The Impact of Business Analytics on Education Sectors-A Study
DOI:
https://doi.org/10.61841/c19eb612Keywords:
Business analytics, Grounded Theory, Success Factors, Appreciative Inquiry, Framework, Business Analytics, Education and TrainingAbstract
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.
Downloads
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.