Data Mining has created a great deal of controversy in the business world due to the fact that it violates many principles of privacy. Where we run into issues with protecting privacy is a result of the strict confidentiality laws that exist in the United States. Consumers ideally do not want companies having access to information about their purchasing patterns. Essentially, customers do not want the hassle of receiving more advertisements than they already do. Data Mining has been seen as the enemy to the consumer because in the modern age that we live in, there is not a great deal of protection for privacy. In order to understand how Data Mining is a great asset to the business community that should be continued until something better comes along, it is important to ascertain what Data Mining actually is. This paper will explore Data Mining and the various benefits to companies associated with Data Mining. Then, the paper will shift focus to the debate pertaining to consumer privacy and postulate whether it is likely that Data Mining will continue in recent years. At that point, this paper will make suggestions on how to keep Data Mining as a viable tool for business professional. That being said, I believe that Data Mining is something that is not going to stop in commerce because businesses need it and even if it were outlawed, businesses would still continue to have the technology to access Data Mining capabilities. Thus, it is highly likely that we are going to see Data Mining continue to be used in the realistic practice of commerce.
In order to be able to engage in a debate as to what Data Mining is, we have to first define what it does for the prospective business owner. Essentially, Data Mining is the extraction of hidden information that is predictive from large databases, (“An Introduction to Data Mining” . What this information is used for is that it predicts future trends and behaviors that influence the way that businesses make their decisions, (An Introduction to Data Mining”). At times, Data Mining can also be called “data or knowledge discovery,” (“Data Mining: What is Data Mining?”). Data Mining essentially analyzes the essence of data from different perspectives and reorganizing it into a medium that can be useful to the prospective business owner, (“Data Mining: What is Data Mining?”). The information that is gathered can be used to increase revenue and cut costs substantially, (“Data Mining: What is Data Mining?”). It is useful to consider Data Mining to be the process of organizing and finding various patterns along dozens of fields in large corresponding databases that are relational in some way, (“Data Mining: What is Data Mining?”). An example of Data Mining in the everyday world can be seen from a typical supermarket using Oracle’s software to learn the usual buying trend of certain products, (“Data Mining: What is Data Mining?”). What the supermarket can learn in this example is what days of the week were typical shopping dates and which products were the best sellers on those corresponding days, (“Data Mining: What is Data Mining?”). Examples such as these are very useful to the prospective corporation in that they can predict what inventory to stock and time their shipping intervals accordingly. Thus, this is a viable investment for many businesses who have to stock inventory.
Within the family of Data Mining, there are five principal methods that are utilized to organize and sequence the data to be useful to companies, (“Advantages and Disadvantages of Data Mining”). Artificial Neural Networks are essentially non-linear predictive models that acquire knowledge through and are equivalent to biological nerve centers, (“Advantages and Disadvantages of Data Mining”). Decision Trees are structures that represent different aspects of consumer’s decisions, (“Advantages and Disadvantages of Data Mining”). Genetic Algorithms are optimization techniques that utilize processes that analyze the evolution of the consumer’s decision, (“Advantages and Disadvantages of Data Mining). Nearest Neighbor Method uses the k-nearest method in order to ascertain which neighbors have the most structural resemblance to one another in order to detect consumer patterns, (“Advantages and Disadvantages of Data Mining”. Lastly, Rule Induction is the extraction of useful if-then rules from data that are derived from statistics that are relevant, (“Advantages and Disadvantages of Data Mining”).
Data Mining principally has two different scopes to include: Automated prediction of behaviors and trends and automated discovery of unknown patterns. These two scopes allow retailers to learn a great deal about their consumers and learn how to structure their business model appropriately according to these pertinent statistics, (Ivanovs, Alex). An example of the first scope that relates to trends and behaviors would be a problem in not targeting the proper market. Under this scope, the marketing pamphlets to see on average which consumers were actively responding to the marketing outreach efforts that the company was investing in, (Ivanovs, Alex). An example of the second scope relating to previously unknown patterns can be seen as related to finding fraudulent credit card practices, (Ivanovs, Alex).
Even though all of this information pertaining to the two scopes is greatly useful to the prospective business owner, it also raises many legal issues that are relevant to any business owner. One aspect of Data Mining that is extremely controversial is the tendency of Data Mining to violate various privacy laws, (Kramer, Shelly). Particularly in the example that we saw from the detection of fraudulent credit cards, one could easily make the argument that Data Mining invades their privacy, (Kramer, Shelly). This argument can also be applied to the marketing trends scope example because it is entirely possible that consumers do not want information about them tracked and used, (Kramer, Shelly). In fact, they may not want any company to be able to access any information about them whatsoever, (Kramer, Shelly). Issues such as these are paramount to the future of Data Mining because in the end, it is highly likely that the consumers could win out with these complaints. That being said, these complaints could take a great deal of time to be litigated, but there is still a possibility that the customers could win.
The confidentiality issues aside, it is important to discuss the reality of Data Mining being used in commerce. The reason for this is that Data Mining is a well established practice in the business world that is going to be hard to eliminate if it were found in the courts to be too invasive to consumer’s privacy. For example, many companies use Data Mining as a means to study their consumer in depth. This in depth analysis is designed to assess how much of a product they should order and whether their marketing efforts are working to attract their targeted consumer. Where there tends to be a problem in the public is that there is a misconception that Data Mining leads to the personalized advertisements that we now see in the Internet. For example, if a consumer is shopping for engagement rings, they will see engagement rings identical to those they looked at popping up on their Facebook and other website searches as advertisements. Consumers tend to associate this with being targeted against their will by companies that is invasive to their visits to other websites. Personally, I found this rather invasive when I was shopping as well; however, there is a very strong argument that this is quite effective when considering how many clicks companies are getting from these advertisements. Additionally, the websites are making a fortune on the advertisements as well. All of this aside, what is going to be the major issue with Data Mining that is used to generate these advertisements is whether consumer’s feel that their privacy is being violated to a certain extent? There is no easy answer to this because by consenting to use the Internet do subject themselves to being public. Consumers also subject themselves to public exposure by purchasing items online. This is not to say that they do not have a right to privacy, but they also have to understand the risks associated with using credit cards and buying items by not paying with physical cash. What computers have done is allow consumers to be tracked whether they like it or not. The need to keep accounting records has facilitated this transition and has left United States policy makers with a gap with how to handle this transition into the modern era of commerce.
Where these issues are particularly pertinent in the United States is pertaining to Fourth Amendment arguments about the right of consumers to be separated from unwarranted searches and seizures. While on the surface this does not appear to apply, this Amendment applies more than consumers realize because it was created with the intention of the founding fathers to prevent citizens from having their privacy invaded by the government. Even though the founding fathers clearly did not foresee Data Mining or smart phones, they did foresee the right of citizens to separate themselves from government interference that was unwarranted by a court order. What this Data Mining and transactions on computers has created is a culture where information is less protected and any prospective government agency or company could conduct studies and searches pertaining to consumer behaviors. Where this becomes an issue is whether that individual is being targeted based on their individual identity or in a pool of consumers to prove a given marketing statistic. If the consumer is anonymous in a pool of consumers for the purposes of a marketing trend study that is conducted through Data Mining, then it is highly likely that there will be less applications to the Fourth Amendment because the consumer may not know whether they are being studied, which throws the Fourth Amendment out of the equation. However, if a marketing agency were to pick up something unusual from a consumer that led to a government agency finding out information that subjected the consumer to liability, then there would be a potential Fourth Amendment argument there to protect the consumer’s interest against unwarranted searches and seizures. This is why these issues as applied to the original Amendments to the United States Constitution are so complex for the courts to debate because there was truly no intention by the founding fathers to anticipate this sophisticated and high technology world that we operate our commerce and personal lives in today.
In order to play Devil’s Advocate and argue the point of view of the business, the business does not want to lose money and Data Mining helps them achieve this objective. The reason for this is that Data Mining allows the business to anticipate what they are ordering and marketing in order to see what they need to do to better know and reach their target consumer. This also helps the business to see pertinent trends in the market that could be useful to them for product development. Even though Data Mining is very expensive to operate, its expensive price tag outweighs many of the figures of corporate loss that businesses experienced prior to the availability of technology. Computers have greatly expanded business’s capabilities in the modern era to better understand their customer and innovate their product offerings. This has completely changed the way and geographic capability that businesses have in order to reach consumers. This is why Data Mining is so crucial to their marketing and overall practice in the stream of commerce.
In regards to arguing who has a stronger case on the issue of Data Mining, I would say that both parties are tied because they both do have valid arguments. As a consumer, I completely understand why many consumers do not want to be the corporate guinea pigs of marketing departments of large companies. Consumers merely want to consume without interruption or study; however, they are open to receiving promotional offers when it benefits them. But when the offer no longer benefits them, it becomes an intrusion to them. This is how the American consumer thinks, which creates an ethical challenges for companies, the legal system, and the overall practice in general within the United States because where can the line be drawn?
A similar comparison can be seen to how the federal government has gathered information about citizens in the wake of the attacks on September 11, 2001. Many individuals felt that this Act intruded on private citizen’s right to not have government looking into their affairs without probable cause. While Data Mining is not as severe as looking for prospective “criminals,” it does have a similar argument that debates how much information sharing is constitutional? This issue is so complicated that even the Supreme Court and some of the brightest attorneys in the nation cannot make a comprehensive argument because the argument is just too complex. This is precisely why Data Mining does have a viable future because it is useful. However, that future is not iron clad because there are serious strong arguments that do have validity when questioning the line of constitutionality pertaining to companies and the government accessing consumer data without their express consent or probable cause.
Thus, even though there are a plethora of issues pertaining to Data Mining due to the concerns that Data Mining brings to confidentiality issues in the field of commerce. That being said, Data Mining is such a part of the contemporary business culture, it will be difficult to eliminate unless another more powerful technology for marketing departments to use comes around. Marketing departments need a way to study their consumer. Where Data Mining becomes problematic is that some consumers simply do not want to be studied and this causes major legal issues for companies. However, the positives of Data Mining is that it eliminates corporate waste in the supply chain of a supermarket, let’s say because it allows the supermarket to know who is buying what product on a Tuesday. This allows the supermarket to anticipate what they need to have in stock for that consumer to buy. This is such a useful tool for the business world that companies will really not want to give this up. What companies are going to have to learn to balance is the consumer’s desires versus the necessity to study them. For example, consumers do not want more advertisements than they already get. Particularly, consumers do not want advertisements that they did not sign up for that merely reflect a statistical calculation of their spending patterns. To many consumers, this infringes on their right to shop without being studied. To consumers, this also infringes on their right to not have businesses and the government study their behaviors. What consumers have feared is government interference in their private affairs, which is precisely why they do not want businesses interfering as well. In my view, both sides have a valid argument; however, the key to how Data Mining will be able to survive relates to how the United States courts decide to curtail its capabilities and also how companies learn to balance consumer’s preferences with their marketing objectives. This will take many years to ascertain which direction this policy will go; however, it appears as if Data Mining is a necessary evil that will be staying in the practice of business marketing studies for the foreseeable future.
Works Cited
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Ivanovs, Alex. CodeCondo. New York. April 2014. Print.
Kramer, Shelly. “The Big Risks of Data Mining.” V3B Marketing. 24 June 2015. Web. 3 April 2016.
Pancewicz, Magdalena. “There Are No Alternatives to Data Mining.” TunedIT Data Mining Blog. 20 July 2010. Web. 3 April 2016.
