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Analyzing and Improving Data Quality
Keywords:
data life cycleAbstract
Data quality is a research area strongly investigated during the 90’s. However, few companies in Argentina apply data quality methodologies or tools during the analysis, design or implementation phases of software development process. Developers generally use techniques to design systems such as UML without considering mechanisms for future data quality problems. In this work we propose a methodology in which the data quality is an essential part of the whole software development process. Early design decisions on data quality strongly impact on the system. Our methodology defines a set of practices to be applied on the software life cycle. In addition these practices act as a means to evaluate if systems already running fulfill with minimal data quality requirements.
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References
[1] D. Ballou and H. Pazer. Modeling data and process quality in multi-input, multi-output information systems. Management Science, 31(2):150–162, 1985.
[2] G. Brackstone. Managing data quality in a statistical agency. Survey Methodology, (25):139–179, 1999.
[3] E. M. Burns, O. MacDonald, and A. Champaneri. Data quality assesment methodology: A framework. In Joint Statistical Meetings - Section on Government Statistics, pages 334–337, 2000.
[4] L.Pipino, Y. W. Lee, and R. Y. Wang. Data quality assessment. Communications of the ACM, 45(4):211–218, 2002.
[5] K. Orr. Data quality and systems theory. Communications of the ACM, 41(2):66–71, February 1998.
[6] E. Pierce. Assesing data quality with control matrices. Communications of the ACM, 47(2):82–86, February 2004.
[7] T. Redman. Data Quality: The Field Guide. Digital Press, January 15 2001.
[8] G. Shankaranarayanan, R. Y. Wang, and M. Ziad. Ip-map: Representing the manufacture of an information product. MIT Conference on Information Quality, 2000.
[9] G. Tayi and D. Ballou. Examining data quality. Communications of the ACM, 41(2):54–57, February 1998.
[10] R. Y. Wang. A product perspective on total data quality managment. Communications of the ACM, 41(2):58–65, February 1998.
[2] G. Brackstone. Managing data quality in a statistical agency. Survey Methodology, (25):139–179, 1999.
[3] E. M. Burns, O. MacDonald, and A. Champaneri. Data quality assesment methodology: A framework. In Joint Statistical Meetings - Section on Government Statistics, pages 334–337, 2000.
[4] L.Pipino, Y. W. Lee, and R. Y. Wang. Data quality assessment. Communications of the ACM, 45(4):211–218, 2002.
[5] K. Orr. Data quality and systems theory. Communications of the ACM, 41(2):66–71, February 1998.
[6] E. Pierce. Assesing data quality with control matrices. Communications of the ACM, 47(2):82–86, February 2004.
[7] T. Redman. Data Quality: The Field Guide. Digital Press, January 15 2001.
[8] G. Shankaranarayanan, R. Y. Wang, and M. Ziad. Ip-map: Representing the manufacture of an information product. MIT Conference on Information Quality, 2000.
[9] G. Tayi and D. Ballou. Examining data quality. Communications of the ACM, 41(2):54–57, February 1998.
[10] R. Y. Wang. A product perspective on total data quality managment. Communications of the ACM, 41(2):58–65, February 1998.
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Published
2008-07-01
How to Cite
Buccella, A., Cechich, A., & Domingo, G. (2008). Analyzing and Improving Data Quality. Journal of Computer Science and Technology, 8(02), p. 57–63. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/742
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Original Articles
