Using fuzzy AHP for evaluating the dimensions of data quality

The business work flow depends on the data quality which is applied in the organisation. Therefore, data quality has become increasingly important to many organisations such as semiconductor industry. Data quality depends strongly on organisation of the information system (IS) and how the data is processed. Measuring and improving data quality in an organisation is a complex task. The purpose of this paper is to provide a good insight into the use of fuzzy analytical hierarchy process (fuzzy AHP) to incorporate four aspect dimensions of data quality: 1) intrinsic; 2) accessibility; 3) contextual; 4) representational. Findings demonstrate that the intrinsic criteria and accessibility criteria are the preferred key decision dimensions of data quality in semiconductor industry. Fuzzy AHP as a decision-making analysis tool is used for handling uncertain and imprecise data.

[1]  Yong Shi,et al.  Problems and systematic solutions in data quality , 2009 .

[2]  Yu Cai,et al.  Managing data quality in inter-organisational data networks , 2007, Int. J. Inf. Qual..

[3]  Thomas Redman,et al.  The impact of poor data quality on the typical enterprise , 1998, CACM.

[4]  Ceyda Güngör Sen,et al.  Evaluation and pre-allocation of operators with multiple skills: A combined fuzzy AHP and max-min approach , 2010, Expert Syst. Appl..

[5]  Yueh-Hsiang Chen,et al.  Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP , 2008, Inf. Sci..

[6]  Giri Kumar Tayi,et al.  Examining data quality , 1998, CACM.

[7]  Shyh-Hwang Lee,et al.  Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university , 2010, Expert Syst. Appl..

[8]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[9]  Thomas Redman,et al.  Data quality for the information age , 1996 .

[10]  Ken Orr,et al.  Data quality and systems theory , 1998, CACM.

[11]  Da Ruan,et al.  A fuzzy multi-criteria decision approach for software development strategy selection , 2004, Int. J. Gen. Syst..

[12]  Ching-Hsue Cheng.  Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function , 1997 .

[13]  Larry P. English.  Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits , 1999 .

[14]  Mahammad Haghighi,et al.  The impact of 3D e-readiness on e-banking development in Iran: A fuzzy AHP analysis , 2010, Expert Syst. Appl..

[15]  R. K. Mittal,et al.  Data mining research for customer relationship management systems: a framework and analysis , 2008, Int. J. Bus. Inf. Syst..

[16]  Cengiz Kahraman,et al.  Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey , 2004 .

[17]  Cengiz Kahraman,et al.  Project risk evaluation using a fuzzy analytic hierarchy process: An application to information technology projects , 2006, Int. J. Intell. Syst..

[18]  Kenyon Ww.  Analysis of the collection cycle. , 1993 .

[19]  Yu Jing,et al.  A discussion on Extent Analysis Method and applications of fuzzy AHP , 1999, Eur. J. Oper. Res..

[20]  C K Kwong,et al.  Determining the Importance Weights for the Customer Requirements in QFD Using a Fuzzy AHP with an Extent Analysis Approach , 2003 .

[21]  F. Chan,et al.  Global supplier development considering risk factors using fuzzy extended AHP-based approach , 2007 .

[22]  B Cassidy,et al.  Medical record department's leadership role in receivables management. , 1993, Topics in health care financing.

[23]  Anany Levitin,et al.  Data as a Resource: Properties, Implications, and Prescriptions , 1998 .

[24]  R. J. Kuo,et al.  A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network , 2002, Comput. Ind..

[25]  Chia-Chi Sun,et al.  A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods , 2010, Expert Syst. Appl..

[26]  Pei-Chann Chang,et al.  Comparison of microaggregation approaches on anonymized data quality , 2010, Expert Syst. Appl..

[27]  J. Buckley,et al.  Fuzzy hierarchical analysis , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[28]  F. A. Lootsma,et al.  Multicriteria decision analysis with fuzzy pairwise comparisons , 1989 .

[29]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[30]  Mei-Chen Lo,et al.  The assessment of the information quality with the aid of multiple criteria analysis , 2009, Eur. J. Oper. Res..

[31]  Ufuk Cebeci,et al.  Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard , 2009, Expert Syst. Appl..

[32]  Richard Y. Wang,et al.  Anchoring data quality dimensions in ontological foundations , 1996, CACM.

[33]  W. Deming.  Quality, productivity, and competitive position , 1982 .

[34]  Chian-Son Yu,et al.  A GP-AHP method for solving group decision-making fuzzy AHP problems , 2002, Comput. Oper. Res..

[35]  Selçuk Perçin,et al.  Journal of Enterprise Information Management Use of fuzzy AHP for evaluating the benefits of information-sharing decisions in a supply chain , 2015 .

[36]  Erich M. Nahum,et al.  Data Quality and Query Cost in Pervasive Sensing Systems , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[37]  Cengiz Kahraman,et al.  Project risk evaluation using a fuzzy analytic hierarchy process: An application to information technology projects: Research Articles , 2006 .

[38]  Mohiuddin Ahmed,et al.  Application of Quality Function Deployment in redesigning website: a case study on TV3 , 2007, Int. J. Bus. Inf. Syst..

[39]  Temel Öncan,et al.  Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey , 2006, Inf. Sci..

[40]  Jayanthi Ranjan,et al.  Data mining techniques for better decisions in human resource management systems , 2008, Int. J. Bus. Inf. Syst..

[41]  Joanne M. Holden,et al.  Validation study of the USDA's Data Quality Evaluation System , 2009 .

[42]  Alev Taskin Gumus,et al.  Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology , 2009, Expert Syst. Appl..

[43]  L. C. Leung,et al.  On consistency and ranking of alternatives in fuzzy AHP , 2000, Eur. J. Oper. Res..

[44]  Sławomir Biruk,et al.  Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment , 2010 .

[45]  Weisong Shi,et al.  Consistency-driven data quality management of networked sensor systems , 2008, J. Parallel Distributed Comput..

[46]  D. Chang.  Applications of the extent analysis method on fuzzy AHP , 1996 .

[47]  Andreas Ciroth,et al.  Cost data quality considerations for eco-efficiency measures , 2009 .