Academic journal article Journal of Information, Information Technology, and Organizations

The Effect of Implementation Factors on Data Warehousing Success: An Exploratory Study

Academic journal article Journal of Information, Information Technology, and Organizations

The Effect of Implementation Factors on Data Warehousing Success: An Exploratory Study

Article excerpt


Since the early 1990s, the data warehouse has become the foundation of advanced decision support applications (Shim et al., 2002). Using sophisticated online analytical processing (OLAP) and data mining tools,

some corporations are able to exploit insights gained from their data warehouse to significantly increase sales (Cooper, Watson, Wixom, & Goodhue, 2000; Heun, 2000; Whiting, 1999), reduce costs (Watson & Haley, 1998; Whiting, 1999), and offer new and better products or services (Cooper et al., 2000; Levinson, 2000; Watson & Haley, 1998). The payoff from a well-managed data warehouse can be huge. For instance, a study conducted by IDC, a leading research firm, found the average return on investments in data warehousing projects to be about 400 percent (Desai, 1999). By the late 1990s, most large corporations had either built or were planning to build a data warehouse (Joshi & Curtis, 1999).

However, the implementation of a data warehouse is both very expensive and highly risky. One study reported an average cost of $2.2 million for a typical data warehouse (Gagnon, 1999). At the same time, success seems to be the exception rather than the rule. One early study reported that one-half to two-thirds of all initial data warehousing efforts fail (Kelly, 1997), while another study placed the failure rate at 60 to 90 percent (Voelker, 2001). A more recent study put the failure rate around 41 percent (Connor, 2003). Despite the high failure rates, spending on data warehousing grew at a healthy 43 percent annually though 2003 (Trowbridge, 2000) and is expected to rise significantly in 2005 (Agosta, 2004).

A major reason is that, with the dramatic drop in storage costs, companies are racing to build ever-larger data warehouses in pursuit of greater granularity and real time information. For instance, Harrah's Entertainment, a leader in data warehousing, is reportedly spending $10 million to build a 30-terabyte data warehouse (Lyons, 2004). Without a good grasp of the core data warehousing success issues, however, spending more money can potentially create bigger problems and result in expensive failures.

Like all major information systems (IS) projects, any number of things can go wrong in a data warehousing endeavor. Unfortunately, the precise nature of the success factors and their impact on data warehousing are still unclear (Mukherjee & D'Souza, 2003). While many implementation factors that could contribute to success or failure have been discussed by practitioners and researchers alike, the effect of these factors has rarely been tested in empirical research. In order to fill this gap in the literature, the current study identifies a number of implementation factors and tests their effect on success using data collected from a cross sectional survey of data warehousing professionals. The objective is to produce an empirically validated list of factors and report their respective strength of impact on various aspects of data warehousing success. This list is readily useful to practitioners in their planning and implementation of data warehousing projects. The list also points out promising directions for continued research into data warehousing success.

Literature Review

Despite the recognition of data warehousing as an important area of practice and research, relatively few studies have been conducted to assess data warehousing practices in general and critical success factors in particular (Shin, 2003; Watson, Annino, Wixom, Avery, & Rutherford, 2001; Wixom & Watson, 2001). The literature is full of practitioners' accounts of data warehousing projects that have succeeded or failed and the possible reasons for these outcomes. Some attempts have been made to summarize their claims (e.g., Sakaguchi & Frolick, 1997; Vatanasombut & Gray, 1999). A few case studies have investigated data warehousing implementation at selected companies (e. …

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