Big Data in the Contemporary Business World: Managing Information
While offering a plethora of opportunities to the members of the global business community in terms of allocating the available resources wisely and identifying the existing choices in the process of business decision-making (Chen, Chiang, and Storey 1166), Big Data also causes numerous problems. Particularly, the application of the Big Data analysis to solve the problems related to the improvement of Supply Chain Management (SCM) processes (Annamalai and Romani 2321) needs to be addressed. Although it has been assumed that the application of the subject matter to the designated environment will help enhance the SCM processes, it may also weaken the security of data transfer, which requires designing an elaborate Big Data Management model.
The problem detailed above is of huge interest to me as its removal will allow for reducing the restrictions imposed on the SCM processes in the contemporary global economy. As a result, creating a safer environment for Big Data transfer can become a possibility with the subsequent drop in the number of threats to the security of the data transfer as well as for its further interpretation and usage. Therefore, the current concept of the Big Data lacks a proper analysis and needs further studies. Particularly, the existing models of the Big Data analysis will have to be identified so that a suitable framework could be created (Robak, Franczyk, and Robak 246).
The significance of the problem identified above is rather big for business. In the environment of the global economy, the ability to operate the Biog Data defines the success of the SCM strategy. The later, in its turn, serves as the means of increasing the quality of the services and products provided to the end customer due to the enhancement of the knowledge management approach. Particularly, it is expected that delays, misconceptions, defects occurring during the production process, etc., can be avoided. Finally, a better analysis of the customers and their needs is expected.
It is suggested that the problem above should be addressed by creating a quality-driven model (Hartmann, Zaki, Feldmann, and Neely 3). The specified approach will allow for an incorporation of a TQM approach (Liedtke 2), which will contribute to addressing the quality issues mentioned above. The provision of the above solution is likely to enhance the security and efficacy of the Big Data management by introducing the principles of CSR to it (Rehman, Baloch, and Sethi 101).
Works Cited
Annamalai, Cindy, and Anne V. Romani. “Critical Success Factors (CSFs) of ServiceOriented Architecture (SOA) in BIG DATA Systems.” International Journal of Research in Management, Science & Technology 3.3 (2015): 23-27. Print.
Chen, Hsinchun, Roger H. L. Chiang, and Veda C. Storey. “Business Intelligence and Analytics: From Big Data to Big impact.” MIS Quarterly 36.4 (2012): 1165-1168. Print.
Hartmann, Phillip, Mohammed Zaki, Niels Feldmann, and Andy Neely. A Taxonomy of Data-driven Business Models used by Start-up Firms. Cambridge, UK: University of Cambridge, 2014. Print.
Liedtke, Charles 2015, Quality, Analysis, and Big Data. Web.
Rehman, Alam, Quadar Bakshish Balochh, and Sonia Sethi. “Understanding the relationship between Firm’s Corporate Social Responsibility and Financial Performance: Empirical Analysis.” Abasyn Journal of Social Sciences 8.1 (2012): 98-107. Print.
Robak, Silva, Bogdan Franczyk, and Marcyn Robak. “Research Problems Associated with Big Data Utilization in Logistics and Supply Chains Design and Management.” Computer Science and Information Systems 3 (2014): 245-249. Print.