Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Information Quality Assessment

  • Leo L. Pipino
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_496

Synonyms

Data; Information

Definition

This entry uses the terms data and information interchangeably. The classical distinction is that data are raw facts whereas information is data in context or data that have been processed. Nevertheless, other than at an abstract level, it is a distinction that is often not made and one finds that the terms are used interchangeably. It is important to note that one individual’s information can be data to another individual. This entry will also use the terms information quality dimension and information quality variable interchangeably.

Further, this chapter defines information of quality as information that is fit for use (or data of quality as data that is fit for use). This means that context and use plays an important role in evaluating information quality. For example, the instantaneous changes in a stock’s price may be of importance to the stock trader who may trade stocks on a minute by minute basis. This instantaneous information, however,...

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Recommended Reading

  1. 1.
    Ballou D, Pazer H. Modeling data and process quality in multi-input, multi-output information systems. Manag Sci. 1985;31(2):150–62.CrossRefGoogle Scholar
  2. 2.
    Ballou D, Wang R, Pazer H, Tayi G. Modeling information manufacturing systems to determine information product quality. Manag Sci. 1998;44(4):462–84.zbMATHCrossRefGoogle Scholar
  3. 3.
    Batini C, Scannapieco M. Data quality: concepts, methodologies and techniques. New York: Springer; 2006.zbMATHGoogle Scholar
  4. 4.
    Codd E. The relational model for database management: version 2. Reading: Addison-Wesley; 1990.zbMATHGoogle Scholar
  5. 5.
    Eden A, Shankaranarayanan G. Understanding impartial versus utility-driven quality assessment in large datasets. In: Proceedings of the 12th Conference on Information Quality; 2007. p. 265–79.Google Scholar
  6. 6.
    Huang K, Lee. Y, Wang R. Quality information and knowledge. Englewood Cliffs: Prentice-Hall; 1999.Google Scholar
  7. 7.
    Krantz D, Luce R, Suppes P, Tversky A. Foundations of measurement: additive and polynomial representation. New York: Academic; 1971.zbMATHGoogle Scholar
  8. 8.
    Lee YW, Pipino LL, Funk JD, Wang RY. Journey to data quality. Cambridge: MIT Press; 2006.Google Scholar
  9. 9.
    Pipino L, Lee Y, Wang R. Data quality assessment. Commun ACM. 2002;45(4):211–8.CrossRefGoogle Scholar
  10. 10.
    Redman T. Data quality: the field guide. Belford: Digital Press; 2001.Google Scholar
  11. 11.
    Strong D, Lee Y, Wang R. Data quality in context. Commun ACM. 1997;40(5):103–10.CrossRefGoogle Scholar
  12. 12.
    Wang R, Lee Y, Pipino L, Strong D. Manage your information as a product. Sloan Manag Rev. 1998;39(4):95–105.Google Scholar
  13. 13.
    Wang R, Strong D. Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst. 1996;12(4):5–34.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of MassachusettsLowellUSA

Section editors and affiliations

  • Yang W. Lee
    • 1
  1. 1.College of Business AdministrationNortheastern Univ.BostonUSA