The Mechanism of Data Mining - Term Paper Example

Data mining has been increasingly popular as a result of its contributions to cost control and increased revenues. According to Ranjan and Bhatnagar (2011), the technique is increasingly used in marketing as it is widely known to help in managing all phases of the customer life cycle from increasing sales from existing customers, acquiring new customers, and maintaining the already existing customers. Ideally, identifying traits of good customers, an organization can use such information to pursue targets exhibiting the same traits.

As such, profiling clients who have earlier interacted with the organization through the purchasing of a given product or service can help marketers to focus on the same characteristics on potential customers who have not interacted with the company before. More so, understanding the nature of customers who have abandoned the company’s products enables marketers to understand customers at higher risk of leaving the organization. With such information, the company can take the right measures to prevent future leaving.

The use of data mining for analyzing data from customers’ databases has had a great impact on marketing. Data mining uses ranges from analyzing point of sale data, predicting television advertisement audiences, studying effects of price variation and channels of distribution on market share and sales volume. Mining gives a continuous automated response to ensure internal control systems operate as planned and that business transactions are carried out in line with established laws, regulations, and policies.

In this vein, it provides management with information that enhances the shift from traditional outdated information management activities. According to Marthandan and Tang (2012), data mining places an organization out of problems by allowing them an opportunity to put in place necessary measures to mitigate the impact of problems that may face the organization. Before the evolution of data mining, organizations maintained reams of papers.

Luckily with data mining, they can access data on a real time basis hence enhancing management assurance. Qiu, Li, and Wu (2008) pointed that data mining helps to identify the main causes of organizational problems, and therefore, putting in place processes and systems of internal controls to address weak areas pinpointed. In customer relationship management, data mining applications contributes immensely to the bottom line. In this respect, rather than contacting customers via emails or through the call center, it is only the prospects that seem to exhibit likelihood of accepting the offer are contacted.

In addition, the management may apply sophisticated methods to optimize campaigns that can help determine the channel that will help assess the customers with the desired needs that a particular good or service seeks to offer. Data mining has been found useful in human resource management. This is because it helps identify the traits of the most successful employees. With the use of this software, the human resource personnel can get information such as colleges attended by successful candidates.

Factors that have led to the growth of data mining In the recent past, there have been numerous developments that have led to increased use of data mining. One of such factors is increasing data from many sources such as numerous online transactions, global positioning systems, and wireless electronic data among others. According to McCue (2006), it is cheaper to collect data presently for it may become a by-product of information communication technology widely considered an integral part of modern organizations.

Online data were also found to be easily stored in data warehouses. Another notable factor that has stimulated increased use of data mining is the lower costs of electronic communication. Previously, data stored manually in different locations were difficult to share hence being of little value to an organization. Presently, with the advanced communication technology mediums and presence of the internet has significantly reduced the cost of transferring information. Improved user/client interfaces have led to increased use of data mining.

It is therefore, not necessary to write computer algorithms hence minimizing opportunities for making mistakes. Notably, the modern versions of data mining software, users can easily evaluate the findings in order to make the right decisions. However, even though these applications lead to data accessibility, but create a space for erroneous application of instructions. In that vein, user extensive training is paramount to guarantee the application of the right software for data interpretation and decision making.

Criticism of data mining Despite the remarkable steps achieve in data management through the use of data mining technologies, there has been controversies surrounding the use of data mining technologies. According to Magnini, Honeycutt, and Hodge (2003), marketing decision is made based on data and not theories. This goes contrary to traditional data analysis methods which are based on a sound theoretical foundation. Data miners claim that data warehouses are vast, and therefore, increasingly difficult to interrogate the entire data and the existing relationships for proper application of hypothesis and theories.

More so, the opponents of data mining argue that its predictive nature is highly unscientific since predictive techniques should be data driven rather than based on theories. Research based on data mining searches for relationships that can confirm existing notions, but even without such relations, numbers can be relied upon to suggest existing relationships. Nonetheless, focusing beyond argument about theory driven approaches, data mining has many advantages including identification of relationships that would have otherwise remained hidden hence making decision making better and highly informed.

More importantly decision making becomes more complete, and therefore, easy understandability of data and underlying patterns leading to more accurate predictions.