Data Visualization Tools for Strategic Business Decisions
-
DOI:
https://doi.org/10.67228/3071642X/IJCFDE-2024PI2V6UPublished 02-05-2024
Data Visualization, Business Intelligence, Strategic Decision Making, Interactive Dashboards, Visual Analytics, Predictive Analytics, Business Analytics, Decision Support Systems, Artificial Intelligence, Big Data Issue
Section
ArticlesHow to Cite
Mandelbrot, B., & Pouzin, L. (2024). Data Visualization Tools for Strategic Business Decisions. International Journal of Commerce, Finance and Digital Economy, 7(1), 01-13. https://doi.org/10.67228/3071642X/IJCFDE-2024PI2V6UAbstract
Data visualization has become an essential element in business intelligence, converting complex data into easy-to-understand charts and visuals that enable informed business decisions and strategic planning. Different organizations in the data-intensive industry create vast amounts of structured and unstructured data from enterprise systems, customer interaction, financial transactions, supply chain operations, and digital platforms, etc. in highly competitive environments. While traditional analytical approaches may falter at capturing these disparate, unstructured data sets efficiently, advanced visualization technology can help executives uncover patterns in the data, track company performance, identify emerging market trends, and forecast future business opportunities. Modern visualization solutions utilize AI, machine learning, cloud computing, and interactive dashboards to deliver real-time business insight, optimizing operations and organizational agility. This study aims to explore the importance of the data visualization tools in helping in the decision for strategic business context by analyzing the current technologies of visualization, the business intelligence frameworks and the analytical methods. The paper traces the history of visualization techniques, reviews the predominant business visualization platforms and suggests a conceptual model for visualization based strategic decision support. Moreover, the study identifies the benefits of interactive dashboards, predictive analytics, and visual stories to enhance executive decision quality. The results show that the use of effective visualization can increase the accuracy of data interpretation, ease decision making process, promote organizational responsiveness, and boost the competitive advantage. Advanced data visualization solutions therefore play a vital role in the implementation of BI ecosystems for sustainable organizational growth, innovation and evidence-based management decision making.
References
[1] S. Few, Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2nd ed. Burlingame, CA, USA: Analytics Press, 2013.
[2] C. Ware, Information Visualization: Perception for Design, 4th ed. Burlington, MA, USA: Morgan Kaufmann, 2021.
[3] B. Shneiderman, "The eyes have it: A task by data type taxonomy for information visualizations," in Proc. IEEE Symp. Visual Languages, Boulder, CO, USA, 1996, pp. 336–343.
[4] D. A. Keim, "Information visualization and visual data mining," IEEE Transactions on Visualization and Computer Graphics, vol. 8, no. 1, pp. 1–8, Jan.–Mar. 2002.
[5] J. Thomas and K. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics. Richland, WA, USA: Pacific Northwest National Laboratory, 2005.
[6] T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning. Boston, MA, USA: Harvard Business School Press, 2007.
[7] E. Turban, R. Sharda, D. Delen, and J. E. Aronson, Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 11th ed. New York, NY, USA: Pearson, 2021.
[8] M. Chen, S. Mao, and Y. Liu, "Big data: A survey," Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, Apr. 2014.
[9] D. J. Power, Decision Support, Analytics, and Business Intelligence, 3rd ed. New York, NY, USA: Business Expert Press, 2019.
[10] T. Munzner, Visualization Analysis and Design. Boca Raton, FL, USA: CRC Press, 2014.
[11] C. Stolper, A. Perer, and D. Gotz, "Progressive visual analytics: User-driven visual exploration of in-progress analytics," IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1653–1662, Dec. 2014.
[12] G. Ellis and A. Dix, "A taxonomy of clutter reduction for information visualisation," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1216–1223, Nov.–Dec. 2007.
[13] F. Cabitza, D. Ciucci, and R. Rasoini, "A giant with feet of clay: On the validity of machine learning in medicine," Big Data & Society, vol. 4, no. 2, pp. 1–6, 2017.
[14] D. Gunning and D. Aha, "DARPA's Explainable Artificial Intelligence (XAI) Program," AI Magazine, vol. 40, no. 2, pp. 44–58, Summer 2019.
[15] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2012.
Downloads
How to Cite
Mandelbrot, B., & Pouzin, L. (2024). Data Visualization Tools for Strategic Business Decisions. International Journal of Commerce, Finance and Digital Economy, 7(1), 01-13. https://doi.org/10.67228/3071642X/IJCFDE-2024PI2V6U
Similar Articles
- N. Seshagiri, Anti-Money Laundering (AML) Systems Using AI , International Journal of Commerce, Finance and Digital Economy: Vol. 8 No. 1 (2025)
- H. N. Mahabala, AI-Powered Automation in Digital Workforce Management , International Journal of Commerce, Finance and Digital Economy: Vol. 5 No. 1 (2022)
- Raj Chandra Bose, Smart Contract Applications in Business Agreements , International Journal of Commerce, Finance and Digital Economy: Vol. 8 No. 1 (2025)
- Dr. Sneha Banerjee, Innovative FinTech Models for Future Financial Services , International Journal of Commerce, Finance and Digital Economy: Vol. 2 No. 1 (2019)
- Marco Bianchi, Supply Chain Resilience Strategies in the Digital Era , International Journal of Commerce, Finance and Digital Economy: Vol. 1 No. 2 (2018)
- Andrey Ershov, Alexey Lyapunov, Product Recommendation Engines Using Machine Learning , International Journal of Commerce, Finance and Digital Economy: Vol. 7 No. 2 (2024)
- Dr. Rajesh Kumar Sharma, Data-Driven Pricing Strategies in Online Retail Markets , International Journal of Commerce, Finance and Digital Economy: Vol. 2 No. 1 (2019)
- Arvind Menon, AI-Based Credit Risk Evaluation in FinTech Applications , International Journal of Commerce, Finance and Digital Economy: Vol. 2 No. 2 (2019)
- N. Seshagiri, H. N. Mahabala, Predictive Modeling for Stock Market Volatility , International Journal of Commerce, Finance and Digital Economy: Vol. 6 No. 1 (2023)
- Dr. John Maseko, Impact of Personalized Marketing on Buyer Decision-Making , International Journal of Commerce, Finance and Digital Economy: Vol. 9 No. 1 (2026)
You may also start an advanced similarity search for this article.