DOMAIN EXPERTISE IN DATA ANALYTICS: ENHANCING INSIGHTS ACROSS INDUSTRIES

Authors

  • Teena Choudhary Infosys Ltd, USA Author

Keywords:

Domain Expertise, Data Analysis, Industry-Specific Insights, Analytical Effectiveness, Cross-Sector Analytics

Abstract

This article investigates the critical role of domain knowledge in enhancing the effectiveness and accuracy of data analysis across various industries. Through a comprehensive examination of case studies in finance, healthcare, and retail sectors, we demonstrate how industry-specific expertise significantly improves data interpretation, metric selection, and the derivation of actionable insights. The article highlights the synergistic relationship between technical analytical skills and deep understanding of industry contexts, revealing that domain experts are better positioned to identify meaningful patterns, develop accurate predictive models, and generate more relevant insights for stakeholders. Furthermore, we provide practical strategies for data analysts to acquire and integrate domain knowledge into their analytical practices, including collaboration with subject matter experts, targeted industry research, and participation in sector-specific training. While acknowledging potential challenges such as cognitive biases and the rapid evolution of industries, this article underscores the importance of domain expertise in the increasingly complex landscape of data analysis. Our findings have significant implications for both aspiring data analysts seeking to specialize in specific industries and organizations aiming to optimize their analytical capabilities

References

D. Delen and H. Demirkan, "Data, information and analytics as services," Decision Support Systems, vol. 55, no. 1, pp. 359-363, 2013. [Online]. Available: https://doi.org/10.1016/j.dss.2012.05.044

J. S. Saltz and N. W. Grady, "The ambiguity of data science team roles and the need for a data science workforce framework," in 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 2355-2361. [Online]. Available: https://doi.org/10.1109/BigData.2017.8258190

S. Wamba, A. Gunasekaran, S. Akter, S. Ren, R. Dubey, and S. Childe, "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, vol. 70, pp. 356-365, 2017. [Online]. Available: https://doi.org/10.1016/j.jbusres.2016.08.009

F. Provost and T. Fawcett, "Data Science and its Relationship to Big Data and Data-Driven Decision Making," Big Data, vol. 1, no. 1, pp. 51-59, 2013. [Online]. Available: https://doi.org/10.1089/big.2013.1508

A. Kirilenko and A. Lo, "Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents," Journal of Economic Perspectives, vol. 27, no. 2, pp. 51-72, 2013. [Online]. Available: https://doi.org/10.1257/jep.27.2.51

K. Hao, "Doctors are using AI to triage covid-19 patients. The tools may be here to stay," MIT Technology Review, 2020. [Online]. Available: https://www.technologyreview.com/2020/04/23/1000410/ai-triage-covid-19-patients-health-care/

S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer, "Enterprise Data Analysis and Visualization: An Interview Study," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pp. 2917-2926, 2012. [Online]. Available: https://doi.org/10.1109/TVCG.2012.219

A. Grover and J. Leskovec, "node2vec: Scalable Feature Learning for Networks," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855-864. [Online]. Available: https://doi.org/10.1145/2939672.2939754

M. Kuhn and K. Johnson, "Applied Predictive Modeling," Springer, New York, NY, 2013. [Online]. Available: https://doi.org/10.1007/978-1-4614-6849-3

E. Brynjolfsson and K. McElheran, "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, vol. 106, no. 5, pp. 133-39, 2016. [Online]. Available: https://doi.org/10.1257/aer.p20161016

D. Kahneman, D. Lovallo, and O. Sibony, "Before You Make That Big Decision," Harvard Business Review, vol. 89, no. 6, pp. 50-60, 2011. [Online]. Available: https://hbr.org/2011/06/the-big-idea-before-you-make-that-big-decision

R. Feldman, M. Govindaraj, J. Livnat, and B. Segal, "Management's Tone Change, Post Earnings Announcement Drift and Accruals," Review of Accounting Studies, vol. 15, no. 4, pp. 915-953, 2010. [Online]. Available: https://doi.org/10.1007/s11142-009-9111-x

J. M. Kanter and K. Veeramachaneni, "Deep feature synthesis: Towards automating data science endeavors," in 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015, pp. 1-10. [Online]. Available: https://doi.org/10.1109/DSAA.2015.7344858

S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, "Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action," MIT Sloan Management Review, vol. 59, no. 1, pp. 1-17, 2017. [Online]. Available: https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

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Published

2024-10-15

How to Cite

Teena Choudhary. (2024). DOMAIN EXPERTISE IN DATA ANALYTICS: ENHANCING INSIGHTS ACROSS INDUSTRIES. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 7(2), 69-82. https://ijrcait.com/index.php/home/article/view/54