Determining the best business intelligence solution according to user requirements

Abstract

When selecting a new Business Intelligence (BI) solution, an important factor to consider must be what type of solution is going to be implemented. There are three main categories to choose from; BI On-Premise, BI Cloud, and BI Hybrid. For each category, there are different elements that affect which solution is most suitable. In this study, different characteristics of a business were identified which might affect the decision of which BI implementation an organisation should go for. By considering these aspects, the most suitable implementation can be chosen. A tool was developed which guides businesses on which type of BI implementation they should choose. The main objectives of this study were to build a model for each BI architecture to better understand the main differences between each architecture. Then, the main characteristics that might affect the decision for which BI implementation to choose, were established after reviewing various research papers. Moreover, a dataset was generated using a method developed by Misra and Mondal. Another dataset was also generated through a questionnaire were the questions reflected the attributes to be passed through the classification algorithms. These generated datasets were added together to form one whole set. Then, the k-fold cross-validation was used to split this set into different training and testing datasets. These were used to train and test the implemented decision tree and SVM algorithms. From the testing sets, the accuracy of the classification algorithms could be evaluated. When the classification algorithms built were tested using the testing datasets mentioned above, good results in terms of the number of correctly predicted records were obtained. In most cases, whenever a record was incorrectly predicted, there weren’t any two-level jumps. This means that if a testing record was labelled as On-Premise and was predicted incorrectly, the predicted value was Hybrid BI. This is significant because there is a much higher jump from On-Premise to Cloud then from On-Premise to Hybrid. The same goes for records in the testing set labelled as Cloud BI that were predicted wrongly.