The Value of Big Data Analytics Pillars in Telecommunication Industry

Abstract

In the Big Data age, businesses in every industry must deal with vast volumes of data. Several experts and practitioners have lately emphasized the need of understanding how, why, and when Big Data Analytics (BDA) applications may be a valuable resource for businesses seeking a competitive edge. However, BDA pays off for some firms while failing to pay off for others due to the fact that investment in Big Data continues to present significant challenges due to the missing link between analytics capabilities and firm performance. According to a recent survey, many businesses spend the bulk of their time analyzing data, with only a tiny fraction employing Big Data Analytics to forecast outcomes and even fewer utilizing analytics apps to enhance processes and strategies. As a result, BDA is not widely used, and only a few companies have seen any benefit from it. To address this issue in the telecommunications domain and in light of the paucity of research on the subject, this study focused on the BDA Pillars (BDAP) in order to achieve benefits through increased revenues and cost savings. For the purpose of this research we have adopted qualitative approach with case study method, and technique of data collection includes semi-structure interview and document analysis. The Delphi technique and in-depth interviews conducted confirmed the existence of five critical elements that contribute to the sustainability of BDAPs and their impact on firm performance.

Big Data's Future Potential as an Innovation in Competitive Advantage and Decision-Making Techniques

“…The findings also underscore the importance of investment in the technological infrastructure and human resources required to support the application of Big Data innovations in decision-making strategies, which aligns with previous research (Ahmed et al, 2022;Ashsifa & Ali, 2019;Basile et al, 2024;Cui et al, 2022;Gravili et al, 2023;Keshavarz et al, 2021;Koshcheeva & Lyapina, 2022;Perifanis & Kitsios, 2023;Wang, 2023). In an increasingly connected and digital business environment, organisations need to ensure that they have robust technology systems and employees skilled in data analysis and interpretation.…”

Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review

“…To answer the study question, the study carried out the SLR to determine the top factors that affect BDA adoption among SMEs, upon which the factors that influence the adoption of BDA are identified. This is followed by (Gong & Janssen, 2021), (Lei et al, 2021), (Shahbaz et al, 2021), (Raut et al, 2021), (Dubey et al, 2021), (Wook et al, 2021), (Alalawneh & Alkhatib, 2021), (Rahman et al, 2023), (Bakici et al, 2023), (Benzidia et al, 2021), (Keshavarz et al, 2021), (Ghaleb et al, 2021), (Park & Kim, 2021) 2020 11 (Saleem et al, 2020), (Shabbir & Gardezi, 2020), (Ghasemaghaei, 2020), (Yadegaridehkordi et al, 2020), (Cabrera-Sa ´nchez & Villarejo-Ramos, 2020), (Mikalef & Krogstie, 2020), (Dubey et al, 2020), (Shahbaz et al, 202010 (Raut et al, 2019, (Moktadir et al, 2019), (Ghasemaghaei, 2019), (Nam et al, 2019), (Okcu et al, 2019), (Shahbaz et al, 2019), (Shamim et al, 2019), (Singh & El-Kassar, 2019), (Dubey et al, 2019), (Shorfuzzaman et al, 2019) 2018 8 (Yadegaridehkordi et al, 2018), (Verma et al, 2018), (Caesarius & Hohenthal, 2018), (Raguseo, 2018), (Lai et al, 2018), (Joshi & Biswas, 2018), (Dubey et al, 2018), (Mahesh et al, 2018) 2017 4 (Brock & ...…”

Maturity and Value of Big Data in the Telecommunications Sector in the Republic of Yemen from the Prospective of Information Technology Professionals

“…The dimensions for these variables selected through review previous studies related to the subject of the study, which are global BD maturity models, TDWI BDMM, BDBMMI, BDMA, BDMM, BDMF, MMBDA, IDC, ZBDMM, and BDBMM [15-24-17-18-19-20-21]. The researcher also relied on the study of [16], [22], [23] ,and benefited from it in defining the axes and paragraphs of the questionnaire that reflect the study problem, its questions, the study model relied on two variables: 1-The independent variable: It represented in the maturity of big data through its following sub-variables (Dimension): knowledge, organizational, technological, infrastructure, Government. To know the maturity level of big data in the telecom sector in Yemen, descriptive statistics used.…”