Bridging the Gap between Theory and Practice in Business Intelligence

Bridging the Gap between Theory and Practice in Business Intelligence
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Bridging the Gap between Theory and Practice in Business Intelligence
Business intelligence (BI) is a collective term used to denote different business managing approaches based on knowledgeable decisions, which bring about a greater level of performance within the organizations. Through the optimization of performance, a company can continue to exist and remain competitive in a transforming market, being flexible to new challenges (Jamaludin & Mansor, 2011). Corporate data symbolizes a valuable asset that is crucial for decision makers. Over the years, organizations have continued to rely on business intelligence approach to transform data into a high value insight. Despite the advancement of different theories in business intelligence, there exists a gap in the practice of these theories. One of the widely used theories in BI is data mining. This paper explains the data mining theory, analyzes the existing gap between theory and practice and outlines the root cause of the gap.
Data mining is one of the commonly used terms in BI. This term is used to refer to the examination and analysis of big quantities of data in order to recognize significant models and rules. Data mining is utilized by the organization as a means of enhancing its marketing, sales and client support operations through the creation of a better comprehension of its clients. As businesses continue to grow, the amount of data they handle also keeps on growing. In order to help in making sense of the large quantity of data, there has been development of data mining algorithms to help in the various data mining functions. In an industry where customers are free to change suppliers at a minimal cost, organizations are repeatedly using data mining to help with customer retention. By utilizing patterns to identify patterns in big groups of data, businesses can learn more about their clients and come up with efficient marketing strategies while at the same time increasing sales and minimizing on costs. For data mining to succeed there has to be efficient data collection and warehousing, as well as computer processing (Jayanthi, 2009).
Data mining has grown exponentially over the last fifteen years. US retailer Walmart was the first to use data mining in a bid to establish unnoticed patterns in shoppers purchasing patterns first used the concept. Using data mining, the company realized that it could sell more alcohol in the evenings by simply placing beer next to the baby products. This strategy worked as the mother usually asked fathers to pick up nappies on their way home after work. The men were more likely to pick up a crate of beer once they saw it at the supermarket. In its initial stages, data mining was a preserve of big corporations that had large reserves of data. However, the phenomenal growth of mobile phones, social networking and cloud computing in the following years, there has been an increase of unstructured data that could be hiding valuable information. Despite this increase of data, more businesses have the resources to make use of it (Jayanthi, 2009).
Gap between Theory and Practice
The technological advancements, reduced costs of har...