Abstract
Currently, business processes are perceived not merely as a sequence of activities responding to an event to generate output, but as a complex system involving the interplay of individuals, technologies, strategies, and business rules to attain certain business outcomes. Consequently, the analysis of a substantial volume of data is essential not only for present operations and several years ahead but also for future trends and long-term objectives. This study aims to present the concept of data analytics within the business domain, integrating it with a business framework specifically for operational purposes, and incorporating machine learning for predictive analytics, culminating in the evaluation of classification predictions. Information is a crucial asset that enables future business planning through a data-driven methodology and demonstrates the importance of business analytics for future success.
Metadata
| Item Type: | Article |
|---|---|
| Creators: | Creators Email / ID Num. Mohamed Yusoff, Syarifah Adilah syarifah.adilah@uitm.edu.my Johan, Elly Johana ellyjohana@uitm.edu.my Warris, Saiful Nizam saifulwar@uitm.edu.my Othman, Jamal jamalothman@usm.edu.my |
| Contributors: | Contribution Name Email / ID Num. Advisor Abd Rahman, Nor Hanim UNSPECIFIED Chief Editor Othman, Jamal UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Analysis > Analytical methods used in the solution of physical problems |
| Divisions: | Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus |
| Journal or Publication Title: | Merging Lanes: Where E-Learning Diversity Meets Future Trends |
| ISSN: | 978-629-98755-9-8 |
| Volume: | 11 |
| Page Range: | pp. 162-171 |
| Keywords: | Business process, Data analytics, Machine learning |
| Date: | April 2026 |
| URI: | https://ir.uitm.edu.my/id/eprint/139361 |
