Bridging the Gap between Theory and Practice in Business Intelligence
Analyze the Gap between Theory and Practice in Business IntelligenceThe journal article listed in this week’s resources provides a supplemental article that examines current theory and practice of BI.Conduct some more research on existing theories in business intelligence (BI). What common theories exist? Have the theories evolved over time? Now, research how these theories are used in practice. You will likely have to include business journals and practical application of the concepts. Choose at least one major theory for further gap analysis. Write an assessment outlining the following:1. Your explanation of the theory that was chosen.2. Your analysis of the gap between theory and practice (industry literature can help show the state of practice and help you determine the gaps compared with the scholarly literature)3. Outline the root cause of this gap. Include the effect of the gap. Whom or what does it impact? Is it a positive or negative impact?Support your paper with a minimum of three scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.Length: 5 pages, not including title and reference pages
Your paper should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University's Academic Integrity Policy.
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 hardware and software, and the revolution in the world-wide-web have enabled organizations to generate, collect, store, process, analyze, distribute and utilize data at a high rate. According to Radhakrishnan, Shineraj and Muhammed (2013), more people are using the Internet to make purchases and conduct banking. The increased use of the Internet and e-commerce has accorded businesses and governmental agencies a chance to gather and analyze information in ways not thought of in the past. For a long time, consumer data has been available through offline sources such as transactions done through credit cards, phone orders and a range of other traditional means. The digital revolution has, however increased the efficiency and effectiveness through which an organization can collect and utilize such information (Radhakrishnan, Shineraj & Muhammed, 2013).
In theory, most organizations are fully aware that by controlling their data resources to build and deploy data mining technologies to better their decision-making capabilities, they can acquire and sustain a competitive advantage. If deployed in the correct manner, data mining offers organizations an important tool through which to make decisions that can help in resource allocation and look for new ways through which to transform data into valuable knowledge. Deploying data mining in the correct way has the capability of greatl...