Machine Learning in Business Intelligence

Abstract

The purposed of this research have the fivefold: First We did in-depth deliberate studies on Business Intelligence (BI) and Machine Learning (ML) technology to provide overview of the state-of-art of its scopes and challenges. Second, to understandand identifyknowledge gaps BI that theproblems of business can be solve in optimal through thetechniquesand algorithms of ML. Third, we have developed conceptual framework of Adaptive Business Intelligence Model to solve the problems of businesses by incorporating a characteristics, techniques, algorithms and technology of machine learning. The key concept was to developed business intelligence machine(BIM)which was the whole system of adaptive business intelligence models in-order toautomate business processes, intelligent decision makingand obtain optimal business desiregoal. Fourth, a systematic review was done on Business Intelligence and Machine Learning along with their chronological and evolutionof terminologies, technologies, techniques, algorithms, classifications, analytics, capabilities and Business Intelligence Machine(BIM)applications. Fifththepaper proposedAdaptiveBusinessIntelligencemodel whichisthesolutiontoexisting business intelligence. Machine learning engine is the heart and soul component of adaptive business intelligence model. Business intelligence machine can solve real world business problems with minimal human involvement, more accurate, more speed, quick response and better decision making in business as compare to existing of business intelligence systems functionalities. In this research paper, we have open look_forward ways to future research directions on the related areas of Business Intelligence from non_adaptive model to adaptive BI model using machine learning technology to solve real world business problems. Different machine learning techniques and algorithms can overcome the existing challenges of business intelligence systems.