extent to which all required data is present
3. Consistency: the degree to which data does not contradict itself across the dataset or other sources
4. Timeliness (also: currency) — how current and up-to-date the data is for its intended use
5. Accessibility: the ease with which data can be accessed by authorized users

These five dimensions form a practical baseline for data quality assessment, though specific contexts may require additional dimensions (e.g., security, interpretability, relevance).

The consensus on which dimensions matter is stronger than the consensus on how to define them.

Related: 04-atom—data-quality-consensus-gap, 04-atom—fitness-for-use-definition