Data Warehousing and Data Mining
Data Warehousing and Data mining
December, 9 2013
Data Mining and Data Warehousing
Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data mining is a process where these corporations extract large amounts of data to help them analyze it from multiple angles. Now for Data warehousing is a process designed for analysis and queries more than transaction processing centralizing the data from multiple sources not just online, but from transactions in stores and over the phone as well as other sources of procurement. This localizes the data placing it in to common models such as; names and definition. Data mining and data warehousing can be extremely helpful and strong tools there are some organizations that struggle with the information such as airlines, people are still in a frenzy to find some sort of pattern (Revels, M., & Nussbaumer, H. 2013).
For all this to come together quickly and accurately it is important to choose the right modeling and in essence of choosing the right questions also. This data routinely analyzes customer trends and patterns to best help predict which items should be reduced or put up for sale and where the company’s investment best be utilized at (Revels, M., & Nussbaumer, H. 2013). Yet again this goes back to choosing the correct database modeling for each company’s ability to succeed in this ever-growing market that has resulted in
December, 9 2013
Data Mining and Data Warehousing
Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data mining is a process where these corporations extract large amounts of data to help them analyze it from multiple angles. Now for Data warehousing is a process designed for analysis and queries more than transaction processing centralizing the data from multiple sources not just online, but from transactions in stores and over the phone as well as other sources of procurement. This localizes the data placing it in to common models such as; names and definition. Data mining and data warehousing can be extremely helpful and strong tools there are some organizations that struggle with the information such as airlines, people are still in a frenzy to find some sort of pattern (Revels, M., & Nussbaumer, H. 2013).
For all this to come together quickly and accurately it is important to choose the right modeling and in essence of choosing the right questions also. This data routinely analyzes customer trends and patterns to best help predict which items should be reduced or put up for sale and where the company’s investment best be utilized at (Revels, M., & Nussbaumer, H. 2013). Yet again this goes back to choosing the correct database modeling for each company’s ability to succeed in this ever-growing market that has resulted in
References: Revels, M., & Nussbaumer, H. (2013). Data Mining and Data Warehousing in the Airline Industry. Academy Of Business Research Journal, 369-82. Jukic, N. (2006). MODELING STRATEGIES AND ALTERNATIVES FOR DATA WAREHOUSING PROJECTS Sen, A., & Sinha, A. P. (2005). A COMPARISON OF DATA WAREHOUSING METHODOLOGIES. Communications Of The ACM, 48(3), 79-84. Coskun Samli, A. A., Pohlen, T. L., & Bozovic, N. (2002). A Review of Data Mining Techniques as they Apply to Marketing: Generating Strategic Information to Develop Market Segments