Abstract
According to Gartner, human data-entry errors, and lack of proper corporate data standards result in more than 25 percent of critical data used in large corporations to be flawed. While the issue of data quality is as old as data itself, it is now exposed at a much more strategic level, e.g. through business intelligence (BI) systems, increasing manifold the stakes involved. Corporations routinely operate and make strategic decisions based on remarkably inaccurate or incomplete data. This proves a leading reason for failure of high-profile and high-cost IT projects such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM) and others. According to an industry survey [1], the presence of data quality (DQ) problems costs U.S. business more than 600 billion dollars per annum.
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Data Warehousing institute. Data Quality and the Bottom Line: Archiving business success through a commitment to high quality data, http://www.dw-institute.com
Johnson, T., Dasu, P.: Data quality and data cleaning: An overview. SIGMOD tutorial (2003)
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© 2008 Springer-Verlag Berlin Heidelberg
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Sadiq, S., Zhou, X., Deng, K. (2008). Research and Practice in Data Quality. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds) Progress in WWW Research and Development. APWeb 2008. Lecture Notes in Computer Science, vol 4976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78849-2_6
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DOI: https://doi.org/10.1007/978-3-540-78849-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78848-5
Online ISBN: 978-3-540-78849-2
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