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Data and information management systems have become a cornerstone for almost all operations of an organisation by providing quality, timely data for decision support. Information systems have progressed to cover a wide variety of asset management areas including asset registration, financial management, process scheduling and control, materials management, maintenance management, condition monitoring, risk management, reliability management, and safety management. Past studies into data management within asset management have focused on understanding why specific systems or specific data management processes are implemented in a firm. However, there has been little investigation into the broader picture of such asset management information systems and their overall data integration strategies. An exploratory, cross-sectional survey was conducted during 2007 and 2008 that explored a variety of data management issues in asset management across more than forty organisations. The survey research questions addressed practices in information system selection, data warehousing, integration, and data retention. Results show the significant adoption of information systems and data warehousing across different industries; the primary use of information systems to streamline business processes and enhance reporting; and the strong desire for improved system integration for next generation asset management information systems.

In an attempt to turn data management into a profitable enterprise, many businesses are seeking to integrate and centralise their data through data warehousing. A data warehouse then allows businesses to turn data into knowledge, and turn knowledge into tangible profits. One key success factor of a data warehouse lies in its ability to integrate data from multiple sources through a unified data model. Within asset management, several such integrated data models have been proposed, however these individually only cover a limited number of areas within asset management data and are not designed with data warehousing in mind. This paper presents the development process of a novel conceptual data warehousing data model that holistically integrates numerous asset management data areas. The comprehensive ethnographic modelling methodology involves a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that describes asset management data. The outputs of the process were verified by more than 20 experts in asset management and validated against four case studies.

Timely decision making is critical in today’s competitive business world and in recent times, data warehousing has been employed by numerous companies to satisfy the needs of accurate and timely information. Data warehousing has traditionally been employed for financial and customer relationship analysis with current applications now moving to other domains, such as the medical and power industries. The asset management industry is one that has seen great strides in recent years due to improved technology, but data warehousing of asset management information has been lacking. A literature review was undertaken to determine data warehousing applications in the area of asset management, and with the lessons learned, a conceptual model of data warehousing for asset management is proposed. The water utility industry is chosen to provide a grounded example of an enterprise data warehouse model that integrates data from various local information systems. The particular issues faced in the asset management domain are highlighted, and several water utility applications are presented.