DOI QR코드

DOI QR Code

Influence of Business Analytics Usage on Operational Efficiency of Information Technology Infrastructure Management

  • Elangovan N (School of Business and Management, Christ University) ;
  • Ruchika Gupta (School of Business and Management, Christ University) ;
  • Sundaravel, E (School of Business and Management, Christ University)
  • Received : 2021.08.07
  • Accepted : 2022.01.19
  • Published : 2022.03.31

Abstract

Organizations today depend and thrive on timely, accurate and strategically relevant information. Business analytics (BA) holds the key to many of these issues. This paper validates a model on how the usage of BA leads to operational efficiency. We identified the factors of basic analytical usage from the Business Capacity Maturity Model (BCMM). The scope of the study is restricted to the Information Technology Infrastructure and Application management domain. A survey was conducted among the managers of the IT companies in Bengaluru, India. The results showed a significant influence of data-oriented culture and BA tools and infrastructure on BA usage. We found a significant influence of BA usage and pervasive use on operational efficiency. The speed to insight is still not practised in organizations. The awareness level of analytical skills in organizations is very low.

Keywords

References

  1. AIMResearch. (2021). Analytics India Industry Study 2021. Retrieved December 27, 2021, from https://analyticsindiamag.com/analytics-india-industry-study-2021/.
  2. Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., and Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, 113-131.
  3. Anand, A., Sharma, R., and Coltman, T. (2016). Realizing value from business analytics platforms: The effects of managerial search and agility of resource allocation processes. 37th International Conference on Information Systems, ICIS 2016 (pp. 1-12). United States: Association for Information Systems.
  4. Bayram, N. (2018). Examining the use of BA in organizations: an extension of the technology acceptance model (Master's thesis, Middle East Technical University).
  5. Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1),169-196.
  6. Burton-Jones, A., and Grange, C. (2013). From use to effective use: A representation theory perspective. Information Systems Research, 24(3), 632-658.
  7. Burton-Jones, A., and Straub Jr, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246.
  8. Corte-Real, N., Ruivo, P., Oliveira, T., and Popovic, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of Business Research, 97, 160-173.
  9. Cosic, R., Shanks, G., and Maynard, S. (2012, January). Towards a Business Analytics capability maturity model. In ACIS 2012: Proceedings of the 23rd Australasian Conference on Information Systems 2012 (pp. 1-11). ACIS.
  10. Davenport, T. (2018). DELTA plus model and five stages of analytics maturity: A primer. Retrieved from https://www.iianalytics.com/delta-plus-primer.
  11. Davenport, T. H. (2000). Mission critical: realizing the promise of enterprise systems. Boston: Harvard Business Press.
  12. Dines, R. (2011). What do you measure in your Infrastructure and Operation department? Forrester Research Communities. Retrieved October, 2013, From http://blogs.forrester.com/rachel_dines/11-04-11-the_essential_metrics_for_infrastructure _and_operations.
  13. Felix, B. M., Rodrigues, E. M. T., and Cavalcante, N. W. F. (2018). Critical success factors for Big Data adoption in the virtual retail: Magazine Luiza case study. Review of Business Management, 20(1), 112-126.
  14. Garg, A., Grande, D., Miranda, G. M. L., Sporleder, C., and Windhagen, E. (2017). Analytics in banking: Time to realize the value. Retrieved from https://www.mckinsey.com/~/media/mckinsey/industries/financial services/ourinsights/analyticsinbankingtimetorealizethevalue/analytics-inbanking-time-to-realize-the-value.pdf
  15. Gefen, D., and Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16(1), 91-109.
  16. Ghasemaghaei, M. (2019). Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems, 120(2), 14-24.
  17. Ghasemaghaei, M., Ebrahimi, S., and Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101-113.
  18. Holsapple, C., Lee-Post, A., and Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64(C), 130-141.
  19. Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., and Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513-538.
  20. Kiron, D., and Shockley, R. (2011). Creating business value with analytics. MIT Sloan Management Review, 53(1), 57-63.
  21. Klatt, T., Schlaefke, M., and Moeller, K. (2011). Integrating business analytics into strategic planning for better performance. Journal of Business Strategy, 32(6), 30-39.
  22. Kraan, W., and Sherlock, D. (2013). Analytics tools and infrastructure. JISC CETIS Analytics Series, 1(11), 1-24.
  23. Krishnamoorthi, S., and Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information and Management, 55(5), 643-666.
  24. Lautenbach, P., Johnston, K., and Adeniran-Ogundipe, T. (2017). Factors influencing business intelligence and analytics usage extent in South African organizations. South African Journal of Business Management, 48(3), 23-33.
  25. Mansell, I. J., and Ruhode, E. (2019). Inhibitors of business intelligence use by managers in public institutions in a developing country: The case of a South African municipality. South African Journal of Information Management, 21(1), 1-8.
  26. McKinsey. (2011). Big data: The next frontier for innovation, competition and productivity. Retrieved February, 2014, from www.mckinsey.com/mgi.
  27. Mendoza, R. A. (2019). Delivering internal business intelligence services: How different strategies allow companies to succeed by failing fast. In Aligning Business Strategies and Analytics , (pp. 157-176). Springer, Cham.
  28. Mitra, S., Sambamurthy, V., and Westerman, G. (2011). Measuring IT performance and communicating value. MIS Quarterly Executive, 10(1), 47-59.
  29. Montero, J. N. (2019). Determining business intelligence system usage success using the DeLone and McLean information system success model (Order No. 13811341). Available from ProQuest Dissertations and Theses Global. (2206337311). Retrieved from https://search.proquest.com/docview/2206337311?accountid=38885
  30. NASSCOM. (n.d.). Members Listing | NASSCOM. Retrieved December 28, 2021, from https://nasscom.in/members-listing
  31. Seddon, P. B., Constantinidis, D., Tamm, T., and Dod, H. (2016). How does business analytics contribute to business value?. Information Systems Journal, 27(3), 237-269.
  32. Sharma, A., and Sharma, T. (2017). HR analytics and performance appraisal system. Management Research Review, 45(2), 334-352.
  33. Sharma, R., Mithas, S., and Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of BA on organizations. European Journal of Information Systems, 23(4), 433-441.
  34. Vidgen, R., Shaw, S., and Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.
  35. Wang, Y., and Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299.
  36. Wixom, B. H., Yen, B., and Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive, 12(2), 111-123.