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An Indicator Function for Insufficient Data Quality – A Contribution to Data Accuracy

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Knowledge and Technologies in Innovative Information Systems (MCIS 2012)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 129))

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Abstract

Owing to the fact that insufficient data quality usually leads to wrong decisions and high costs, managing data quality is a prerequisite for the successful execution of business and decision processes. An economics-driven management of data quality is in need of efficient measurement procedures, which allow for a predominantly automated identification of poor data quality. Against this background the paper investigates how metrics for the DQ dimensions completeness, validity, and currency can be aggregated to derive an indicator for accuracy. Therefore existing approaches to measure these dimensions are analyzed in order to make explicit, which metric addresses which aspect of data quality. Based on this analysis, an indicator function is designed returning a measure for accuracy on different levels of a data resource. The indicator function’s applicability is demonstrated using a customer database example.

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Author information

Authors and Affiliations

  1. FIM Research Center, University of Augsburg, Universitätsstr. 12, 86135, Augsburg, Germany

    Quirin Görz

  2. Senacor Technologies AG, Erika-Mann-Str. 55, 80636, Munich, Germany

    Marcus Kaiser

Authors
  1. Quirin Görz
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  2. Marcus Kaiser
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Editor information

Editors and Affiliations

  1. Dep. Sistemas de Informação, Universidade do Minho, Guimaraes, Portugal

    Hakikur Rahman  & Isabel Ramos  & 

  2. Instituto Politecnico do Porto, S. Mamede Infesta, Portugal

    Anabela Mesquita

  3. Dipt. di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

    Barbara Pernici

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© 2012 Springer-Verlag Berlin Heidelberg

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Görz, Q., Kaiser, M. (2012). An Indicator Function for Insufficient Data Quality – A Contribution to Data Accuracy. In: Rahman, H., Mesquita, A., Ramos, I., Pernici, B. (eds) Knowledge and Technologies in Innovative Information Systems. MCIS 2012. Lecture Notes in Business Information Processing, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33244-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-33244-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33243-2

  • Online ISBN: 978-3-642-33244-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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