The Applications of Data Mining in Retail Industry Essay Example
Abstract
Data mining is one of the most important tools for analyzing information from large databases. The retail industry has recently seen the growing number of data mining applications in reducing time and cost for the industry. The paper defines data mining and the seven operations of data mining that have been classified through many different literatures. It then focus on the important applications of data mining in retail industry including marketing, customer relationship management, risk management, fraud detection and retail inventory management. Further discussion of the challenges associates with data mining enable an understanding of the responsibilities of organization and the risks of ignoring these challenges.
Introduction
Data might be one of the most valuable assets of any corporation – but only if it knows how to reveal valuable knowledge hidden in raw data. Data mining allows business to extract these diamonds of knowledge from historical data to predict outcomes of future situations. In modern day, data mining is being used by various industries including banking & finance, retail, health care, insurance, etc (Bhasin, 2006). Several recent trends have increased the interest in data mining, including the declining cost of data storage, increasing ease of collecting data, development of efficient machine-learning algorithms to process data and better computational power (Hormozi & Giles, 2004). The retail industry is realizing that it is possible to gain a competitive advantage in utilizing data mining. Retailers have been collecting enormous amounts of data
throughout the years and with data mining, they now have the tools to manage and analyze these data to obtain useful knowledge (Hormizi & Giles, 2004). This paper discusses the applications of data mining in retail industry and how competitive advantages can be achieved through data mining as well as challenges that organization has to be aware of in using data mining.
Literature
Data mining is one of the most important tools for analyzing information from large databases. The retail industry has recently seen the growing number of data mining applications in reducing time and cost for the industry. The paper defines data mining and the seven operations of data mining that have been classified through many different literatures. It then focus on the important applications of data mining in retail industry including marketing, customer relationship management, risk management, fraud detection and retail inventory management. Further discussion of the challenges associates with data mining enable an understanding of the responsibilities of organization and the risks of ignoring these challenges.
Introduction
Data might be one of the most valuable assets of any corporation – but only if it knows how to reveal valuable knowledge hidden in raw data. Data mining allows business to extract these diamonds of knowledge from historical data to predict outcomes of future situations. In modern day, data mining is being used by various industries including banking & finance, retail, health care, insurance, etc (Bhasin, 2006). Several recent trends have increased the interest in data mining, including the declining cost of data storage, increasing ease of collecting data, development of efficient machine-learning algorithms to process data and better computational power (Hormozi & Giles, 2004). The retail industry is realizing that it is possible to gain a competitive advantage in utilizing data mining. Retailers have been collecting enormous amounts of data
throughout the years and with data mining, they now have the tools to manage and analyze these data to obtain useful knowledge (Hormizi & Giles, 2004). This paper discusses the applications of data mining in retail industry and how competitive advantages can be achieved through data mining as well as challenges that organization has to be aware of in using data mining.
Literature