Data Warehousing and Mining: Trouble or Worth?

Introduction

Data mining is a computerized extraction of prognostic information from big databases. Data mining is a proactive activity that increases computing power and improves data management and collection. Data mining is used for personalizing websites, detecting credit card scam, analyzing markets and data. Data mining can also be used for express mail marketing (Thearling, 2009).

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Data warehousing is used for server querying and reporting data, modeling and storing data that has been processed (Greenfield, 2005). Even though data mining and warehousing have many advantages, they can cause people a lot of problems. Therefore, people need to control data warehousing and mining to avoid making these latest technology turn into more trouble than of value.

How to stop Data Mining and Warehousing

Data mining and warehousing have been advantageous to many organizations. However, these latest technologies have proven to cause more harm than value to organizations when not controlled. Many organizations have implemented data warehousing for reasons of performing and speeding up the server querying and reporting and other server duties. Speeding up of querying and reporting is done by the use of data modeling techniques (Greenfield, 2005).

Data warehousing also provides a user friendliness environment in which employees that have less technical knowledge can write down and maintain small queries and reports. Data warehousing is also used for storing processed and corrected data from the transaction process systems. Data warehousing also provides security for data that has been stored in databases so that unauthorized people may not change or modify information that was stored in the databases (Greenfield, 2005).

Data warehousing may be of no value to some organization if not controlled. This is because, data warehousing stores chronological data that may be of less interest to the organization. The indifference are generated when an organization compete with organizations in a perpetual process in which historical data is not needed. Business procedures can also be complicated by the use of data warehousing (Greenfield, 2005).

Data warehousing can cause clutters that can complicate the business procedures if it is unconstrained. When most of the transactions of data warehousing are met, data warehousing may not be of use to the organization. Data warehousing investment returns usually takes long to be realized making organization to be impatient. This shows that learning curves for data warehousing are usually long. Organizations are faced with the challenge of data warehousing maintenance increasing unnecessary costs for an organization (Greenfield, 2005).

In data warehousing, people might make mistakes of wiping out necessary data while storing other data in databases. People also store unnecessary data in databases therefore depleting the warehouses. In storing compressed data in order to save storing space, organizations may loose some of the important information needed. Data warehousing can also increase duplication of data therefore complicating the business processes (Parkinson, 2005).

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Data mining has also many uses that have been advantageous to organizations. Data mining is used for personalizing websites, detecting credit card scam. Data mining is also used for analyzing markets and data. Data mining can also be used for express mail marketing (Thearling, 2009). Data mining is believes to be a vital organizational initiative. Data mining helps organization retrieve information so as to make profitable decisions on business trends to gain a competitive advantage over other business organizations. Data mining provide the necessary information that organizations need for knowing their customers trends. Data mining also helps organizations understand their customers well so as to be able to amend their marketing strategies (Mitch, 2003).

Data mining can also cause harm to organizations. This is because people can use data mining for hacking into the organization’s system to retrieve. To prevent such cases from occurring in an organization, data mining applications can be used to identify people that are hacking into the organizations’ systems. These applications can discover credit card fraud since it is done from a centralized system in the organization. These systems can also discover deceitful employees in an organization and therefore identify losses that can occur (Hadfield, 2009).

Conclusion

Data warehousing and mining can turn into more trouble than of value if not controlled. These latest technologies have helped many organizations gain return on investments due to their benefits. Data warehousing can help in modeling and storing data that has been processed (Greenfield, 2005). Data mining has many advantages such as personalizing websites and detecting credit card scam (Thearling, 2009). However, if not controlled people may hack into organizations’ system. Thus, people need to control data warehousing and mining to avoid making these latest technology turn into more trouble than of value.

References

Greenfield, L. (2005). The Case for Data Warehousing. LGI Systems Incorporated. Web.

Greenfield, L. (2005). The Case Against Data Warehousing. LGI Systems Incorporated. Web.

Hadfield, M. (2009). Case Study: Jaeger uses data mining to reduce losses from crime and waste. Computer Weekly. Web.

Mitch, B. (2003). Unexpected Insights from Data Mining. Computerworld Inc. Web.

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Data Warehousing and Mining: Trouble or Worth?
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Parkinson, J. (2005). Pack-rat Approach to Data Storage is Drowning IT. CIO Insight. Web.

Thearling, K. (2009). An Introduction to Data Mining. Thearling.com. Web.

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