A chef preparing a gourmet meal might use a thermometer to check the temperature of the meat. A mechanic working on a car might use a dipstick to check the engine's oil level. For data teams, how do we measure data quality?
Data quality is more important than ever, but as technology evolves, **the way we talk about data quality is struggling to keep up**.
Historically many data quality indicators have been adopted, like Accuracy, Validity, Completeness, Consistency, Reliability, Timeliness, Uniqueness, Accessibility, Confidentiality, Relevance, Integrity, … etc.
However, there is ***no standardization*** of their names or descriptions.
A comprehensive survey of over 60 quality dimensions was conducted by DAMA NL Foundations and published in [DDQ-Research-2020](https://www.dama-nl.org/wp-content/uploads/2020/09/DDQ-Dimensions-of-Data-Quality-Research-Paper-version-1.2-d.d.-3-Sept-2020.pdf) as an attempt to move towards more standardization. Among the many dimensions, a small subset of the most critical ones emerged.
These are referred to as the primary or critical dimensions. They are:
**Completeness, Validity, Accuracy, Consistency, Uniqueness, and Timeliness**.
This chapter focuses on these six widely used dimensions and their measurements, which we refer to as data quality indicators (DQI).
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