Abstract
Project management (PM) is a vital part of any data warehouse (DWH) implementation due to its complexity, time constraints, size, high costs, and importance to business. To help achieve efficient PM, project managers require a source of reference that aggregates the previously acquired body of knowledge (BOK) and presents the discovered findings. Yet, no such resource currently exits. Furthermore, no evaluation of the existing BOK has been made, which obstructs its current understanding and hinders future enhancements in the field. The goal of this paper is to remove these gaps by conducting a systematic review of the literature. The review identified 33 relevant studies. Results show that the current literature mostly covers global considerations and research on DWH requirements analysis techniques and that only a small number of studies were conducted to assist project managers. The review method incorporated two research dimensions used to synthesize, interpret and present the findings: the PMBOK ® Guide ’s PM knowledge areas (PMKAs) and DWH stage. The majority of identified studies pertained to integration management, followed by scope management PMKAs. The biggest research gap was discovered for procurement management PMKA. The project initiation/planning DWH stage is most frequently analyzed in existing studies, followed by requirement analysis and database design. Findings from the identified studies are incorporated into a reference map in order to serve project managers as a reference point for additional guidance in their projects and an agenda for further research is provided for researchers looking to contribute to the field.
Highlights
Today, data warehouse (DWH), analytics and business intelligence (BI) stand for some of the most important information initiatives for companies [1], [2]
Implications for project managers proposed in this study provide some practical guidelines for practitioners, which can lead to more effective project management (PM) and conclusively, reduce the reported high failure rates of DWH implementations
With the aim to clearly display mappings to the first research dimension, that is, the findings against the PM knowledge areas (PMKAs), subsections of this chapter are further structured in PMKAs, starting from integration management and ending with stakeholder management
Summary
Data warehouse (DWH), analytics and business intelligence (BI) stand for some of the most important information initiatives for companies [1], [2]. DWH projects are mostly noted as large [28], time consuming [29], expensive [30]– [32], and change-sensitive [33] enterprise projects. Due to this specific nature of DWH implementations, they have shown high-failure-rate outcomes [28], [34]–[36], [S15]. Such results, in combination with the trends mentioned above, demonstrate a need for the effective management of these projects, which should be established alongside other factors required for successful DWH implementation [6], [37], [38], [S7], [S22]. There is significant evidence of the need for PM in DWH implementations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
