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.
Highlights
In recent years, Big Data (BD) has become the main tech talk between academia and practitioners in the digital competitive play yard
Based on conducting Systematic Literature Review (SLR) by [38], [39], this paper attempts to contribute to the body of knowledge by answering three research questions as: RQ1: ‘‘How many studies have investigated on Big Data Critical Success Factors (CSFs) for analytics and implementation?’’
STAGE 1: PLANNING THE REVIEW The activities associated with the stage of planning include identifying the need for the review, formulating the research questions, identifying the search strategy, and develop the review protocol that will be used to formulate the research question which will be addressed and the review methodology which will be used to perform the review [38]. This SLR will answer three research questions: RQ1: ‘‘How many studies have investigated on Big Data CSFs for analytics and implementation?’’, RQ2: ‘‘What are the CSFs of Big Data Analytics?’’, and RQ3: ‘‘What are the categories of Big Data Analytics CSFs?’’
Summary
Big Data (BD) has become the main tech talk between academia and practitioners in the digital competitive play yard. BD is an important asset that attracts the attention of many CEOs in different organizations to gain faster insights and high revenue [1]. The journey of BD started when many organizations recognized that the large volume of their data exceeds the capabilities of their organizations, process, capacity, structure, technology infrastructure, and governance. They struggled to deal with the requirements for analyzing the high volume of various data [2]–[4]. In 2013, they defined Big Data as ‘‘a high-volume, highvelocity and high-variety information assets that demand cost effective, innovative forms of information processing for enhanced insight and decision making’’ [5], [6]. Industrial Development Corporation (IDC) and EMC Corporation reported that the amount of created data in 2020 will be exceeding 40 zettabytes (ZB) which is greater than the amount of data in 2009 with a scale of 44 times [5]–[7]
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