Modeling the prediction of business intelligence system effectiveness

Area of Science:

• Information Systems
• Business Analytics
• Enterprise Management

Background:

• Business intelligence (BI) technologies are crucial for enterprises in dynamic environments.
• Effective management of BI solutions is vital for organizational success.
• Predicting BI system effectiveness (BISE) is necessary for optimizing BI implementation.

Purpose of the Study:

• To develop models and rules for predicting BISE.
• To identify critical attributes influencing BISE.
• To enable enterprises to self-evaluate BI solution effectiveness.

Main Methods:

• Constructed prediction models and rules for BISE.
• Utilized decision tree structures to derive classification and prediction rules.
• Employed logistic regression analysis to build a refined prediction model.

Main Results:

• Identified critical prediction indicators for BISE and BI performance forecasting.
• Highlighted five classification and prediction rules for BISE using decision trees.
• Developed a regression model with four indicators for BISE prediction.

Conclusions:

• The study provides a framework for enterprises to improve BISE.
• The findings aid in effectively managing BI solution implementation.
• The developed models and rules cater to both practical management and academic theory.