Data Quality
For the emerging GC approach to defining and assessing data quality, see the GC Data Quality Framework.
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 DAMA-BMBOK2 goes on to explain that 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 data quality:
- Accuracy
- Objectivity
- Believability
- Reputation
- Contextual data quality:
- Value-added
- Relevancy
- Completeness
- Appropriate amount of data
- Representational data quality:
- Interpretability
- Ease of understanding
- Representational consistency
- Concise representation
- Accessibility data quality:
- Accessibility
- Access Security
Informatica defines data quality as "The overall utility of a dataset as a function of its ability to be easily processed and analyzed for other users, usually by a database, data warehouse, or data analytics system." [5]
// Poor data quality can cripple a business and its ability to make informed decisions.
- ↑ DAMA-DMBOK2, Figure 91 Context Diagram: Data Quality, p.451
- ↑ DAMA-DMBOK2, 1.3.1 Data Quality, p.453
- ↑ http://mitiq.mit.edu/Documents/Publications/TDQMpub/14_Beyond_Accuracy.pdf
- ↑ DAMA-DMBOK2, 1.3.3. Data Quality Dimensions, p.455
- ↑ https://www.informatica.com/ca/services-and-training/glossary-of-terms/data-quality-definition.html
