Vol: 13, Issue: 3 2023

Page: 170-177

Employability of Big Data Tools and Techniques in Catalyzing an Effective Business Transformation

Vibhu Goel

Received Date: 2023-06-25

Accepted Date: 2023-09-08

Published Date: 2023-09-23

http://doi.org/10.37648/ijtbm.v13i03.013

Over the past decade, big data analytics (BDA) matured from promise to practice, reshaping how firms sense opportunities, decide, and deliver value. Synthesizing peer-reviewed work from 2012–2021, this paper explains how BDA capabilities (data, technology, talent, governance, and culture) convert into operational excellence, enhanced customer experience, and new business models. We ground the discussion in the resource-based and dynamic capabilities views, and a socio-technical lens, and distill evidence across healthcare, manufacturing, marketing/retail, and the public sector. Comparative analyses show BDA outperforming traditional business intelligence (BI) when environmental dynamism is high and when firms orchestrate complementary organizational changes. We also catalogue risks—data quality, privacy, algorithmic bias, and adoption barriers—and outline mitigations. We conclude with a research agenda on measurable value pathways, capability micro foundations, responsible AI, and sector-specific playbooks.

References

• Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & 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. https://doi.org/10.1016/j.ijpe.2016.08.018
• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
• Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114– 126. https://doi.org/10.1037/xge0000033
• Escobar, C. A., Morales-Menendez, R., & Morales-Menendez, A. (2021). Quality 4.0: A review of big data challenges in manufacturing. Journal of Intelligent Manufacturing, 32(1), 231– 252. https://doi.org/10.1007/s10845-021-01765-4
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
• Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
• Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191– 209. https://doi.org/10.1016/j.jsis.2017.07.003
• Khanra, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Big data analytics in healthcare: A systematic literature review. Enterprise Information Systems, 14(7), 878–912. https://doi.org/10.1080/17517575.2020.1812005
• Martin, K. (2015). Ethical issues in the big data industry. MIS Quarterly Executive, 14(2), 67–85.
• Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261– 276. https://doi.org/10.1016/j.jbusres.2019.01.044
• Moyne, J., Qamsane, Y., Balta, E. C., Kovalenko, I., Faris, J., Barton, K., & Tilbury, D. M. (2020). A requirements driven digital twin framework: Specification and opportunities. IEEE Transactions on Automation Science and Engineering, 17(4), 1725–1743. https://doi.org/10.1109/TASE.2020.2970930
• National Institute of Standards and Technology. (2015). NIST big data interoperability framework: Volume 1, definitions (NIST Special Publication 1500-1). https://doi.org/10.6028/NIST.SP.1500-1
• Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
• Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2, 3. https://doi.org/10.1186/2047-2501-2-3
• Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a multi-industry context. Technological Forecasting and Social Change, 149, 119781. https://doi.org/10.1016/j.techfore.2019.119781
• Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
• Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626– 639. https://doi.org/10.1016/j.ejor.2017.02.023
• Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23– 45. https://doi.org/10.1080/21693277.2016.1192517
• Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 2053951714559253. https://doi.org/10.1177/2053951714559253