Difference between revisions of "Data Quality"
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The '''DAMA-DMBOK2''' defines Data Quality (DQ) as ''“the planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meet the needs of data consumers.”''<ref>DAMA-DMBOK2, Figure 91 Context Diagram: Data Quality, p.451</ref><br><br> | The '''DAMA-DMBOK2''' defines Data Quality (DQ) as ''“the planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meet the needs of data consumers.”''<ref>DAMA-DMBOK2, Figure 91 Context Diagram: Data Quality, p.451</ref><br><br> | ||
| − | The term data quality refers both to the characteristics associated with high quality data and to the processes used to measure or improve the quality of data.<ref>DAMA-DMBOK2, 1.3.1 Data Quality, p.453</ref><br><br> | + | ''"The term data quality refers both to the characteristics associated with high quality data and to the processes used to measure or improve the quality of data."''<ref>DAMA-DMBOK2, 1.3.1 Data Quality, p.453</ref><br><br> |
The '''Strong-Wang''' framework (1996)<ref><nowiki>http://mitiq.mit.edu/Documents/Publications/TDQMpub/14_Beyond_Accuracy.pdf</nowiki></ref> focuses on data consumers' perceptions of data. It describes 15 dimensions across four general categories of data quality:<ref>DAMA-DMBOK2, 1.3.3. Data Quality Dimensions, p.455</ref> | The '''Strong-Wang''' framework (1996)<ref><nowiki>http://mitiq.mit.edu/Documents/Publications/TDQMpub/14_Beyond_Accuracy.pdf</nowiki></ref> focuses on data consumers' perceptions of data. It describes 15 dimensions across four general categories of data quality:<ref>DAMA-DMBOK2, 1.3.3. Data Quality Dimensions, p.455</ref> | ||
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** Accessibility | ** Accessibility | ||
** Access Security<br><br> | ** Access Security<br><br> | ||
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Revision as of 10:25, 27 April 2020
The DAMA-DMBOK2 defines Data Quality (DQ) as “the planning, implementation, and control of activities that apply quality management techniques to data, in order to assure it is fit for consumption and meet the needs of data consumers.”[1]
"The term data quality refers both to the characteristics associated with high quality data and to the processes used to measure or improve the quality of data."[2]
The Strong-Wang framework (1996)[3] focuses on data consumers' perceptions of data. It describes 15 dimensions across four general categories of data quality:[4]
- Intrinsic DQ:
- Accuracy
- Objectivity
- Believability
- Reputation
- Contextual DQ:
- Value-added
- Relevancy
- Completeness
- Appropriate amount of data
- Representational DQ:
- Interpretability
- Ease of understanding
- Representational consistency
- Concise representation
- Accessibility DQ:
- Accessibility
- Access Security
