Changes

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>

''For the emerging GC approach to defining and assessing data quality, see the [[GC Data Quality Framework]].''

''"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 '''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-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." <ref>DAMA-DMBOK2, 1.3.1 Data Quality, p.453</ref><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 [http://mitiq.mit.edu/Documents/Publications/TDQMpub/14_Beyond_Accuracy.pdf 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>

* '''Intrinsic DQ:'''

* '''Intrinsic data quality:'''

** Accuracy

** Accuracy

** Objectivity

** Objectivity

** Believability

** Believability

** Reputation

** Reputation

* '''Contextual DQ:'''

* '''Contextual data quality:'''

** Value-added

** Value-added

** Relevancy

** Relevancy

** Completeness

** Completeness

** Appropriate amount of data

** Appropriate amount of data

* '''Representational DQ:'''

* '''Representational data quality:'''

** Interpretability

** Interpretability

** Ease of understanding

** Ease of understanding

** Representational consistency

** Representational consistency

** Concise representation

** Concise representation

* '''Accessibility DQ:'''

* '''Accessibility data quality:'''

** Accessibility

** Accessibility

** Access Security<br><br>

** Access Security<br>

<references />

 

[https://www.informatica.com/ca/services-and-training/glossary-of-terms/data-quality-definition.html 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." <ref><nowiki>https://www.informatica.com/ca/services-and-training/glossary-of-terms/data-quality-definition.html</nowiki></ref><br></br>

 

// Poor '''''data quality''''' can cripple a business and its ability to make informed decisions. <br></br>