The Impact of Business Analytics on Education Sectors-A Study

Authors

  • Nalini V. Assistant Professor, Faculty of Management Studies, DR MGR EDUCATIONAL AND RESEARCH INSTITUTE, Maduravoyal, Chennai. Author
  • Bagirathi M. Assistant Professor, Faculty of Management Studies, DR MGR EDUCATIONAL AND RESEARCH INSTITUTE, Maduravoyal, Chennai. Author

DOI:

https://doi.org/10.61841/c19eb612

Keywords:

Business analytics, Grounded Theory, Success Factors, Appreciative Inquiry, Framework, Business Analytics, Education and Training

Abstract

Business analytics is believed to be an enormous boon for organizations, as well as in emerging education sectors. Since it helps offer timely insights over the competition, helps optimize business processes, and helps generate growth and innovation opportunities. As organizations start their business analytics initiatives, many strategic questions, just like the thanks to operationalize business analytics so on drive the foremost value, arise. Recent Information Systems (IS) literature has focused on explaining the role of business analytics and thus the necessity for business analytics. However, little or no attention has been paid to understanding the theoretical and practical success factors related to the operationalization of business analytics. The primary objective of this study is to fill that gap within the IS literature by empirically examining business analytics success factors and exploring the impact of business analytics on education sectors. Through a qualitative study, we gained deep insights into the success factors and consequences of business analytics. Our research informs and helps shape possible theoretical implementations of business analytics. 

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References

1. Anderson-Lehman, R., Watson, H.J., Wixom, B.H., and Hoffer, J.A. (2004), “Continental Airlines Flies High with

real-time business intelligence”, MIS Quarterly Executive, Vol. 3 No. 4, pp. 163-76. APICS

(2012),"APICS2012bigdatainsightsandinnovationsexecutivesummary,available at: www.apics.org/docs/industry-content/apics-2012-big-data-executive-summary.pdf (accessed August 30, 2014).

2. Bean (2014), “Big data fatigue?”, MIT Sloan Review Blog, June 23, available at:

http://sloanreview.mit.edu/ article/big-data-fatigue/ (accessed September 30, 2014).

3. Chen, H., Chiang, R.H. and Storey, V.C. (2012), “Business intelligence and analytics: from big data to big

impact”, MIS Quarterly, Vol. 36 No. 4, pp. 1165-88. Cross, J. (1996), “Training vs education: a distinction

that makes a difference”, Bank Securities Journal, available at:

www.internettime.com/Learning/articles/training.pdf (accessed July 15, 2014).

4. Davenport, T.H. and Harris, J.G. (2007), competing on analytics: The New Science of Winning, Harvard

Business Press, and Boston, MA. Davenport,T.H.,Barth,P.andBean,R.(2012), “How ‘bigdata

‘isdifferent”,MITSloanManagementReview, Vol. 54 No. 1, pp. 43-6.

5. Davenport, T.H., Harris, J.G. and Morison, R. (2010), Analytics at Work: Smarter Decisions, Better

Results, Harvard Business Press. Department for Business-Innovation and Skills (2013), “Seizing the data

opportunity: a strategy for UK data capability”, available at:

www.gov.uk/government/uploads/system/uploads/attachment_data/file/34764/12p120c-guide-to-bis-2012-

2013.pdf (accessed August 17, 2014).

6. Katharaki, M., Prachalias, C., Linardakis, M. and Kioulafas, K. (2009), “Business administration training

seminar for public sector executives: implementation and evaluation”, Industrial and Commercial Training,

Vol. 41 No. 5, pp. 248-57.

7. Kiron, D. (2013), “Organizational alignment is key to big data success”, MIT Sloan Management Review,

Vol. 54 No. 3, pp. 1-n/a. Langley, J.C.J. (2014), “2014 Third-Party logistics study: the state of logistics

outsourcing”, Capgemini Consulting, 56pp, avilable at: www.capgemini.com/resource-fileaccess/resource/pdf/3pl_study_report_ web_version.pdf (accessed August 27, 2014).

8. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S. and Kruschwitz, N. (2011), “Big data, analytics: and

the path from insights to value”, MIT Sloan Manage. Rev., Vol. 52 No. 2, pp. 21-. Manyika, J.,

9. Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), “Big data: the next

frontier for innovation, competition (p 9) and productivity”, technical report, McKinsey Global Institute.

10. Mithas,S.,Lee,M.R.,Earley,S.,Murugesan,S.andDjavanshir,R.(2013), “Leveragingbigdataandbusiness

analytics”, IT Professional, Vol. 15 No. 6, pp. 18-20. Provost, F. and Fawcett, T. (2013), “Data science and

its relationship to big data and data-driven decision making”, Big Data, Vol. 1 No. 1, pp. 51-9.Rao, M.S. (2014),

11. “Enhancing employability in engineering and management students through soft skills”, Industrial and Commercial Training, Vol. 46 No. 1, pp. 42-8.Shah, S., Horne, A. and Capellá, J. (2012), “Good data won’t guarantee good decisions”, Harvard Business Review, Vol. 90 No. 4, pp. 23-5.

12. Sharma, R., Mithas, S. and Kankanhalli, A. (2014), “Transforming decision-making processes: a

researchagendaforunderstandingtheimpactofbusinessanalyticsonorganizations”,EuropeanJournalofInformati

on Systems, Vol. 23 No. 4, pp. 433-41.

13. Tweney, D. (2013), “Walmart scoops up Inkiru to bolster its ‘big data’ capabilities online”, available at:

http:// venturebeat.com/2013/06/10/walmart-scoops-up-inkiru-to-bolster-its-big-data-capabilities-online/

(accessed August 11, 2014). Waller, M.A. and Fawcett, S.E. (2013), “Data science, predictive analytics,

and big data: a revolution that will transform supply chain design and management”, Journal of Business

Logistics, Vol. 34 No. 2, pp. 77-84.

14. Wilkins, J. (2013), “Big data and its impact on manufacturing”, available at: www.dpaonthenet.net/article/

65238/Big-data-and-its-impact-on-manufacturing.aspx (accessed August 13, 2014). Wixom, B., Yen, B.

and Relich, M. (2013), “Maximizing value from business analytics”, MIS Quarterly Executive, Vol. 12 No.

2, pp. 37-49.

15. Huang, T.C.K., Liu, C.C. and Chang, D.C. (2012), “An empirical investigation of factors influencing the

adoption of data mining tools”, International Journal of Information Management, Vol. 32 No. 3, pp. 257-

70.

16. Lycett, M. (2013), “‘Datafication’: making sense of (big) data in a complex world”, European Journal of Information Systems, Vol. 22 No. 4, pp. 381-6. Nagle, T. and Sammon, D. (2014), “Big data: a framework for research”, in Phillips-Wren, G. et al. (Eds), DSS 2.0-Supporting Decision Making with New

Technologies, Vol. 261, IOS Press, pp. 395-400.

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Published

31.05.2020

How to Cite

V. , N., & M. , B. (2020). The Impact of Business Analytics on Education Sectors-A Study. International Journal of Psychosocial Rehabilitation, 24(3), 3739-3750. https://doi.org/10.61841/c19eb612