Data Quality Measure

Context:
- It can range from being a Quantitative Data Quality Meausure to being a Qualitative Data Quality Measure.
- It can be measured by a[ Data Quality Assessment System (that supports a data quality analysis task).
- It can be defined by Data Governance System.
- It can be a Data Validity Measure.
- It can be a Data Auditability Measure.
- It can be a Data Availability Measure.
- It can be a Data Accessibility Measure.
- It can be a Data Consistency Measure.
- It can be a Data Reusability Measure.
- It can be a Data Security Measure.
- It can be a Data Timeliness Measure.
- It can be a Data Understandability Measure.
- It can be supported by a Data Verification Rule.
- It can be assessed by a Data Quality Monitoring System (against data quality specifications).
- …
Example(s):
- a Lexicon Quality Measure.
- a Clinical Trial Data Quality Measure.
- …
Counter-Example(s):
- a Task Performance Metric, Data Asset, Data Privacy Policy, Data Governance, Information Quality, Quantitative Data, Data Cleansing, Qualitative Data.

References

(Moses et al., 2022) ⇒ Barr Moses, Lior Gavish, and Molly Vorwerck. (2022). “Data Quality Fundamentals.” O'Reilly Media, Inc..

(Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/data_quality Retrieved:2018-11-28.
- Data quality refers to the condition of a set of values of qualitative or quantitative variables. There are many definitions of data quality but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning”. Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as data volume increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. Data cleansing may be required in order to ensure data quality.

https://talend.com/resources/what-is-data-quality/
- QUOTE: ... Data quality is the process of conditioning data to meet the specific needs of business users. Data quality initiatives are generally centered on improving data quality metrics so that data will promote optimal performance of business systems and support user faith in the systems' reliability.
  Data has quality if it satisfies the requirements of intended use.
  The narrow definition of data quality is that it's about data that is missing or incorrect. A broader definition is that data quality is achieved when a business uses data that is comprehensive, consistent, relevant and timely. …

(Herzog et al., 2007) ⇒ Thomas N. Herzog, Fritz J. Scheuren, and William E. Winkler. (2007). “Data Quality and Record Linkage Techniques.” Springer Science & Business Media,