Technology

OVERVIEW  WHAT IS DATA MINING
Sometimes referred to as knowledge discovery, data mining is the process of getting data from different angles and transforming it into useful information. Technically, you would say, data mining is the process of finding correlations and patterns among numerous fields in huge relational databases. Data mining is commonly used in practices like fraud detection, marketing, surveillance and scientific discovery. Even though data mining is somehow new term, the technology is not.

Organizations have used powerful machines to sift through large volumes of supermarket scanner data and analyze market research reports for a long time. However, continuous innovations in the processing power of a computer, the disk storage capacity, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.

DATA, INFORMATION AND KNOWLEDGE
Data
Data is a collection of facts and figures that can be processed by a computer (William Stallings, 2007). Data exists in huge amounts in different formats that include
Meta data  this is the data about the data itself such as logical database design and data dictionary definitions
Transactional data  sometimes called operational data, this includes data such as sales, inventory, cost, accounting and payroll
Nonoperational data  this includes data like forecast data, industry sales, macro economic data among others

Information
This is the knowledge conveyed concerning some specific fact, subject or event that of which one is apprised or told intelligence, news (Jill Dyche, 2000).

Knowledge
Information can be changed into knowledge about historical traditions and future trends (Bill Palace, 1996). For example, information on shop sales can be analyzed to provide knowledge of consumer behavior. Thus, a shopkeeper or manufacturer can determine which items are most susceptible to promotional efforts.

THE EFFECT OF DATA MINING AND ITS CAPABILITIES
Data mining is nowadays mainly used by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It helps these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factors such as economic indicators, competition, and customer demographics. Bill Palace (1996) goes on to say that it also helps them to determine the impact on sales, satisfaction of the customer, and corporate profits. Lastly, it enables them to get into summary information to view detail transactional data.

With data mining, a retailer could use sales records of customer purchases to send targeted promotions based on an individuals purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

SOCIAL AND ETHICAL CONCERNS
Data mining when, used in a business context and applied to some type of personal data, it helps companies to build detailed customer profiles, and gain marketing intelligence (VAN WEL Lita  ROYAKKERSLambr, 2004). However, data mining is a big threat to some important ethical values like privacy and individuality. Data mining makes it hard for one to autonomously control the unveiling and dissemination of data about their private life. VAN WEL Lita  ROYAKKERSLambr (2004) go on to say that to study these threats, we distinguish between content and structure mining and usage mining. Web content and structure mining is a major cause for concern when data published on the web in a certain setting is mined and combined with other data for use in a totally different context. Web usage mining introduces privacy concerns when web users are tracked down, and then their activities are analyzed without them knowing. Furthermore, both types of web mining are always used to create customer files with a propensity of judging and treating people basing on group characteristics instead of on their own personal characteristics and worth (also known as de-individualization). Even though there are varying solutions to privacy-problems, none of them offers adequate protection. A combination of a solution package consisting of solutions both at an individual and collective level is the only thing that can contribute to reduce some of the conflict between the pros and cons of web mining. Privacy and individuality values ought to be given respect and be protected to ensure that people are judged and treated fairly.

In other scenarios, like as artificial neural networks, nearest neighbor classifiers, which dont make their knowledge explicit in rules, the use of controversial classification attributes may be hard to identify. Even with ways and methods that make their classification transparent, such as decision trees, there is not a lot to prevent an organization using rules based on controversial attributes if that improves the accuracy of the classification. Persons who suffer denial of credit or employment based on race, sex, ethnic background or other controversial attributes in a way where this is contrary to law are in a strong position to demonstrate harm only if they illustrate the artificial classifiers are using such attributes. The question is how they obtain access to the classifier results.

If in a way or another, the person loses money or reputation due to this, courts may award damages. Moreover, since the potential for inaccuracies involved in the exercise is huge, it is predictable that the courts might apply a higher than usual standard of care in considering whether a company has breached its duty to a plaintiff sufficiently to amount to negligence. Only time will tell.

THE FUTURE OF DATA MINING
Invasion of privacy is one problem that needs to be addressed as we continue to embrace data mining. A possible solution is the anonymising of personal data (Rick Sarre, 2007). This will at least provide privacy to data subjects. But then it would render data mining a bit powerless because of its dependence on identifiable data subjects. However, a compromise would be to empower individuals to dictate the type and amount of data they think is appropriate for an organization to mine.

CONCLUSION
We should have the ability to identify ethical dilemmas as they come up and look for solutions in a manner that is timely and concurrent. Most ethical and social issues have effects on each other and overlap. We therefore are supposed to identify any commonalities and differences that are present and exploit them to derive solutions that help us to uphold our ethical standards. We are in an environment that has fast changing technology with increasing social relevance. We therefore have to use the tools technology provides wisely considering our culture and future. As technology advances, we have to check and make sure that it does not interfere with our social and ethical values. Human integrity should always be upheld, no matter how important the technological inventions are.