Benefits Of Data Warehouse - Research Paper Example

Benefits Of Data Warehouse, OlAP And Data Mining For Business Applications Introduction 3 Objectives 3 Data warehouse 4 OLAP 4 Data Mining 4 Methodology 5 Results and analysis 6 Intelligent decisions 6 Elimination of biases in decision making 6 Cost of decision making reduced 6 Data integrity maintained 7 Conclusion 7 References 8 Introduction Businesses in the modern day are storing a lot of information about their clients, sales, production and strategies. The online analytical processing (OLAP) and data mining are the greatest analysis mode used in the modern world. Data warehouse has been a principal means of storage of data without the capability of utilizing it. The warehouse provides a means to access the data by the different tools compatible with it. The integration of the three technologies is a significant benefit to business in the way they use data. Developers of these three technologies are recently integrating them into one package. This eradicates the need of specialists installing them after purchasing the database (Ling and Tamer, 2009). Objectives The research will establish the different areas of running businesses that this technology can be applied. Some of the areas to be checked are budgeting, sales forecasting and determining whether to produce a new product or not. The main objective will be establishing the benefits accrued by multinational businesses when they carry out data mining in their data warehouse. This information will be used in determining if these benefits can be passed to small scale businesses without the capability of purchasing this technology (O’Brien & Marakas, 2009). The research will establish the cost benefit that businesses receive by using this technology. Cost benefit will establish the opportunity cost of using this technology instead of manual data analysis using the stand alone software. Data warehouse The data warehouse is a collection of data which helps management in decision making. The warehouse has a capability of providing enormous amount of information at the same time. This makes it a better tool for use by businesses that are storing amount of information about their production operation process. The data warehouse ability of integration with modern analysis tools like OLAP is an advantage to corporate. This helps in timely and correct analysis of data, which can be used in decision making by managers (Agosta, 2004). OLAP These are software tools that enable data analyst analyze data and view it from multiple perspective. The multiple perspective help managers analyze different options of a problem before arriving at a conclusion. OLAP support activities like analyzing the financial trends in businesses. This would bring to the attention a manager a production line that cost of production is rising without any additional benefits accruing from it. OLAP tools are easy to use and do not require massive investment because some of them can be used in desktop machines. Managers would also like to get relationship between different outcomes in the businesses. This tools ability to provide relationship in data can be of immense help in these situations (Daniel, 2007). Data Mining Data mining involves finding particular information in a huge amount of data to acquire knowledge. Businesses with huge data would like to establish patterns in their data. These patterns may include sales pattern or production pattern. Data mining tools will provide these patterns by looking at the huge amount of data in the business data warehouse. Data mining enables businesses analysts to create models such as predictive models. These models can be used in telling the future outcomes in businesses after taking certain activities. These models reduce the cost of doing research because of their ability to represent to act abstract representation of the real world situation (Belsley, Kuh, & Welsch, 1980). Methodology The methodology of carrying out research involved visiting businesses that use this technology to acquire first hand information. Research showed data warehousing, data mining and OLAP analysis were greatly used in the banking industry. One of the bank informational technology answered questions about the topic but sort anonymity because of the security of the job. One of the questions asked is there any importance of carrying data mining in businesses. Another question was efficiency achieved in business by using these technologies. This showed how data mining was used in making business decisions. The personnel also said this technology reduced the cost of running businesses by reducing the number of personnel required. Literature review of businesses that were already using this technology was also applied. The literature review was out carried by reading online websites, which provide rich and accurate information. The review also included studying the oracle literature on business application of OLAP (Erik,1997). This literature review would also establish the cost of acquiring this technology on businesses. The literature review was preferred because it would do away with any biomasses in information provision. The research also involved reading texts on this technology that involves businesses. These text books provided information that is crucial in identifying positive and negative effects of this technology. Analysis of the gathered information was carried. The agreed upon information was documented to form the final part of the research. Results and analysis It was established that this technology has not been taken over by small scale businesses. They were seen as businesses that preferred the manual filing of business data. Expertise and cost required in use of these tools was the main hindrance to them in taking up the technology (Chen, Soliman, Mao, & Frolick, 2000). The research established the following as some of the benefits of taking over this technology by businesses. Intelligent decisions The research revealed that this technology has been used by businesses in making intelligent decisions. Neural networks are used in data mining to solve current problems using past data. The problems solved required human expertise which was not readily available. (Linstedt, Graziano & Hultgren, 2010). Elimination of biases in decision making OLAP has been applied by business managers in carrying data analysis to provide alternatives. The alternatives provided by this tool are objective in nature because of their use of underlying data to generate them. Leaving managers to come with these objectives will make them bring their own personal perception in the problem. These leads to making a decision that is from the managers’ perspective rather than the businesses problem perspective. Cost of decision making reduced Data mining and OLAP tools are used by decision makers abundantly in the modern world. They eliminate the need of employing a large workforce in businesses to carry out analysis on data before it is forwarded to the decision makers. This does not mean that these tools totally eliminate human workers in decision making. Human beings are still needed to operate these tools (Davenport, Thomas, and Harris, 2007). Data integrity maintained The biggest asset to a business today is its data. Data warehouse will provide protection to this data by eliminating unauthorized access. Integrity can be lost when data is accessed by unauthorized personnel either when it is in transit or storage. The data warehouse has a location transparency by ensuring person accessing data do not know the location of the data. Data encryption services are also provided in the data warehouse. The encrypted data cannot be decrypted by any unauthorized person (Kimball, and Ross, 2002). Conclusion The benefits that large enterprises have accrued from the use of this technology justify the need of encouraging small business to take up data warehousing. The large businesses have been able to reduce the cost of running and making decisions by using OLAP in analysis. These benefits have vastly improved on the quality of decisions that are made by eliminating biases. The data warehouse has also been seen to provide unlimited storage space for the businesses operation data which might be required in the future. This technology has been seen to utilize past and present data in decision making, which leads to making of an inclusive decision. Data mining has provided a way of making intelligent businesses systems that help in solving current unpredictable decisions. References Agosta, L. (2004). Data warehousing lessons learned: A time of growth for data warehousing. New York: John Wiley& Sons. Belsley, D., Kuh, E., & Welsch, R. (1980). Regression diagnosis: Identifying influential data and sources of co linearity. New York: John Wiley & Sons. Chen, L., Soliman, K., Mao, E., & Frolick, M. (2000). Measuring user satisfaction with data warehouses. Harvard: Harvard Business School Press. Daniel, L. (2007). "Data warehousing and OLAP-A research-oriented bibliography. Boston, MA: McGraw-Hill/Irwin. Davenport, Thomas, H., and Harris.(2007). Competing on analytics: The new science of winning. Harvard: Harvard Business School Press. Erik, T. (1997). OLAP Solutions: Building Multidimensional Information Systems. New York: John Wiley & Sons. Kimball, R., and Ross, M.(2002). The data warehouse toolkit. New York: John Wiley and Sons. Ling, L., and Tamer. (2009). Encyclopedia of database systems. New York: Apress Linstedt, Graziano & Hultgren .(2010). The business of data vault modeling. Boston :Daniel Linstedt O’Brien, J., & Marakas, G. (2009). Management information systems. Boston, MA: McGraw- HillIrwin. Read More