Factors influencing to the implementation success of big data analytics: A systematic literature review

Abstract

Big data analytics (BDA) readiness factors have been widely researched; nevertheless, few have investigated the impact and success factors of BDA implementation in the organizational context. The relevance and quality of BDA outcomes have been a significant concern to the organizational leaders in supporting them for strategic decision-making. To that end, the objective of this study is twofold. Firstly, it investigates the factors that influencing the success of BDA implementation for effective decision-making. Secondly, this study adds to the body of knowledge in the information system (IS) domain, especially with its focus on BDA implementation packages. Based on 18 selected papers, this review has established 10 influencing factors that may influence the success of BDA implementation, therefore, contribute to the practice and research of BDA domain and its effectiveness towards the organizational performance enhancement.

Examining the Core Dilemmas Hindering Big Data-related Transformations in Public-Sector Organisations

“…Solow's (1987) productivity paradox -IT is everywhere except in productivity statistics -encapsulates several decades of research demonstrating that people's attitudes and ways of working can be barriers and result in dilemmatic outcomes in IT-related innovation. Researchers such as Adrian et al (2017Adrian et al ( , 2018 and Halaweh and Massry (2015) suggest that the same enablers and barriers that apply to IT-related innovations are also relevant to BD-related innovations. From the perspective of the technology used and people's interactions with the innovations, this is true; however, from the perspective of knowledge, the two are quite diff erent, for the reasons given above.…”

“…Unlike IT, however, established BD systems also give rise to new knowledge, creating continuous decision cycles that also create uncertainties tending to result in dilemmas (Pink et al 2018;Hampden-Turner 2009). Adrian et al's (2017Adrian et al's ( , 2018 capabilities is readiness, which, unfortunately, is left undefi ned. Klievink et al (2017, 268), following a similar capabilities approach, suggested that readiness amounts to organisational alignment, organisational maturity and organisational capabilities.…”

“…Conceptually, this study draws on the incorporation interaction of combining the tenets of readiness (Adrian et al 2017(Adrian et al , 2018 and the dilemma approach (Hampden-Turner 1990). Th e hindrances in BD-driven transformations in the two case organisations of this study are associated with the individual actors' (manag-ers) either / or choices and their readiness for BD-related transformations that might generate dilemmas in public organisations.…”

A Preliminary Systematic Performance on Critical Success Factors Categories for Big Data Analytics

“…These results could stem from several issues that occur for most organizations; from the ineligible capabilities of Big Data and analytics infrastructures to process the large, varied and constant flow of data during the implementation phase. Therefore, a combination of such issues related to low-quality data and insufficient organizational requirements from Big Data could affect an organization's overall performance, business outcomes, and decision-making (Adrian et al 2017). Big DataAnalytics (BDA) implementation is enveloped by several factors that organizations should examine to guarantee a successful implementation (Halaweh & Massry, 2015).…”

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

“…The understanding of analytics deliverables and effective decisionmaking totally depends on the influencing factors. Several factors such as organizational capability, technology capability, analytics capability, talent or human capability, and the information quality are effecting the success of its implementation [8].…”