The role of artificial intelligence and macnine learning in business intelligence
https://doi.org/10.53360/2788-7995-2023-4(12)-4
Abstract
This article explores how Artificial Intelligence (AI) and Machine Learning (ML) are changing the way businesses use data. In a world where data is super important, many companies are using AI and ML to make the most of their data. This study looks at how AI and ML are being used in Business Intelligence (BI), which is all about collecting and analyzing data to help businesses make smart decisions. First, we look at the old way of doing BI and how it couldn't handle the huge amount of data we have today. Then, we see how AI and ML are being used to solve this problem. These technologies help by automatically processing data, predicting future trends, and finding important information in big piles of data. We also check out some real-life examples from different industries to see how AI and ML are helping companies make better decisions. These examples show how businesses can get more accurate data, make decisions faster, and predict things better by using AI and ML in their BI. We also talk about some challenges and things we need to think about when using AI and ML in BI, like making sure we use these technologies in a responsible and fair way. In summary, this research shows that AI and ML are not just tools, but they're changing the way we do BI. By using these technologies, companies can get better insights from their data, stay competitive, and take their BI to the next level.
About the Authors
M. M. AbalkanovKazakhstan
Miras Maratovich Abalkanov – master's degree
010000, Astana, Mangilik El avenue, 55/11
G. A. Abitova
Kazakhstan
Gulnara Askerovna Abitova – PhD, Associate Professor
010000, Astana, Mangilik El avenue, 55/11
References
1. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
2. Ward, J. S., & Barker, A. (2013). Undefined by data: A survey of big data definitions. arXiv preprint arXiv:1309.5821.
3. Gartner. (2020). Magic Quadrant for Analytics and Business Intelligence Platforms. Retrieved from [Gartner Research Database].
4. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
5. Kimball, R., Ross, M., Becker, B., Mundy, J., Thornthwaite, W., & Adamson, C. (2013). The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. Wiley.
6. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
7. Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business Press.
8. Janssen, M., Wimmer, M. A., & Deljoo, A. (2015). Policy practice and digital science: Integrating complex systems, social simulation, and public administration in policy research. Public Administration, 93(4), 956-972.
9. Eckerson, W. (2020). The BI elephant in the room: Practical tips for becoming a data-driven organization. TDWI Best Practices Report.
10. Marr, B. (2015). Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance. John Wiley & Sons.
Review
For citations:
Abalkanov M.M., Abitova G.A. The role of artificial intelligence and macnine learning in business intelligence. Bulletin of Shakarim University. Technical Sciences. 2023;1(4(12)):25-30. https://doi.org/10.53360/2788-7995-2023-4(12)-4