The Deficiencies of Current Data Quality Tools in the Realm of Engineering Asset Management

Data and information quality is a well-established research topic and gradually appears on the decision-makers' top concern lists. Many studies have been conducted on how to investigate the generic data/information quality issues and factors by providing a high-level abstract framework or model. Based on these previous studies, the researchers of this paper tried to discuss the actual data quality problems with the operation-level and middle-level managers in engineering asset management organizations. By identifying the unique data quality problems (fitness for use) in asset management and reviewing the existing data cleansing software tools against real engineering asset databases, the deficiencies of the existing data cleansing approach are highlighted.

[1]  Graeme G. Shanks,et al.  Understanding Data Quality in a Data Warehouse , 1998, Aust. Comput. J..

[2]  Ken Orr Data Quality and System Theory. , 1998 .

[3]  Frada Burstein,et al.  Decision support in an uncertain and complex world , 2007, Decis. Support Syst..

[4]  Felix Naumann,et al.  Information Quality: How Good Are Off-The-Shelf DBMS? , 2004, ICIQ.

[5]  Yvette Salaün,et al.  Information quality: meeting the needs of the consumer , 2001, Int. J. Inf. Manag..

[6]  Richard Y. Wang,et al.  A product perspective on total data quality management , 1998, CACM.

[7]  C. Spires Asset and maintenance management ‐ becoming a boardroom issue , 1996 .

[8]  Graeme Shanks,et al.  A Semiotic Information Quality Framework , 2004 .

[9]  J. C. Steed Aspects of how asset management can be influenced by modern condition monitoring and information management systems , 1998 .

[10]  Barbara Wixom,et al.  An Empirical Investigation of the Factors Affecting Data Warehousing Success , 2001, MIS Q..

[11]  Graeme G. Shanks,et al.  Stakeholder Perceptions of Data Quality in a Data Warehouse Environment , 1999, Aust. Comput. J..

[12]  M. Pamela Neely A Proposed Framework for the Analysis of Source Data in a Data Warehouse , 2001, IQ.

[13]  M. Pamela Neely DATA QUALITY KNOWLEDGE MANAGEMENT: A TOOL FOR THE COLLECTION AND ORGANIZATION OF METADATA IN A DATA WAREHOUSE , 2002 .

[14]  Shien Lin,et al.  Inforamtion Quality in Engineering Asset Management , 2007 .

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

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

[17]  Diane M. Strong,et al.  Information quality benchmarks: product and service performance , 2002, CACM.

[18]  T. J. Hannagan,et al.  Management: Concepts & Practices , 1998 .

[19]  M. Pamela Neely,et al.  Data Quality Tools for Data Warehousing: A Small Sample Survey , 1998, IQ.

[20]  I. Caballero,et al.  Data quality management improvement , 2003 .

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

[22]  Christopher P. Firth Data Quality in Practice: Experience from the Front Line , 1996, IQ.

[23]  Diane M. Strong,et al.  IT process designs for improving information quality and reducing exception handling: A simulation experiment , 1997, Inf. Manag..

[24]  A. Adam Whatever happened to information systems ethics? Caught between the devil and the deep blue sea , 2004 .

[25]  Donald P. Ballou,et al.  Designing Information Systems to Optimize the Accuracy-Timeliness Tradeoff , 1995, Inf. Syst. Res..