For an organization every year, a large amount of information is generated regarding its employees, customers, business partners, suppliers, etc. Volume, which is one of the attributes of big data, is aptly named because of the vast number of data sources and the size of data generated by these sources. Big data solutions should not only focus on the technological aspects, but also on the challenges that may occur during the project lifecycle. The main purpose of this research is to build on the current diverse literature around big data by contributing discussion on factors that influence successful big data projects. The systematic literature review adopted in this study includes relevant research regarding such critical success factors that are validated in previous studies. The study compiled these critical success factors as provided in the literature regarding big data projects. Notable success factors for big data projects were compiled from literature such as case studies, theoretical observations, or experiments.
TopIntroduction
The explosion of data being captured and stored in information systems has created a new area of challenges and opportunities for information technology (IT) professionals. While substantial efforts have been made towards algorithms and technologies that are used to perform these analytics, comparatively, there has been limited empirical research on Critical Success Factors (CSFs) that relate to Big Data projects.
Critical Success Factors (CSFs) are the few key areas where “things must go right” for “the business to flourish and for the manager's goals to be attained” (Bullen and Rockart, 1981, p. 7). Also, they are common means of assessing projects (Nixon, Harrington and Parker, 2012). Various challenges of human and organizational components of a project can be approached and tackled by understanding the related CSFs (Fortune and White, 2006).
The study of CSF for project management began in the 1960s, several lists of factors have been published where some researches have focused on specific problem domains and types of activity, and others have suggested CSFs, which can apply to all types of projects (Fortune and White, 2006). Some of the most studied CSFs are defined and examined in the next sections.
This conceptual chapter identifies the key areas –Critical Success Factors– essential for achieving project success in Big Data projects. A review of the literature indicated a gap exists in the project management literature and the business literature about a comprehensive factor list to support predicting project performance (Cooke-Davies, 2002; Hyväri, 2006).
The lack of critical success factor sources can doom an IS project to an absolute failure. This research promises to help organizations identify factors that impact success – as perceived by practitioners and professionals – on Big Data projects.”
The objectives of this chapter are as follows:
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to build on the current diverse literature around Big Data
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to provide insight into the CSFs of Big Data projects
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to present a joint agreement for CSFs
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to generate solutions and recommendations for success
The“research objectives are based on the argument establishing Big Data be used as a tool for the organization to develop and create efficiencies enterprise-wide.
A comprehensive review of the literature is conducted to depict CSFs. The literature review includes relevant research regarding such critical success factors that are validated in previous studies. Several different case studies and theoretical discussions enlist success factors regarding Big Data projects. The study compiled these critical success factors as provided in the literature regarding Big Data projects. Significant success factors for Big Data projects were compiled from literature such as case studies, theoretical observations, or experiments. The chapter identifies the current gaps, definitions, and existing variables from the literature regarding Big Data projects and CSFs.
Different challenges are encountered at an organizational level when implementing Big Data projects (Saltz, 2015). To deploy and exploit Big Data in an optimal manner, the organization must pay more efforts in managing these projects more efficiently. The literature review uncovered several research efforts on project success and performance.
Given the importance of data and information analysis for the success and survival of organizations, big data management and implementation projects present a critical issue for all companies and organizations today. The CSFs introduced and discussed in this study would provide useful guidelines for managers to carry out Big Data projects in their institutions.”
TopBackground
A“literature review on critical success factor theories led to varying conclusions by different researchers on the importance and the inclusion of factors (Anderson et al., 2006; Baladi, 2007; Delisle, 2001; Hass, 2006; Nasr, 2004; Pinto, 1986; Shao, 2006; Westlund, 2007; Felix et al., 2018; Tokuç, Uran and Tekin, 2019; Narayan and Tan, 2019). The theories include the dynamic importance of factors theory, critical success indicators theory, integrated project planning, control system theory, competent project manager theory, communication theory, and other theories. Professionals frequently use the principles of these theories to affect project performance.