Vol: 7, Issue: 3 2017

Page: 21-23

Business Analytics and Data Mining Techniques Using Predictive Algorithms to Enhance Business Intelligence

Rishi Jain, S.Venkatesan

Received Date: 2017-07-03

Accepted Date: 2017-07-28

Published Date: 2017-08-03

The objective of this paper is to present a review literature on what are impacts of Data Mining (DM) and Business Analytics (BA) in enhancing Business Intelligence (BI). The paper highlights various features of DM and BA using predictive algorithms. It involves three steps: explorations, pattern identification and deployment. Business analytics presents itself as an information system that combines different data from internal and external sources from organizations in order to help to improve the knowledge of the managers, as well as the decision making process. The competitive advantage is created by better and greater understanding of the data. It focuses on business and gathers three types of analysis: descriptive, predictive and prescriptive. Data Mining is recognized as computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Business Intelligence is the hot topic among all industries aiming for relevance. BI emphasizes on detail integration and organizing of data. DM and BA work together to process and analyse data to lighten workload for the user and organization and hence in understanding discovered materials. Predictive algorithm hence plays extensive role in enhancing BI.

References

• “Big Data Analytics”-Mingmin Chi, Member, IEEE, Antonio Plaza
• Big Data by Viktor Mayer-Schonberger
• Arti J. Ugale, P. S. Mohod, "Business Intelligence Using Data Mining Techniques on Very Large Datasets", International Journal of Science and Research (IJSR), Volume 4 Issue 6, June 2015 , pp- 2932-2937
• Prachiagarwal, "Benefits and Issues Surrounding Data Mining and its Application in the Retail Indu stry" , International Journal of Scientific and Research Publications, Volume 4, Issue 7, July 2014.
• Jiawei Han, MichelineKamber and Jian Pei, “Data Mining: Concepts and Techniques”. Third Edition, Morgan Kaufmann Publishing, USA, 201 I.
• R.Sharda, D.Adomako and N.Ponna, “Business Analytics: Research and Teching Perspectives”, 35th Int. Conf. on Information Technology Interfaces, June 24-27, 2013, Cavtat, Croatia.
• S. LaValle, E. Lesser, R. Shockley, M.S. Hopkins and N. Kruschwitz, “Big data, analytics and the path from insights to value”, MIT sloan management review, vol.21, 2013.
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). “Business Intelligence and Analytics: From Big Data to Big Impact”. MIS quarterly, 36(4), 1165-1188.
• Meryem Ouahilal; Mohammed El Mohajir; Mohamed Chahhou; Badr Eddine El Mohajir “A comparative study of predictive algorithms for business analytics and decision support systems: Finance as a case study”, 2016 International Conference on Information Technology for Organizations Development (IT4OD).
• S. Kishore Babu; S. Vasavi; K. Nagarjuna, “Framework for Predictive Analytics as a Service Using Ensemble Model”, 2017 IEEE 7th International Advance Computing Conference (IACC)