Data Mining: What Is It and How Is It Used? Coursework Example

Enterprise Computing for Business

Data mining is a knowledge discovery process that is also known as Knowledge Discovery in Databases (KDD). The primary function of data mining or KKD is to analyze and search large number of data patterns in a database. Likewise, it utilizes computerized data analysis techniques to expose relationships of data items that were previously hidden or undetected. However, the data that is analyzed via different techniques is fetched from data warehouses, where many databases are interconnected with each other. Major techniques that are involved in the process of data miming are regression, classification and clustering (Data Mining. 2007).Data mining is incorporated for gaining in depth patterns for market intelligence from data warehouses containing massive amount of data. However, the issue that arises is not the quantity of data, as we already have massive amount of data to work with, it is the methodology that is required to learn data. Likewise, data provides all the attributes but how to utilize them for gaining benefit is another question KONG, 2011). This is the area that is addressed by data mining, as it is used for extracting valuable information from large amount of data saved on periodic basis. Likewise, information that can be extracted may contain relationships and different patterns. For instance, a retail store may indicate that some products are more in demand in one channel of distribution, there may be two different products that are sold at the same time in a specific geographic location, some specific products are more in demand in some geographic locations and similarly, some products are more demanding in certain events may be associated with religious events. If we take an example of Wal-Mart, the store has found that if there is a probability of a hurricane, the demand of beet increases in that specific geographical area, therefore, stores have to stock more beers that usual in this sort of situation (Keating 2008). Employee associated with utilizing patterns of customer behavior from data mining, i.e. a financial analyst would seek facets of the store or organization that may become bankrupt, similarly, human resource managers would seek information of a successful potential employee, employees working in a credit card department would like to get information associated with credit card debts payments from potential customers and also to analyze the legitimate credit card transactions against the falsified ones, marketing department executives would like to extract information associated with product purchases (Keating 2008). For instance, any online store would like to know what kind of brands are more popular for shoes; Adidas, Nike or Reebok. If all the mentioned information is available to any organization’s different departments, than efficient decisions will be made accordingly. Credit card department may focus and target potential customers to prioritize for minimizing fraudulent and fake transactions. Similarly, human resource department can short list based on background checks and hires the most appropriate candidate. Likewise, the marketing department will focus on demands of customer for different products. Accordingly, after making all the departments capable of taking efficient and informed decisions based on insights from data miming, businesses may reconstruct their objectives, strategies, goals and offerings on different products.

However, data mining techniques are not limited to the mentioned benefits that it injects in to business processes and decisions, law enforcement agencies also use data mining for detecting and analyzing probability of crime scenarios and environment of these crimes. Moreover, stock exchange can utilize data mining techniques for detecting fake activities, so do pharmaceutical organizations by mining data for predicting effectiveness in compounds and to reveal innovative chemical objects that may directly contribute for a specific disease. Similarly, data miming is utilized by the airline industry to forecast delayed flights, so do weather experts for predicting weather by analyzing patterns. They can forecast the weather by informing the time and day for weather types such as sunny, warm, cloudy, bright skies, snow, cold, heavy or natural disaster. Furthermore, trust organization and nonprofit organizations also use data mining techniques for accessing the prospect of donations from donors for any sort of charity work or cause.

Enterprise Application

Apart from the tools, there are certain applications that are required to effectively deploy data mining techniques and methods. Likewise, the two major applications that are used currently are SAS enterprise Miner and SPSS. Both of these application contains a wide range of tools supporting all data mining techniques i.e. prediction tool, classification tool, clustering analysis tool OLTP and association rule discovery tool (Online Transaction Processing (OLTP), 2007). These two most widely used data mining software are stand alone and have the capability to import and process data, on wired and wireless networks, in almost any format. As these two tools are stand-alone fully functional data mining software, there are other various data mining tools that do not support statistical support, as compare to these two, but they can be integrated with spreadsheets such as Microsoft Excel. Likewise, these small data mining tools can provide an interface for examining effectiveness and familiarity with data mining.

Key point indicator (KPI) is a value that can be in numeric formulation, as the basic purpose is to provide indications of the current state of a certain process. For instance, if a project is on a certain level of a certain stage, the KPI will indicate the completeness by providing a percentage.

References

Data Mining. 2007. Network Dictionary, , pp. 134-134.

Keating, B., 2008. Data Mining: what is it and how is it Used? Journal of Business Forecasting, 27(3), pp. 33-35.

Online Transaction Processing (OLTP). 2007. Network Dictionary, , pp. 351-351.

KONG, Q. and ZHANG, M., 2011. The Integrative Structure and Outcome Model of Relationship Benefits: Using Data Mining. Journal of Software (1796217X), 6(1), pp. 48-55.