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Abstract Engineering asset management processes rely heavily on input of data and also produce a large amount of data. Many asset management organisations need to manage their data for a long period of time (e.g. Water supply pipelines data will be kept for more than 100 years in utility companies). Due to the inability to access original data requirements and system design documentation, it is difficult for these organisations to redesign their asset management systems which often results in ongoing data quality problems. Our nation-wide data quality survey of 2500 Australian engineering management organisations and a pilot study have revealed that many of the data quality problems emanate from inconsistent applications of business rules that govern the behaviour of data (e.g. data management, data flow, system interactions and so on) within asset management information systems. Thus, this research will investigate the problems of business rule-based data and information integration from disparate sources in various forms found in asset management systems (e.g. Databases, Excel spreadsheets, etc.). This research aims at developing innovative methods to automatically discover undocumented business rules from disparate data sources and to use business rules for automated data integration in order to deliver quality asset data sets. Keywords Data Quality, Business Rule and Discovery __________________________________ J. Gao School of Computer and Information Science, University of South Australia, Australia A. Koronios School of Computer and Information Science, University of South Australia, Australia S. Kennett Maritime Platforms Division, Defence Science and Technology Organisation, Australia H. Scott Defence Materiel Organisation, Australia J.E. Amadi-Echendu, K. Brown, R. Willett, J. Mathew, Definitions, Concepts and Scope of Engineering Asset Management, © Springer 2010

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Introduction

Industry has recently put a strong emphasis on to the area of asset management (AM). In order for organisations to generate revenue they need to utilize assets in an effective and efficient way. Often the success of an enterprise depends largely on its ability to utilize assets efficiently. In other words, asset management is regarded as a set of business-capability building-processes within organisations. Thus, asset management has been regarded as an essential business process in many organisations, and is moving to the forefront of contributing to an organization’s financial objectives. However, engineering asset management processes rely heavily on input of data and also produce a large amount of data during business operations. In many cases, the poor data quality resulted in severe impacts on asset management system performances – for example, an inability to determine the asset health status and predict the asset maintenance schedule. Data Quality is becoming a critical issue for asset management. Modern organisations, both public and private, are continually generating large volumes of data. Ac

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