Ethical Issues Raised by Data Mining Essay

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

Data mining is essentially the ability to discover new information by exploring through various databases of existing information. According to Laura and Jack Cook, data mining "facilitates the discovery of previously unknown relationships among the data. …These operations present results that users already intuitively knew existed in the database."[2] As an example, let us take a school system consisting of three databases: one which stores the student profiles consisting of name and identification number, another to store student grades based on identification number, and the last one stores all the transactions at the bookstore through the student identification card. This is a simple example, but it should illustrate our point. Alone, the separate databases might not tell us much. With data mining techniques, the process might be able to tell us that in a particular school year, students of a certain ethnic background obtained above a 3.0 GPA, or that the bookstore sold mostly engineering books to students last year, or even that students who obtained above a 3.0 GPA were ones who bought engineering books. More specifically, the technology might be smart enough to associate that John Doe from Ireland had a 3.32 GPA in his engineering classes, even though he did not buy any engineering books from the bookstore. This type of technology is very powerful source of

It became an issue for consumer, and they felt uncomfortable due to companies invaded consumers privacy without their consent. Another issue regarding data mining is there no clear policies around how the company uses data once they have collected the information. After Target received negative publicity regarding its original data mining approach, they change their strategy by mixing all their ads, so it not that obvious that they were spying on them. It makes customers’ to assume that everyone else gets same coupons and ads. Data mining has given Target awareness into the relationship between product sales and online reviews. Target retailers identify guests how write reviews, examining their history of purchasing and invited to write review on their products. This program was highly successful, leading Target to obtain incremental reviews and increase sales of those key products. More importantly, data mining has empowered companies to find new opportunities for growth, make better decisions to succeed business goals and safe money. The retailer industry utilizes data mining and customer analytics to support customer decisions. Exploring how collected data can be manipulated to identify customers buying patterns and increase profitability is a growing business

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Dana and Gandy (2002) studied the technical and social concerns associated with using data mining techniques to create customer profiles that may exclude classes of consumers from the marketplace. Dana and Gandy’s (2002) study differ from Scott (2014) and Bond and Foss (2005) because it takes into consideration the specific use of data mining techniques employed by companies such neural networks, decision trees, market basket analysis, and clustering. The solution Dana and Gandy (2002) propose is to use more sensitive alternatives to the use of data mining. Dana and Gandy (2002) employ the use of hypothetical scenarios to illustrate how different data usages negatively affect people to simply a complex topic, which is similar to the persuasive devices used by both Scott (2014) and Bond and Foss

the access to this superfluous amount of data. A major concern is how this information is being used to

Data Mining, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

Database marketing assist marketers to record actual purchase behaviours of customers and hence help them monitor and tailor their promotions towards the customer’s interests. Also on the internet a customer is

Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed, or ones that give a more insightful view of the