Engineering asset management organisations (EAMOs), such as utilities and transportation enterprises, store vast amounts of asset-orientated data (Lin, Gao, Koronios, & Chanana, 2007). However, the data and information environments in these organisations are typically fragmented and characterised by disparate operational, transactional, and legacy systems spread across multiple platforms, diverse structures, and different data formats (Haider, 2007; Haider & Koronios, 2003). The plethora of different systems makes it very difficult, even impossible, for a system in one functional unit to communicate with systems in other units. This lack of integration of information systems, together with the large volumes of transactional data which might be spread in different pools across the enterprise, can lead to increased difficulties in analysing, summarising, and extracting actionable information resulting in suboptimal management performance (Ponniah, 2001). Moreover, heightened competition resulting from market deregulation as well as increased regulatory compliance and governance requirements, such as the Sarbanes-Oxley ordinance in the U.S. and the CLERP 9 Acts in Australia, have demanded greater accountability for decision making within such organisations (Logan & Buytendijk, 2003; Mathew, 2003).
On the other hand, existing management information systems are no longer adequate for EAMO’s modern business needs and not always meeting the expectations of decision makers at all hierarchical levels (Olszak & Ziemba, 2007). These systems were unable to handle the integration of different, dispersed, and heterogenic data within such enterprises. Nor could they effectively interpret such data in any broader contexts or discover new data interdependencies (Bui, 2000, cited in Olszak & Ziemba, 2007; Gray & Watson, 1998), due to improper techniques of data acquisition, analysis, discovery, and visualisation (Olszak & Ziemba, 2007). Therefore, in response to these pressing challenges of information dispersion and compliance requirements, EAMOs are compelled to improve their business execution and management decision support through the implementation of a contemporary BI system (Olszak & Ziemba, 2007).
According to Negash (2004), “BI systems combine data gathering, data storage, and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers.” Whilst Moss and Atre (2003) state that “it is an architecture and a collection of integrated operational as well as decision-support applications and databases that provide the business community easy access to business data.” Stated simply, the main tasks of a business intelligence (BI) system include “intelligent exploration, integration, aggregation and a multidimensional analysis of data originating from various information resources” (Olszak & Ziemba, 2007). Implicit in this definition, data is treated as a highly valuable corporate resource, and transformed from quantity to quality (Gangadharan & Swami, 2004). As a result, critical information from many different sources of an asset management enterprise can be integrated into a coherent body for strategic planning and effective allocation of assets and resources. Hence, meaningful information could be delivered at the right time, at the right location, and in the right form (Negash, 2004) to assist individuals, departments, divisions, or even larger units for improved decision-making (Jagielska, Darke, & Zagari, 2003).
From an architectural standpoint, a BI system is composed of a set of three complementary data management technologies, namely data warehousing, OLAP, and knowledge discovery (which is aided predominantly by data mining techniques). To be specific, Olszak and Ziemba (2007, p. 138) posit that a BI system is composed of the following components:
Extraction-Transformation-Load (ETL) tools that are responsible for data transfer from operational or transaction systems to data warehouses;
Data warehouses to provide some rooms for thematic storing of aggregated and analysed data;
OLAP analytic tools to let users access, analyse and model business problems and share information that is stored in data warehouses;
Data mining tools for determining patterns, generalisations, regularities, and rules in data resources;
Reporting and ad hoc inquiry tools for creating and utilising different synthetic reports; and
Presentation layers that include customised graphical and multimedia interfaces to provide users with information in a comfortable and accessible form.
In the past few years, the BI market has experienced extremely high growth as vendors continue to report substantial profits (Gartner, 2006a; IDC, 2007). Forrester’s recent survey indicated that for most CIOs, BI was the most important application to be purchased (Brunelli, 2006). The results of the latest Merrill Lynch survey into CIO spending similarly found that the area with the top spending priority was BI (White, 2006). These findings are echoed by Gartner’s CIOs priorities surveys in 2006 which revealed that BI ranked highest in technology priority (Gartner, 2006b). In the most recent survey of 1,400 CIOs, Gartner likewise found that BI leads the list of the top ten technology priorities (Gartner, 2007).
While the BI market appears vibrant and the importance of BI systems is becoming more widely recognised, particularly in EAMOs, nevertheless few studies have investigated the implementation of BI systems in general and critical success factors in particular. Although there have been a plethora of guidelines from the IT industry, most rely on anecdotal reports or quotations based on hearsay (Jagielska et al., 2003). This is because the study of BI systems is a relatively new area that has primarily been driven by the IT industry and vendors (Jagielska et al., 2003). Therefore, empirical research to shed more light on those critical factors influencing the implementation of BI systems is desirable because the understanding of critical success factors (CSFs) enables BI stakeholders to optimise their scarce resources and efforts on those significant factors that are most likely to have an impact on the system implementation, and thus increase the chances of implementation success.