The primordial role of Business Intelligence and Real Time Analysis for Big Data : Finance-based case study
Keywords:
Big data, Business intelligence, Real time analysis, characteristics, relationshipsAbstract
This study is about big data and its relationships with business intelligence and real time analysis. Few studies have studied this relation and fewer the parameters and variables of the characteristics and relations. In this study, this is presented in the literature review then for
the research method, it is a questionnaire to finance sector leaders managers –unit of analysis with Lickert scale, yes and no questions and comments about the characteristics and relations of big data with real time analysis and business intelligence. The analysis uses SPSS for windows and NVIVO 12 for the quantitative and qualitative analysis. The results of the analysis present concrete and concise models in which big data is in relation to real time analysis and business intelligence. It also provides a thematic analysis leading to the development of a new framework model that lead to the definition of the characteristics and relationships. There are various
theoretical and managerial implications for the big data management and possible finance sector. The future research is to scale the questionnaire to a survey basis, to modify the origins of the questions to a complete Lickert scale and to elaborate on new links with big data using the new conceptual framework.
References
Alnoukari, Mouhib, 2020, An examination of the organizational impact of business intelligence and big data based on management theory, Vol. 10 No. 3 (2020): Journal of Intelligence Studies in Business, Vol. 10, Nr. 3 2020, https://doi.org/10.37380/jisib.v10i3.637.
Atriwal, Labhansh, Parth Nagar, Sandeep Tayal and Vasundhra Gupta. “Business Intelligence Tools for Big Data.” (2016).
Bhatti, A., Malik, H., Ahtisham, Z. K., Aamir, A., Lamya, A. A. & Ullah, Z. 2021, “Much- needed business digital transformation through big data, internet of things and blockchain capabilities: implications for strategic performance in telecommunication sector”, Business Process Management Journal, vol. 27, no. 6, pp. 1854–1873.
Cronemberger, F. A. 2018, Factors Influencing Data Analytics Use in Local Governments, State University of New York at Albany.
Dong-Hui, J. & Hyun-Jung, K. 2018, “Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics”, Sustainability, vol. 10, no. 10, pp. 3778.
Ge, M. 2018, “The Study of “big data” to support internal business strategists”, IOP Conference Series.Earth and Environmental Science, vol. 108, no. 4.
Goar, V. K., and N. S. Yadav. “Business Decision Making by Big Data Analytics”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 5, May 2022, pp. 22–35, doi:10.17762/ijritcc.v10i5.5550.
Ionescu, L. & Andronie, M. 2021, Big Data Management and Cloud Computing: Financial Implications in the Digital World, EDP Sciences, Les Ulis.
Keren Naa, A. A. & Owen, R. 2019, “A Micro-ethnographic Study of Big Data-Based Innovation in the Financial Services Sector: Governance, Ethics and Organisational Practices: JBE”, Journal of Business Ethics, vol. 160, no. 2, pp. 363–375.
Kimble, C. and Milolidakis, G. (2015), Big Data and Business Intelligence: Debunking the Myths. Glob. Bus. Org. Exc., 35: 23–34. https://doi.org/10.1002/joe.21642.
Kumar Mishra, Devendra, Kushal Johari, Shivangi Ghildiyal, Dr. Arvind Kumar Upadhyay and Dr. Sanjiv Sharma, 2022, A Novel Approach in Business Intelligence for Big Data Analytics Using an Unsupervised Technique, ECS Trans. 107 12525.
Leitner-Hanetseder, Susanne & Lehner, Othmar. (2022). AI-powered information and Big Data: current regulations and ways forward in IFRS reporting. Journal of Applied Accounting Research. 10.1108/JAAR-01-2022-0022.
Li, P. (2022). Research on Big Data Driven Innovation of Public Management Mode. BCP Social Sciences & Humanities, 20, 322–327. https://doi.org/10.54691/bcpssh.v20i.2337.
Mangla, S. K., Raut, R., Narwane, V. S., Zuopeng (Justin) Zhang & priyadarshinee, P. 2020, “Mediating effect of big data analytics on project performance of small and medium enterprises”, Journal of Enterprise Information Management, vol. 34, no. 1, pp. 168–198.
Marshall, A., Mueck, S. & Shockley, R. 2015, “How leading organizations use big data and analytics to innovate”, Strategy & Leadership, vol. 43, no. 5, pp. 32–39.
Mishra, Hariom R., 2022, Big Data Security Challenges, International Journal of Research Publication and Reviews, vol. 3, no. 10, pp. 693–696, October 2022. ISSN 2582-7421.
Othman Anawar, S. N. F., Selamat, S. R., Ayop, Z., Harum, N., & Abdul Rahim, F. (2022). Security and Privacy Challenges of Big Data Adoption: A Qualitative Study in Telecommunication Industry. International Journal of Interactive Mobile Technologies (iJIM), 16(19), pp. 81–97. https://doi.org/10.3991/ijim.v16i19.32093.
Paradza, D. & Daramola, O. 2021, “Business Intelligence and Business Value in Organisations: A Systematic Literature Review”, Sustainability, vol. 13, no. 20, pp. 11382.
Persaud, A. 2021, “Key competencies for big data analytics professions: a multimethod study”, Information Technology & People, vol. 34, no. 1, pp. 178–203.
Pour, Mona Jami, Fatemeh Abbasi and Babak Sohrabi, 2022. Toward a Maturity Model for Big Data Analytics: A Roadmap for Complex Data Processing, International Journal of Information Technology & Decision Making, 1–43, 10.1142/S0219622022500390 [doi].
Ramachandra M. N., Srinivasa Rao M., Lai W. C., Parameshachari B. D., Ananda Babu J., Hemalatha K. L. An Efficient and Secure Big Data Storage in Cloud Environment by Using Triple Data Encryption Standard. Big Data and Cognitive Computing. 2022; 6(4):101. https://doi.org/10.3390/bdcc6040101.
Sirin, E. and H. Karacan, “A Review on Business Intelligence and Big Data”, Int J Intell Syst Appl Eng, vol. 5, no. 4, pp. 206–215, Dec. 2017.
Yin, R. K. (2014) Case Study Research: Design and Methods (5th edn.). Thousand Oaks, CA: SAGE.
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