data quality

Data quality is a perception or an assessment of data's fitness to serve its purpose in a given context. 

Aspects of data quality include: 

  • Accuracy
  • Completeness
  • Update status
  • Relevance
  • Consistency across data sources
  • Reliability
  • Appropriate presentation
  • Accessibility

Within an organization, acceptable data quality is crucial to operational and transactional processes and to the reliability of business analytics (BA) / business intelligence (BI) reporting. Data quality is affected by the way data is entered, stored and managed. Data quality assurance (DQA) is the process of verifying the reliability and effectiveness of data. 

Maintaining data quality requires going through the data periodically and scrubbing it. Typically this involves updating it, standardizing it, and de-duplicating records to create a single view of the data, even even if it is stored in multiple disparate systems. There are many vendor applications on the market to make this job easier.

 

See also: data life cycle management (DLM), data governancedata management, data protection management (DPM)

 

 

This was last updated in November 2005
Posted by: Margaret Rouse
View the next item in this Essential Guide: data scientist or view the full guide: Building an effective data governance framework

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