A Comparative Study of Data Mining Tools and Techniques for Business Intelligence

Abstract

Business intelligence (BI) is a collection of different frameworks and tools that convert the required raw data into meaningful information which may aid in supporting the decision-making process of the management. The present-day BI gives a reporting functionality to the identification of data groups, i.e., clusters useful for data mining techniques and business performance maintenance with predictive analysis in real-time BI applications. In fact, the core function of BI is to support the effective decision-making process. The BI frameworks are often known to business clients as decision support systems (DSSs) or reality-based supporting systems that they utilize to analyze the data and extract information from data sources. Through this research work, the authors aim to discuss various tools, approaches, and techniques for data mining that has support for BI. The research work also aims to describe the study as processes and procedures to systematically identify, counter, store, analyze, and explore data accessibility for making effective operations in business decisions. Different algorithms, methods, and techniques of BI are also highlighted along with varied types of applications with preferable implementation. The later part of the study discusses BI applications, which include operations of decision supporting frameworks, data management frameworks, query and reporting with online analytical processing (OLAP), forecasting apart from statistical analysis used in distributed BI applications. This research work uses visualized charts to explain the usage frequency of BI techniques with their performance comparison.