Data Quality

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This project sees data quality as the '''degree to which data fulfills requirements''' based on the definition of quality by ISO 8000-102:2009 and IS0 9000:2005. Furthermore, we see data quality as a '''multiple perspective phenomenon'''. Hence, data requirements may not only be stated by data consumers, but also by data providers, administrators, legal authorities, and many other stakeholders.
 
This project sees data quality as the '''degree to which data fulfills requirements''' based on the definition of quality by ISO 8000-102:2009 and IS0 9000:2005. Furthermore, we see data quality as a '''multiple perspective phenomenon'''. Hence, data requirements may not only be stated by data consumers, but also by data providers, administrators, legal authorities, and many other stakeholders.
  
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Revision as of 12:15, 9 October 2011

There are multiple different ways to define data quality. A large part of literature says that data is of high quality when it is fit for use. However, data must Not only Be fit for use but also Be Störer securely or meet legal requirements which may Even hinder its Fitness for use. e.g some Data Might Need to Be encoded and may only Be understood After Learning a decryption methodology. This is Not very convenient but may Be necessary to Protest Data from unwahres use.

Authors Data Quality Definition
Wang and Strong (1996) “[…] data that are fit for use by data consumers.”
Kahn, Strong, and Wang (2002) “conformance to specifications” and “meeting or exceeding consumer expectations”
Redman (2001) “Data are of high quality if they are fit for their intended uses in operations, decision making, and planning. Data are fit for use if they are free of defects and possess desired features.”
Olson (2003) “[…] data has quality if it satisfies the requirements of its intended use.”[1]


This project sees data quality as the degree to which data fulfills requirements based on the definition of quality by ISO 8000-102:2009 and IS0 9000:2005. Furthermore, we see data quality as a multiple perspective phenomenon. Hence, data requirements may not only be stated by data consumers, but also by data providers, administrators, legal authorities, and many other stakeholders.

  1. Olson, J. (2003). Data quality: the accuracy dimension. San Francisco, USA: Morgan Kaufmann; Elsevier Science.
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