Optimizing OLAP Cube for Supporting Business Intelligence and Forecasting in Banking Sector

• Main Authors: Sonali Mathur, Shankar Lal Gupta, Payal Pahwa
• Format: Article
• Language: fas
• Published: University of Tehran 2021-01-01
• Series: Journal of Information Technology Management
• Subjects: on-line analytical processing; business intelligence; business forecasting; data warehouse, decision making
• Online Access: https://jitm.ut.ac.ir/article_80026_1e2f1a1cc187c1f04e0950d7a2c4927d.pdf

The data stored in data warehouse is used for making strategic decisions by integrating heterogeneous data from multiple sources at a single storage place, where data is used for querying and analysis purposes. With the advancement in the technology, Business Analytics and Business intelligence are being increasingly used in the financial sector for forecasting business decisions. Many On-Line Analytical Processing (OLAP) tools are being largely explored that can contribute to business decision making. Banking operation handles a lot of data as they operate daily. Subsequently, preparing of this tremendous volume of information requires instant and quick tools that can process the information at high processing speeds. Through this research paper, we represent the OLAP cube as one of the tools which can be used for business analysis. A case study of a bank and loan approval process is considered as one of the areas for implementation and analysis of business decisions using business intelligence which can serve as a key factor for increasing intelligence in the banking sector to make reliable business decisions. Higher management can forecast and predict various outcomes from the bank data warehouse using On-Line Analytical Processing technology which provided a multidimensional view of the data. Analysts can make business decisions by analyzing the reports and pattern trends in the graphs. Management can modify existing policies and procedures to increase the growth of the bank and can have a healthy competition with their competitors.