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Introduction to Information Quality

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Data and Information Quality

Part of the book series: Data-Centric Systems and Applications ((DCSA))

Abstract

The Search query “data quality” entered into Google returns about three million pages, and searching similarly for the term “information quality” (IQ) returns about one and a half million pages, both frequencies showing the increasing importance of data and information quality. The goal of this chapter is to show and discuss the perspectives that make data and information (D&I) quality an issue worth being investigated and understood. We first (Sect. 1.2) highlight the relevance of information quality in everyday life and some of the main related initiatives in the public and private domains. Then, in Sect. 1.3, we show the multidimensional nature of information quality by means of several examples.

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Notes

  1. 1.

    Wikipedia definition available at http://www.en.wikipedia.org/wiki/Cloud_computing#Architecture.

References

  1. Berti-Equille L, Batini C, Srivastava D (eds) (2005) Exploiting Relationships for Object Consolidation. ACM, New York

    Google Scholar 

  2. Bouzeghoub M, Peralta V (2004) A framework for analysis of data freshness. In: Proceedings of the International Workshop on Information Quality in Information Systems, Paris, June 18th 2004

    Google Scholar 

  3. Crosby P (1979) Quality Is Free. McGraw-Hill, New York

    Google Scholar 

  4. Dasu T, Johnson T (2003) Exploratory Data Mining and Data Cleaning. J. Wiley Series in Probability and Statistics. Wiley, New York

    Book  MATH  Google Scholar 

  5. Data Warehousing Institute (2005) Data Quality and the Bottom Line: Achieving Business Success Through a Commitment to High Quality Data. http://www.dw-institute.com/

  6. Davis GB, Olson MH (1984) Management Information Systems: Conceptual Foundations, Structure, and Development, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  7. Davis R, Strobe H, Szolovits P (1993) What is knowledge representation. AI Magazine 14(1):17–33

    Google Scholar 

  8. De Michelis G, Dubois E, Jarke M, Matthes F, Mylopoulos J, Papazoglou MP, Schmidt J, Woo C, Yu E (1997) Cooperative information systems: a manifesto. In: Papazoglou M, Schlageter G (eds) Cooperative Information Systems: Trends & Directions. Academic, London

    Google Scholar 

  9. Dejaeger K, Hamers B, Poelmans J, Baesens B (2010) A novel approach to the evaluation and improvement of data quality in the financial sector. In: Proceedings of the 15th International Conference on Information Quality

    Google Scholar 

  10. European Parliament (2003) Directive 2003/98/EC of the European Parliament and of the Council of 17 November 2003 on the Re-use of Public Sector Information. Official Journal of the European Union

    Google Scholar 

  11. European Parliament (2013) Revision of the directive 2003/98/ec of the European parliament and of the council on the re-use of public sector information

    Google Scholar 

  12. Flemming, Annika (accessed 2014) Basel Committee on Banking Supervision, http://www.ots.treas.gov

  13. Floridi L (2005) Semantic conceptions of information

    Google Scholar 

  14. Han J, Kamber M (2000) Data Mining: Concepts and Techniques. Morgan Kaufmann, Los Altos

    MATH  Google Scholar 

  15. Härle P, Heuser M, Pfetsch S, Poppensieker T (2010) Basel iii. What the draft proposals might mean for european banking. Online verfügbar unter http://wwwmckinseycom/clientservice/Financial_Servicvices/Knowledge_Highlights/~{}/media/Reports/Financial_Services/MoCIB10_Basel3 ashx, zuletzt geprüft am 30:2011

    Google Scholar 

  16. International Organization for Standardization (accessed 2014) http://www.iso.org

  17. ISO-25012 (2008) ISO/IEC 25012:2008 software engineering – software product quality requirements and evaluation (SQuaRE) – data quality model

    Google Scholar 

  18. ISO (2000) Quality Management and Quality Assurance. Vocabulary. ISO 84021994. International Organization for Standardization, 1994

    Google Scholar 

  19. Juran J (1988) Juran on Planning for Quality. The Free Press, New York

    Google Scholar 

  20. Lenzerini M (2002) Data integration: a theoretical perspective. In: Proceedings of the PODS 2002, pp 233–246

    Google Scholar 

  21. Lohningen H (1999) Teach Me Data Analysis. Springer, New York

    Google Scholar 

  22. Missier P, Lack G, Verykios V, Grillo F, Lorusso T, Angeletti P (2003) Improving data quality in practice: a case study in the Italian public administration. Parallel and Distributed Databases 13(2):135–160

    Article  MATH  Google Scholar 

  23. Nebel B, Lakemeyer G (eds) (1994) Foundations of Knowledge Representation and Reasoning. Lecture Notes in Artificial Intelligence Edition. Springer, New York, vol 810

    Google Scholar 

  24. Office of Management and Budget (2002) Information Quality Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by Agencies. http://www.whitehouse.gov/omb/fedreg/reproducible.html

  25. ORACLE (accessed 2014) http://www.oracle.com/solutions/business-intelligence

  26. Ozsu T, Valduriez P (2000) Principles of Distributed Database Systems. Springer Science & Business Media, New York

    Google Scholar 

  27. Shankaranarayan G, Wang R, Ziad M (2000) Modeling the manufacture of an information product with IP-MAP. In: Proceedings of the 5th International Conference on Information Quality (ICIQ’00), Boston

    Google Scholar 

  28. Unit EI (2011) Big data: Harnessing a game-changing asset. A report from the economist intelligence unit sponsored by sas

    Google Scholar 

  29. US National Institute of Health (NIH) (accessed 2014) http://www.pubmedcentral.nih.gov/

  30. Wang RY, Lee YL, Pipino L, Strong DM (1998) Manage your information as a product. Sloan Management Review 39(4):95–105

    Google Scholar 

  31. Wang RY, Chettayar K, Dravis F, Funk J, Katz-Haas R, Lee C, Lee Y, Xian X, S B (2005) Exemplifying business opportunities for improving data quality from corporate household research. In: Wang RY, Pierce EM, Madnick SE, Fisher CW (eds) Advances in Management Information Systems - Information Quality (AMIS-IQ) Monograph, Sharpe ME

    Google Scholar 

  32. Wayne S (1983) Quality control circle and company wide quality control. Quality Progress 14–17

    Google Scholar 

  33. White C (2005) Data Integration: Using ETL, EAI, and EII Tools to Create an Integrated Enterprise, http://ibm.ascential.com

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Batini, C., Scannapieco, M. (2016). Introduction to Information Quality. In: Data and Information Quality. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-24106-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-24106-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24104-3

  • Online ISBN: 978-3-319-24106-7

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