The Gap Between Practice in Business Intelligence and Theory
Overview
This paper examines the divergence between theoretical frameworks and practical applications of Business Intelligence (BI) in modern organizations. It discusses how BI systems and data mining techniques are being implemented by businesses to optimize performance and decision-making, while analyzing the growing gap between established theory and evolving real-world practice in the field.
What this essay covers
- Introduction to Business Intelligence and its role in competitive advantage
- Data mining as a key BI theory and its applications in business practice
- The widening gap between BI theory and practice due to technological advancement and digital transformation
- Impact of automation on human labor versus efficiency gains in data analysis
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Business Intelligence and Theory
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The Gap Between Practice in Business Intelligence and Theory
Business Intelligence (BI) refers to practices, applications, technologies that help in the running of a business, and presentation of information regarding a business. By the optimization that leads to performance, a company can be in a position to be competitive in a market that is transformative as a business can be ready for any challenge. Analyzed data shows a valuable asset that is vital to managers and other people that make decisions. In recent times, businesses have been applying the BI system to make data meaningful (Trieu, 2017). There is still a gap in the way the theories are used as much as there is growth in the area of business intelligence. Data mining is a significant theory that is used in BI.
Data mining is a significant phrase that is applied in the area of BI. Data mining refers to the analysis, including examination of large amounts that have data to know essential rules with their models. Companies use data mining as a method of promoting client support systems, sales, and marketing by getting to understand clients more. As a business expands, the clientele base keeps growing (Tan et al., 2016). In order to make sense with the massive amounts of data, data mining methods have been developed to assist in bringing to sense about the functions of data mining.