Role of Big Data and Predictive Analytics in Business organizations: A Review of Current Status and Research Issues

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2018 by IJCTT Journal
Volume-61 Number-1
Year of Publication : 2018
Authors : Pavithra K
DOI :  10.14445/22312803/IJCTT-V61P103

MLA

MLA Style: Pavithra K "Role of Big Data and Predictive Analytics in Business organizations: A Review of Current Status and Research Issues" International Journal of Computer Trends and Technology 61.1 (2018): 18-20.

APA Style:Pavithra K, (2018). Role of Big Data and Predictive Analytics in Business organizations: A Review of Current Status and Research Issues. International Journal of Computer Trends and Technology, 61(1), 18-20.

Abstract
The role of Big Data and predictive analytics in business organizations is set to rise in importance. In today’s globalized competitive business environment, companies are facing challenges in dealing with big data issues of rapid decision-making for improved productivity. This paper overviews and consolidates the efforts of various researchers in designing strategies, frameworks and component functionalities of big data analytics for overall firm performance. In this paper, the impact of Big Data and Predictive Analytics on supply chain management, customer relationship management and organizational performance are also discussed. The limitations and scope for future work in this field are also outlined.

Reference
[1] S.C. Lenny Koh, Mehmet Demirbag, Erkan Bayraktar, Ekrem Tatoglu, Selim Zaim, The impact of supply chain management practices on performance of SMEs, Industrial Management & Data Systems, Vol. 107( 1), 2007, 103-124.
[2] Angappa Gunasekaran, Thanos Papadopoulos , Rameshwar Dubey, Samuel Fosso Wamba, Stephen J. Childe , Benjamin Hazenf, Shahriar Akter, Big data and predictive analytics for supply chain and organizational performance, Journal of Business Research 70 (2017) 308–317.
[3] Matthew A. Waller, Stanley E. Fawcett, Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain, Journal of Business Logistics, 2013, 34(4): 249–252.
[4] Obias Schoenherr, Cheri Speier-Pero, Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential, Journal of Business Logistics, 2015, 36(1): 120–132.
[5] Ren, S. Ji-Fan., Fosso Wamba, S., Akter, S., Dubey, R. & Childe, S. J. (2017), Modelling quality dynamics, business value and firm performance in a big data analytics environment, International Journal of Production Research, 55 (17), 1-16.
[6] Wang, Gang and Gunasekaran, Angappa and Ngai, Eric W.T. and Papadopoulos, Thanos (2016), Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics,176. pp. 98-110.
[7] D. P. Acharjya, Kauser Ahmed P, Survey on Big Data Analytics: Challenges, Open Research Issues and Tools, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 2, 2016, 511-518.
[8] Amir Gandomi, Murtaza Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management 35 (2015) 137–144.
[9] Jay Lee*, Hung-An Kao, Shanhu Yang, Service innovation and smart analytics for Industry 4.0 and big data Environment, Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems, Procedia CIRP 16 ( 2014 ) 3 – 8.
[10] Uthayasankar Sivarajah, Muhammad Mustafa K, Zahir Irani, Vishanth Weerakkody, Critical analysis of Big Data challenges and analytical methods Journal of Business Research 70 (2017) 263–286.

Keywords
Supply Chain Management, Customer Relationship, Organizational Growth, Data Scientist, IoT.