Critical Success Factors for Big Data: A Systematic Literature Review

Abstract

During the last few decades, many organizations have started recognizing the benefits of Big Data (BD) to drive their digital transformation and to gain faster insights from faster data. Making smart data-driven decisions will help the organizations to ride the waves toward invaluable investments. The successful implementation of Big Data projects depends on their alignment with the current organizational, technological, and analytical aspects. Identifying the Critical Success Factors (CSFs) for Big Data is fundamental to overcome the challenges surrounding Big Data Analytics (BDA) and implementation. In recent years, the investigations related to identifying the CSFs of Big Data and Big Data Analytics expanded on a large scale trying to address the limitations in existing publications and contribute to the body of knowledge. This paper aims to provide more understanding about the existing CSFs for Big Data Analytics and implementation and contributes to the body of knowledge by answering three research questions: 1) How many studies have investigated on Big Data CSFs for analytics and implementation?, 2) What are the existing CSFs for Big Data Analytics, and 3) What are the categories of Big Data Analytics CSFs?. By conducting a Systematic Literature Review (SLR) for the available studies related to Big Data CSFs in the last twelve years (2007-2019), a final list of sixteen (16) related articles was extracted and analyzed to identify the Big Data Analytics CSFs and their categories. Based on the descriptive qualitative content analysis method for the selected literature, this SLR paper identifies 74 CSFs for Big Data and proposes a classification schema and framework in terms of 5 categories, namely Organization, Technology, People, Data Management, and Governance. The findings of this paper could be used as a referential framework for a successful strategy and implementation of Big Data by formulating more effective data-driven decisions. Future work will investigate the priority of the Big Data CSFs and their categories toward developing a conceptual framework for assessing the success of Big Data projects.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

“…Many businesses have not achieved integrated use beyond the initial adoption process [10]. Although adoption of BD is getting attention as a means to create impacts, such as the establishment of new business areas and business optimization, a limited number of businesses have adopted it and obtained the appropriate values [12,13].…”

“…In the case of SMEs, although they are major contributors to the economy of the nation, they are left far behind when it comes to BD implementation, owing to their limited resources and lack of understanding as to the main barriers that prevent BD adoption and use [14,[20][21][22]. A review of the literature shows that studies dedicated to adoption of BD among SMEs are still few and quite limited [12,15,23]. Hence, the present work employs a technological, organizational and environment (TOE) framework to examine BD adoption drivers among the SMEs sector.…”

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…In the recent past 'Big Data' (Ciampi et al 2020;Günther et al 2017;Mikalef et al 2016Mikalef et al , 2020aNelson and Olovsson 2016;Olszak and Mach-Król 2018;Wamba et al 2015;Zhao-hong et al 2018), 'Big Data and dynamic capabilities' (Rialti et al 2019), 'Big Data business models' (Huberty 2015;Wiener et al 2020), and 'Big Data assessment models' (Adrian et al 2016) have been explored. Researchers have also focused upon 'Big Data Analytics' (Adrian et al 2017;Al-Sai et al 2020;Bogdan and Lungescu 2018;Inamdar et al 2020;Maroufkhani et al 2019;Mikalef et al 2019a;Singh et al 2021;Sivarajah et al 2017), Artificial Intelligence (Alsheiabni et al 2020;Borges et al 2021;Keding 2020;Kitsios and Kamariotou 2021;Markus 2017), Business Analytics (Cao and Duan 2017), and Business Intelligence and Analytics (BI&A) (Chen et al 2012;Eggert and Alberts 2020;Lautenbach et al 2017;Llave 2017;Moreno et al 2019;Sang et al 2020).…”

An active human role is essential in big data-led decisions and data-intensive science

“…3 Many sectors have been transformed by the possibility to make accurate and useful decisions by harnessing big data through diverse effective strategies. 4 For instance, in the health sector, sophisticated big data analytics can assist doctors to generate accurate and clinically useful individualized predictions for diagnostic or prognostic purposes, which ultimately translate into improved treatments, and more efficient and cost saving service delivery. 5,6 In the education sector, the ability to process big data about students in real-time helps to understand and enhance learner's behavior and performance and to integrate data into the curriculum.…”