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Towards Corporate Data Quality Management


  • Ana Lucas

    () (National Laboratory for Civil Engineering and ISEG School of Economics and Management)


Today, it is a well known fact that poor quality data is costing large amounts of money to corporations all over the world. Despite the increasing research on methods, concepts, and tools for data quality (DQ) assessment and improvement, little has been done about corporate DQ management. The purpose of this research is to understand the nature and complexity of corporate DQ management, through various perspectives. These include the various kind of sponsorship, type and level of collaboration between business and IS/IT, organizational position of the DQ management team, scope of the DQ initiatives, roles, services provided, DQ methodologies, techniques and tools in use, etc. This paper presents, analyzes and discusses a single pilot exploratory case study, undertaken in a fixed and mobile telecommunications company in a European Union Country. The purpose of this case study is to check the validity of some initial propositions, and eventually find new ones, to be used in a subsequent multiple-case study, in order to provide an in-depth understanding of the corporate DQ management phenomenon. Classification- JEL:

Suggested Citation

  • Ana Lucas, 2010. "Towards Corporate Data Quality Management," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 173-196.
  • Handle: RePEc:pjm:journl:v:xv:y:2010:i:2:p:173-196

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    References listed on IDEAS

    1. Sing What Tee & Paul L. Bowen & Peta Doyle & Fiona H. Rohde, 2007. "Factors influencing organizations to improve data quality in their information systems," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 47(2), pages 335-355, June.
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    Cited by:

    1. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.

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    data quality management; case study;


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