Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
A New Precautionary Method for Measurement and Improvement the Data Quality

Authors: Seyed Mohammad Hossein Moossavizadeh, Mehran Mohsenzadeh, Nasrin Arshadi

Abstract:

the data quality is a kind of complex and unstructured concept, which is concerned by information systems managers. The reason of this attention is the high amount of Expenses for maintenance and cleaning of the inefficient data. Such a data more than its expenses of lack of quality, cause wrong statistics, analysis and decisions in organizations. Therefor the managers intend to improve the quality of their information systems' data. One of the basic subjects of quality improvement is the evaluation of the amount of it. In this paper, we present a precautionary method, which with its application the data of information systems would have a better quality. Our method would cover different dimensions of data quality; therefor it has necessary integrity. The presented method has tested on three dimensions of accuracy, value-added and believability and the results confirm the improvement and integrity of this method.

Keywords: Data quality, precaution, information system, measurement, improvement.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078675

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1139

References:


[1] J. Patrtabian, The data quality measuring using by data mining (MSc Thesis), Departmen t of computer, Science and Research branch, Islamic Azad University, Khouzestan, Iran, Winter 2009.
[2] S. M. T. Rohani Rankouhi, Foundamental concepts of DataBase, Jelveh Publications, First Edition, 2002.
[3] H. Seraj, A new framework for measuring of the data value-added, (MSc Thesis), Departmen t of computer, Science and Research branch, Islamic Azad University, Khouzestan, Iran, Summer 2010.
[4] Batini, C., Cappiello, C., Francalanci, C., and Maurino, A. 2009. Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41, 3, Article 16 (July 2009), 52 pages.
[5] Wang, R. 1998. A product perspective on Total Data Quality Management. Comm. ACM 41, 2.
[6] Juran, J. M.; Gryna, Jr, F. M., "Quality Planning and Analysis", 2nd ed., McGraw-Hill, New York, 1980.
[7] Luebbers, Dominik; Grimmer, Udo; Jarke, Matthias. "Systematic Development of Data Mining-Based Data Quality Tools". Proceedings of the 29th VLDB Conference, Berlin, Germany, 2003.
[8] Batini, C. and Scannapieco, M. 2006. Data Quality: Concepts, Methodologies and Techniques. Springer Verlag.
[9] Pipino, L., Lee, Y. and Wang, R. 2002. Data Quality Assessment. Commun. ACM 45, 4.
[10] Wang, R., Ziad, Mostapha; W. Lee, Yang. "Data Quality". Springer: Kluwer Academic Publishers, 2002.