Empirical Investigation on the Evolution of BI Maturity in Malaysian Organizations

Empirical Investigation on the Evolution of BI Maturity in Malaysian Organizations

In Lih Ong (Universiti Tunku Abdul Rahman, Malaysia) and Pei Hwa Siew (Universiti Tunku Abdul Rahman, Malaysia)
DOI: 10.4018/978-1-4666-7272-7.ch012
OnDemand PDF Download:
No Current Special Offers


Many organizations have recognized the importance of increasing commitment towards delivering long-term success of Business Intelligence (BI). However, effective BI strategies and governance to accommodate the rapid growth of data volumes are still scarce. Furthermore, there appears to be low usage rates of BI and analytics among business users. Consequently, many organizations are still positioned at either low or moderate levels in the BI maturity chart. In view of these issues, this chapter explores and develops a multi-dimensional BI maturity model that serves as a guideline to lift the BI capabilities of an organization for effectively planning, assessing, and managing BI initiatives. The focus of this research is to assess the current BI maturity level in Malaysian organizations and identify factors that affect the BI maturity. It also examines the effect of organization's demographic variables (i.e., types of industry, organizational size, and age of BI initiatives) on the BI maturity.
Chapter Preview

Business Intelligence

From a historical standpoint, the underlying concept of BI is not new one. It has existed over the last 50 years in the area of information systems (IS). According to Wixom et al. (2011), the origins of BI can be traced back to the early 1970s when decision support systems (DSS) were first introduced. Over the years, numerous applications such as executive information systems (EIS), online analytical processing (OLAP), data mining, predictive analytics, and dashboards have emerged and added to the domain of decision support applications (Watson and Wixom, 2007).

The term “business intelligence” was first used by Hans Peter Luhn in 1958 in an IBM journal article. However, BI became widely recognized in the 1990s only after it was used by Howard Dresner, a research analyst of Gartner Group in 1989 (Shollo and Kautz, 2010). According to Power (2002), Howard Dresner explained BI as “a set of concepts and methods to improve business decision making by using fact-based support systems” (p. 128).

Even though there has been a growing interest in BI area, there is no commonly accepted definition of BI. The literature shows that the definition of BI has evolved from a one-dimensional view to a multi-dimensional view (Vitt et al., 2010). Drawing upon extant literature, it was found that the scope and definition of BI have been extended to include the idea that it is product, not just a process. As noted in the study of Jourdan et al. (2008), BI is viewed as both a process and a product. Petrini and Pozzebon (2009) provided a similar distinction of perspectives to BI in terms of technical and managerial perspectives. Shariat and Hightower (2007) characterized BI as a composition of process, technology, and product. Based on the definitions from various sources, four main focus of BI were identified for this research, namely organizational management, process, technology, and outcome as summarized in Table 1.

Key Terms in this Chapter

OLAP: Online analytical processing (OLAP) tools “allow the user to query, browse, and summarize information in an efficient, interactive, and dynamic way” ( Shariat and Hightower, 2007 , p. 41).

Analytics: Davenport et al. (2007) AU15: The in-text citation "Davenport et al. (2007)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. describe analytics as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p. 7).

Capability Maturity Model: Capability maturity model (CMM), a well-known software process improvement model, was developed by Watts S. Humphrey and his team members from Software Engineering Institute (SEI) of Carnegie Mellon University in 1986 ( Paulk et al., 1993 ). CMM is structured into five maturity levels: initial, repeatable, defined, managed, and optimizing.

Data Warehouse: The function of data warehouse (DW) is to “collect and store integrated sets of historical data from multiple operational systems and feeds them to one or more data marts” ( Williams and Williams, 2007 , p. 201).

Business Intelligence: Business intelligence (BI) refers to “a collection of data warehousing, data mining, analytics, reporting and visualization technologies, tools, and practices to collect, integrate, cleanse, and mine enterprise information for decision making” ( Dayal et al., 2009 , p. 1).

Maturity Model: Tapia et al. (2007) defines maturity model as “a framework that describes, for a specific area of interest, a number of levels of sophistication at which activities in this area can be carried out” (p. 203).

ETL: Williams and Williams (2007) defines extract, transformation, and loading (ETL) as a “process that extracts data from source systems, potentially changes it (transformation process), and loads it into target data stores in the BI/DW environment” (p. 201).

Complete Chapter List

Search this Book: