These days, business intelligence has witnessed the various challenges of big data analytics due to exponentially growing information with uncertainty existing in the market. To effectively analyze the large amount of data from various sources in business, algorithms of artificial intelligence techniques should be efficiently improved. We present a comprehensive and novel approach for the evaluation of outcome by using the interactions of features and then applying this approach to estimate the trend by jointly modelling features in management data. In terms of predictive accuracy, the proposed method outperformed machine learning methods such as regression, penalized regression, decision tree, random forest and k-nearest neighbors in the high-dimensional business data analysis. Data-preprocessing was used to curate the data for better prediction and network analysis was conducted to appropriately visualize and analyze the data analysis results. The business literature represents that investigating artificial intelligence techniques with theoretical ideas for big data analytics can have an impact on reducing costs and risks in management. Future directions have been devised to elucidate the gap between actual values in real-world data of business intelligence and predicted values by the proposed approach. Machine learning methods including features of demographic and strategic data can estimate the effect of marketing characteristics. Using the proposed method, businesses may better assess strongly correlated features with the target output in the similarly structured business data.
저자
Donggeun Kim [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
Juyong Ko [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
Minho Sun [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
Jai Woo Lee [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
