Data Mining in the Pharmaceutical Industry

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A Look at Data Mining in the Pharmaceutical Industry
Topics Covered:
1) What is Data Mining and why is it used?
2) How is Data Mining used in the Pharmaceutical Industry? 3) Recent debate in the legality of Data Mining and the Pharmaceutical Industry

Pharmaceutical companies are taking advantage of the growing use of technology in the healthcare arena by using data to enhance their marketing efforts and increase the quality of research and development. The process of data mining allows companies to extract useful information from large sets of individual data. This process provides a knowledge that is vital to a pharmaceutical company’s competitive position and organizational decision-making. “Data Mining enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data. It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System (DSS) tools” (Rangan, 2007).

1) What is Data Mining and why is it used?
“Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD)” (Oracle, 2008). As stated, data mining is used to help find patterns and relationships stored within large sets of data, these patterns and relationships are then used to provide knowledge and value to the end user. The data can help prove and support earlier predictions usually based on statistics or aid in uncovering new information about products and customers. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. Data mining is being increasingly used in business to help identify trends that would have otherwise gone unnoticed. There are several different opinions on the exact “steps” of data mining, but they all agree on these basics: planning, modeling and extracting information. Oracle defines 4 steps in the data mining process: 1) problem definition, 2) data gathering and preparation, 3) model building and evaluation, and 4) knowledge deployment. The first step of data mining is to understand the purpose, scope and requirements of the project . “Once the project is specified from a business perspective, it can be formulated as a data mining problem and a preliminary implementation plan can be developed” (Oracle, 2008). The data gathering process takes a look at how well the data serves the purpose of the project. In this step many changes can be made to the attributes of the data so that they better serve the objective and requirements of the project. This process can play a large part in the value of the knowledge and information derived from the data. “For example, you might transform a DATE_OF_BIRTH column to AGE; you might insert the average income in cases where the INCOME column is null” (Oracle, 2008). The third step of data mining is to build and evaluate the model. The model should be tested and evaluated to make sure that it will answer the question and stay within the requirements of the business objectives stated in the first phase of the process. The final phase includes knowledge deployment which is where actual information and realization comes from the data. Here is where the relationships and patterns are turned into something meaningful that meets the objective of the project. There are several techniques used for data mining, some of them have been used for decades prior to the information technology boom that has changed the system dramatically. According to (Alex Berson, 2000), these “classic” techniques include Statistics, Neighborhoods and...
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