International Journal of Data Science and Analysis
Volume 6, Issue 6, December 2020, Pages: 183-203
Received: Oct. 30, 2020;
Accepted: Nov. 11, 2020;
Published: Nov. 19, 2020
Views 15 Downloads 28
Dina Darwish, Computing and Digital Technology School, ESLSCA University, Giza, Egypt
Big Data represents the greatest game-changing chance and change in outlook for marketing since the creation of the telephone or the Web going standard. Big Data alludes to the ever-expanding volume, velocity, variety, variability and multifaceted nature of data. Big Data is the key result of the new promoting scene, conceived from the computerized world we currently live in for marketing associations. The expression "big data" doesn't simply allude to the information itself; it additionally alludes to the difficulties, capacities and skills related with putting away and examining such gigantic data sets to help a degree of decision-making that is more precise and timely than anything recently endeavored. Because of the many benefits of big data, the big data applications have appeared, and they can play important roles especially in making companies take informative business decisions in different fields, such as, healthcare, banking, manufacturing, media and entertainment, education and transportation and many others. This paper concentrates on the importance of Big Data Analytics nowadays, especially in the marketing process inside companies, as well as challenges and obstacles facing Big Data analytics, and a case study of a bank wanting to market a new financial tool to its customers is studied using R tool.
Developing and Implementing Big Data Analytics in Marketing, International Journal of Data Science and Analysis.
Vol. 6, No. 6,
2020, pp. 183-203.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Diebold, F. (2003). Big Data: Dynamic Factor Models for Macroeconomic Measurement and Forecasting. Advances in Economics and Econometrics, Eighth World Congress of the Econometric Society. Cambridge University Press, 115-122. doi: 10.1017/CBO9780511610264.005.
Laney, D. (February 6, 2001). 3-D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note. Retrieved 10 November 2020, from https://idoc.pub/documents/3d-data-management-controlling-data-volume-velocity-and-variety-546g5mg3ywn8.
Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A. and Gruber, R. E. (2008). Bigtable: A Distributed Storage System for Structured Data. ACM Transactions on Computer Systems, 26 (2), 1-26. doi: 10.1145/1365815.1365816.
EMC education services (2015). Data Science and Big Data Analytics, Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons. doi: 10.1002/9781119183686.
McKinsey & Company (March 2015). Marketing & Sales, Big Data, Analytics, and the future of Marketing & Sales. Retrieved 10 November 2020, from https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Marketing%20and%20Sales/Our%20Insights/EBook%20Big%20data%20analytics%20and%20the%20future%20of%20marketing%20sales/Big-Data-eBook.ashx.
H. Davenport, T., and Dyché, J. (May 2013). Big Data in Big companies. International Institute for analytics. Retrieved 10 November 2020, from https://docs.media.bitpipe.com/io_10x/io_102267/item_725049/Big-Data-in-Big-Companies.pdf.
Dresner Advisory Services (December 20, 2017). Big Data Analytics Market Study Wisdom of Crowds Series Licensed to MicroStrategy. Retrieved 10 November 2020, from https://www3.microstrategy.com/getmedia/cd052225-be60-49fd-ab1c-4984ebc3cde9/Dresner-Report-BigData_Analytic_Market_Study-WisdomofCrowdsSeries-2017.pdf.
Naimat, A. (2016). The Big Data Market, A data driven analysis of companies using Hadoop, Spark, Data Science & Machine Learning. Oreilly. Retrieved 10 November 2020, from http://ixion.pld-linux.org/~arekm/free-books/data/the-big-data-market.pdf.
Intel IT Center Peer research (August 2012). Big data analytics, Intel’s IT Manager Survey on How Organizations Are Using Big Data. Retrieved 10 November 2020, from https://www.intel.com/content/dam/www/public/us/en/documents/reports/data-insights-peer-research-report.pdf.
Project, R (2020). R project. Retrieved 10 November 2020, from https://www.r-project.org/about.html.
Project, GNU (2020). GNU project. Retrieved 10 November 2020, from http://www.gnu.org/gnu/gnu.html.
Project, CRAN (2020). CRAN project. Retrieved 10 November 2020, from https://cran.r-project.org/bin/windows/.
Anurag (18 Apr. 2020). 6 Reasons: Why Choose R Programming for Data Science Projects? Retrieved 10 November 2020, from https://www.newgenapps.com/blog/6-reasons-why-choose-r-programming-for-data-science-projects/.
Agrawal, V. (25 February, 2016). Applications Of R Programming In Real World. Retrieved 10 November 2020, from https://elearningindustry.com/ applications-r-programming-r-eal-world.
Lawson, J. (2015). Design and Analysis of Experiments with R. Brigham Young University Provo, Utah, USA, Taylor & Francis Group. doi: 10.1201/b17883.