Data Mining in Business Research

Data Mining in Business Research Executive Summary The perfect storm is brewing over analytics and their use to define the future of data mining and its associated techniques and technologies, (Columbus 1). The use of data mining, its adjunct technologies for text mining and the ability to interpret, analyze and create linguistic models from unstructured content is revolutionizing the concept of data mining away from being purely used for structured content in data warehouses to now encompass unstructured content found throughout organizations globally. Text mining's growth is attributable to many factors, which include but are not limited to the following. First, there is the rapid growth and popularity of Google, and its search engine technologies and many patents on taking unstructured text content and turning it into intelligence (Google, et.al.). Their most famous patents surround latent semantic indexing, an approach where a model is constructed from the similarities of linguistic content to create threads of intelligence on specific topics. There's also the development by some of the world's largest software companies in the area of Natural Language Processing (IBM Natural Language Processing, 3) , where unstructured or un-organized content is captured, analyzed for trends and assumptions of their interrelationships, and then reported back (CoGenTex, 4). This is a specific technology used for interpreting the "chatter" of terrorists and the intercepted cell phone, e- mail, and Instant Messaging done between cells in the United States and other nations. This technology can create in effect a hierarchy of senders and receivers of content and what their key messages are, which allows intelligence organizations worldwide to plot the organization charts of terrorist organizations. Second, text mining is growing rapidly due to the burgeoning growth of blogs, and the insights obtainable from these and other forms of consumer-generated media. Companies are actively mining blogs to discover what consumers are saying about them. Third, text mining is becoming pervasive as many companies of all types have their most important information stored in systems as comments, and the need to interpret these comments and form a common thread of knowledge is needed. Fifth, compliance in its many forms is making text mining a critical ingredient of every manufacturer's mix of tools for getting a forward look at what is happening in their installed base of customers. Warranties specifically have been a focus on the part of American automobile manufacturers given the TREAD Act, which is based on the strong of Firestone Tire accidents on Ford Explorers (Mixer, 1). The Sarbanes-Oxley Act also is forcing the issue of text mining as well. (Hagerty, 2) Taken together, all these factors and more are making text mining a critical link in both government and commercial strategies for defense, security, insight, and growth. Data mining's growth has equally been impressive, yet can be attributed to different factors than text mining. First, there is the demand from within many companies of creating a centralized repository of all content so the data mart or data warehouse can quickly accessed for insights and intelligence (Columbus and Murphy, 4). The future of data mining is in predictive analytics, which is the ability to interpret then define the future of key areas of any company, based on their previous performance. The intent of this paper is to provide insights into these areas of data mining, and the currently high levels of growth analytics use and applications software are experiencing as a result.

Using Data Mining in Business Research The ability to create differentiated and effective strategies rests for many companies on the interpretation of their vast stores of data. The ability to parse through the many records of transactions, customer contacts, and many other items stored electronically creates the foundation for data mining's definition. Data mining specifically is defined as the process of data selection, exploration and building models using vast data stores to uncover previously unknown patterns, insights, and observations that lead to strategies for effective differentiation and growth. Central to the development of data modeling is the creation of data and prediction models based on data collected from a variety of sources, including corporate transactions, customer histories, and demographics, even external sources such as credit bureaus and services organizations that sell content (Westphal, C., Blaxton, T., 34). Companies accomplishing best practices in data mining then use the many data and prediction models to produce patterns in the information that can support decision making and predict new business opportunities. What's unique about data mining is the ability to quickly create entire snapshots and background statistical and content-specific data quickly using seemingly disparate and unrelated information (Kay, 44). As a result, data mining's reach is extending across many industries. The following are examples of where data mining is being used. In telecommunications, companies are using data mining to analyze and predict stock market performance and fluctuations, define credit card and insurance limits and strategies for delivering better customer service performance to clients. In the medical industry, companies are increasingly using data mining to predict the effectiveness of surgical procedures, medical, tests, medications, and also predict the impact of healthcare strategies and policies on populations of patients and those for whom treatment is targeted. Retailers are perhaps the most prodigious users of data mining today, as their focus turns from pure cost cutting to managing pricing and promotional discounts for the most profitable sales possible. In retailing there is also the fact that RFID (Radio Frequency Identification) has become a primary market driver that is creating ever-larger data warehouses (Romanow, 1 & 2). In the specific case of RFID-generated warehouses, there is a dearth of data warehouse and data mart application software vendors who are offering solutions in this area today, which translates into a burgeoning area of data mining in retail for years to come. RFID is going to revolutionize the supply chains of many of the world's largest retailers including Wal-Mart and Target, and data mining will be turn be used to define which specific products are ordered at what time, for what price. Data mining is impacting the price anyone pays at Wal-Mart or Target for the smallest to the very largest product.