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Data Mining Technologies Case Study


Introduction

According to Mihai & Crisan (2010), in the world today, almost every transaction that takes place is recorded and kept in files for later references. This has resulted to an increase in the data produced and stored in various fields of activity. In fact, every enterprise has accumulated data on operations, activities and performance.

All these data hold valuable information, e.g., trends and patterns, which can be utilized to improve business decisions and optimize success. Mihai & Crisan (2010), point out that that traditional methods of data analysis that are based mostly on humans dealing directly with the data, basically do not scale to handle these large amounts of data sets. For this reason, various advanced technologies called data mining techniques have been developed to process the huge volumes of data.

According to Han & Kamber (2000), data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases and data warehouses. The data mining process is employed by several sectors to manage and deal with troubles that are normally related to clients and which hider efficient operations of various entities.

Important features of data mining tools

Data mining tools integrate many operations and provide an easy-to-use way to perform the data mining process. There are many different types of data mining tools. The tools are diverse in design and implementation. However, there are several important features for data mining tools that enable them to be accommodated by users in the most efficient and effective manner.

The first important feature is the capability to access various data sources. Usually, data is obtained from a variety of sources in diverse designs. A good tool should enable the user to access different data sources without difficulties. It should also posses the ability to run on huge data sets. This is very important in circumstances where an entity stores large amount of data (Mihai & Crisan, 2010).

Han & Kamber (2000) argue that good data mining tools should be user friendly. According to them, this is significant since most of the times the persons using the tools are not specialists. The data-mining tools should possess the ability to process data in a most efficient manner since this is often considered crucial in solving problem. Connolly, Begg & Holowczak (2008), point out that data mining tools should also enable good data and model visualization.

This is will enable users to properly analyze the data and make sound decisions. They further state that the data mining tools should also enable users to easily integrate different techniques during the mining process. This is because there is no one tool that can effectively handle all prevailing dilemmas. Therefore, superior data-mining tools should allow users to incorporate a variety of procedures in order to deal with various dilemmas.

Han & Kamber (2000), assert that since the rate of innovation is very rapid, new techniques and algorithms are always present in the market, it is also important that data mining tools provide good extensibility means. This design allows users to monitor the tools in the most efficient and effective approach. Good data mining tools should also enable interoperability with other tools as well as data exchange of model and provide support for good data mining standards.

Data warehouse

A data warehouse is a site where information is stored. The idea of data warehousing was developed out of the need by different parties to have easy access to structured store of quality data that can be used for decision-making. Generally, information is a very powerful asset that can provide important benefits to any enterprise and a competitive advantage in the business world.

The massive amount of data possessed by firms has made it difficult for the firms to access it and make use of it. This is because it is in many different formats, exists on many different platforms, and resides in many different file and database structures developed by different vendors. Data warehousing offers a better approach to managing these data (Connolly, Begg & Holowczak, 2008).

How data mining can realise the value of data warehouses

According to Connolly, Begg & Holowczak (2008), data mining represents one of the most important applications for data warehousing. This is because most of the information that can be used to analyze various problems is accumulated in a data warehouse. The data mining techniques enables users to extract all relevant information needed for making good decisions and which is hard to obtain in most cases.

This kind of data enables realization of the value of a data warehouse when used appropriately. Han & Kamber (2000), point out that data mining can also realize the value of data warehouse by making use of advanced data analysis techniques for strategic management to interpret the information stored in the data warehouses. It is crucial that decisions that are taken by administrators are taken on informed basis and not based exclusively on the talent and knowledge of the administrator.

This application of data mining techniques became possible by making predictions based on the data that an enterprise has access to i.e. data from its own databases (data warehouses). This is very important since the data collected is available in time for analysis when required.

According to Mihai & Crisan (2010), data mining can also aid in designing data warehouses for a specific application. In this way, the value of the data warehouse can more easily be realized because the amount of pre-processing required before data is mined can be determined according to the data available.

For instance, if the data is stored in relational databases, it is easier to analyze it and most of the data mining tools can be used without difficulties. Data mining also transforms the detailed level of operational data stored in the data warehouse to a relational form that makes the information to be more amenable to analytical processing.

Conclusion

Data mining is an exceptionally valuable tool to explore the essential data to create reasonable advantage in the ever-changing environment. Data mining is employed by several sectors to manage and deal with troubles that are normally related to clients and which hider efficient operations of the various entities.

There are many different types of data mining tools. The tools have different features and are diverse in design and implementation. Data warehouses are sources of new information and are built to provide simple means to access source of high quality data. Therefore, data mining can easily realise value of data warehouses by making use of the stored information.

References

Connolly, T., Begg, C., & Holowczak, R. (2008). Business database systems. Harlow: Addison Wesley.

Han, J., & Kamber, M. (2000). Data Mining: Concepts and Techniques. Massachusetts: USA, Morgan Kaufmann.

Mihai, A., & Crisan, D. (2010). Commercially Available Data Mining Tools used in the Economic Environment. Database Systems Journal, 1(2)45-54.

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IvyPanda. (2019, December 7). Data Mining Technologies. Retrieved from https://ivypanda.com/essays/data-mining/

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"Data Mining Technologies." IvyPanda, 7 Dec. 2019, ivypanda.com/essays/data-mining/.

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IvyPanda. "Data Mining Technologies." December 7, 2019. https://ivypanda.com/essays/data-mining/.

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IvyPanda. 2019. "Data Mining Technologies." December 7, 2019. https://ivypanda.com/essays/data-mining/.

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IvyPanda. (2019) 'Data Mining Technologies'. 7 December.

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