Data mining is the process of discovering patterns and trends in large sets of data for the purpose of getting new information for better decision-making.
This also refers to the process of extracting concealed analytical information from large databases in various organizations. This process has of late become a very essential tool for helping companies to get the most important information’s from their stores. Data mining techniques are instrumental in the sound decision-making process in an organization because they make predictions of the future after analysis of the current sources. The techniques can provide solutions to problems in a business that might have taken a very long time to resolve. Data mining has had its origin in research and product development processes. With the storage of data in computer systems need, there was need for more proactive procedures rather than the retrogressive methods used in the past. Massive collection of data, powerful computers and algorithms for data mining are the technologies that have facilitated data mining process. Data mining has a significant impact on our lives but it is quite up surd that the invaluable contribution of this invisible technology often goes unnoticed by people. The worthiness of data mining as an essential process in organizations comes in a number of ways. (Thearling 1997).
In marketing, data mining is significant in providing clues on the patterns of customers buying behavior. It is from this basis that marketers are able to come up with better strategies that can see an increase in sales. Marketers can also utilize data mining to make predictions of the products that customers are likely to prefer in future. From such predictions they can be better placed to surprise the consumers and make their shopping interesting Data mining has also been worth in banking and credit institutions. (Noyes 2004)This is in the line of loans whereby data mining can provide important information for instance by comparing other customers the risk of an awarded loan can be estimated Information gained can also be useful, in detecting credit card frauds. Even though it does not present very accurate information but it is a very useful tool for preventing credit card related fraud. Still in the light of crime, Data mining is a very vital tool for law enforcers as it is useful for detecting criminal elements. This can be achieved by the identification of suspects through the various trends and patterns in their behavior. In research activities, data mining can prove to be a very useful tool. This can be viewed in the sense that it can be used to speed up data analysis processes and this can allow researchers to do other things.
Therefore the above-mentioned importance can best be used to describe why data mining is such a worthy technique for organizations. (Noyes 2004).
However it is of importance to mention that there are issues with data mining that make it more of a threat than its usefulness. First of all, I would commence by mentioning issues to do with privacy. With the coming of the internet privacy issues have emerged and data mining causes great concern. Consumers fear purchasing online products because of the fact that somebody can access personal information and end up using it in a manner that is not acceptable at all. Therefore better protection of personal information has to be put in place to prevent the unethical use of personal information by people with malicious intentions.The sale of personal information without customers consent should be discouraged at all costs.This should be done by firmly enforcing the privacy law that can act as a deterrent to those who might be having the intention of infringing on other people’s personal information. This would be a major contribution towards preventing data mining from becoming a trouble than it is worth.
Security issues concerning data mining have also emerged. Most companies such as the pharmaceuticals do not have sufficient security systems to prevent data mining from being a threat. Hackers have in the past gained access to customer information such as social security numbers and other details like in the case of Ford Motor Credit Company.
If sufficient security measures are not developed, then data mining can end up becoming a weapon that can even be used by terrorists to attack certain companies after accessing such sensitive information. Sharing of personal information has to be stopped in companies as it is the main cause of identity theft problems. This is so because companies do not have adequate policies that address the use of personal information. They also do very little to take good care of personal information.
Another issue concerns the misuse of patterns of information obtained through data mining. For instance patterns obtained for the purposes of marketing being misused because of one reason or another could cause some serious problems.Therefore data mining can be used in this sense by powerful people in the society to discriminate against the vulnerable groups or less fortunate in the course of pursuing their selfish interests. This can lead to serious consequences. Thus the laws should be amended concerning such issues to ensure that the vulnerable groups are protected from being discriminated against because of the data mining techniques. Though most people recommend the need for privacy to be enhanced as far as data mining is concerned, the use of anonymity and the related agents should also be put into consideration. This should be in an effort to prevent the user identity from being recognized during data mining. Negotiation and trust agents can also be useful in helping the users with information concerning a service before deciding to submit personal information. Organizations should come up with guidelines and rules that site managers and administrators can use in the various analyses of data without compromising the privacy and identity of users. (Noyes 2004) This can ensure that the administrators protect the users from threats that are caused by data mining.It is important to underscore the fact that emerging concerns with extensive mining cannot be adequately addressed by privacy issues only. Putting in place a distributed data mining approach can help to address the issue of extensive mining. With this kind of approach, different agencies will become in charge and fully responsible for different data repositories. By ensuring that no central agency is in charge of and full control of all the data mining technologies and the resources, would help to minimize the threats that might come up as a consequence of data mining. The governments in various countries must have their contribution in the data mining phenomenon. This should be done through the maintenance and preservation of regional and national sources of data. This is crucial in preventing unauthorized entry to such type of information.
In conclusion, it is vital to point out that data mining is a phenomenon that is undergoing changes in terms of maturity and it is widely used in even encompassing situations. Data mining has become a potential threat to organizations and customers because of the fact that data has been misused by some people. It is the misuse of data mining that is capable of causing serious consequences. There is need for people to learn how to use data mining effectively to enrich their lives without harming other people. The dangers that present in organizations as a result of data mining should be adequately tackled through deliberate efforts by everybody who is involved. This refers to the law enforcement agencies, the data mining agencies, and the users of the systems in various organizations. The stakeholders in data mining should ensure that there are strict regulations to prevent the misuse of data. This is because of the fact that it is actually misused of data that is responsible for bringing about concerns in data mining. Addressing the issues that will prevent misuse of data mining would be a major step in making data mining worth than the underlying threats it has to consumers, business organizations and governments. (Klingler 2002)Therefore through the cooperation of the above-mentioned parties, proper regulations should be put in place to control the use of data mining techniques. For these regulations to work properly they need a good legal framework to back them to prevent manipulations from any corner.
Thearling, K. (1997) Understanding Data Mining: It’s All in the Interaction. DSStar. Web.
Noyes, H. (2004) People, Process and Technology in Risk Management. DM Review Magazine. Web.
Klingler, D.E. (2002) IT and Pharmaceutical Data: Finding Needles in Haystacks. CIOInsight. Web.
Lindorff, D. (2003) Case Study: Parkway Corp. and Business Intelligence. CIOInsight.