Abstract

Advanced data analytics especially big data analytics (BDA) has proved to have a great potential to improve public service delivery both in urban and rural or remote areas. However, many of the BDA implementation projects in public sector organizations are failing. Early efforts to the successful implementation of BDA, suggest that business-driven reference architecture (RA) for these projects is among the viable solutions to have BDA project failure rate addressed in public sector organizations. The current study aimed at designing business-driven RA for big data analytics implementation (BRABDAI) to act as a frame of reference (blueprint) for delivering big data analytics implementation projects in public sector organizations. Uganda as a case study, three public organizations were considered (Ministry of Health, Ministry of Education and Sports, and Uganda Bureau of Statics). To accomplish the study's goals, the design science research methodology (DSRM) was used supported by a questionnaire survey and mini literature review for data collection. The developed RA offers a mechanism to systematically consider business use cases and processes in the first place before assembling BDA technical aspects and procedures. Hence, the main result of this paper is BRABDAI for public sector organizations. The practical implication of the paper is that feasibility study, requirement engineering, design, implementation, deployments, and maintenance of big data analytics projects in public sector organizations can now be driven by the need of the organization in question, other than assembling technology first which can result into detrimental misalignment between organizational vision and technology.

Keyword

Big data analytics, Reference architecture, Design science research methodology , Business-driven reference architecture.

Cite this article

Didas M, Chali FH, Elisa N.A business-driven reference architecture for big data analytics implementation by public sector organizations: a case study of Uganda. International Journal of Advanced Computer Research. 2024;14(69):125-149. DOI:10.19101/IJACR.2023.1362047

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