Businesses and organizations of all sizes are becoming increasingly dependent on data analytics, and data warehouses or business analytic infrastructure has become a business critical application for many (if not most) companies. Indeed, these companies have always searched for better ways to understand their customers, and anticipate their needs. They have longed to improve the speed and accuracy of operational decision-making. Equally important as timeliness is the depth of the data analysis. Generally, the companies want to decipher all secrets hidden within the massive amounts of ever-increasing data. A data warehouse appliance, which is an integrated collection of hardware and software designed for a specific purpose typically involving the high throughput of data and analytic functions, can be used by organizations to optimize various areas of data processing. Its main intent is to supplant conventional business intelligence functions, such as warehousing, extract-transform-load (ETL), analysis and reporting. Due to its cost-effectiveness and efficiency, the data warehouse appliance has become an important segment of the data warehousing market. In this paper, I will examine the data warehouse appliances and describe its positive impact on business enterprises.
Since introduced in the early 1990s, data warehouse (DW) has proven to be the key platform for strategic and tactical decision support systems in the competitive business environment today. It has become a major technology for building data management infrastructure, and resulted in many benefits for various organizations, including providing “a single version of the truth, better data analysis and time savings for users, reductions in head count, facilitation of the development of new applications, better data, and support for customer-focused business strategies” (Rahman, 2007). The technology has become extremely important in an environment where increasing competition, unpredictable market fluctuations, and changing regulatory environments are putting pressure on business organizations. Data warehouses are also becoming the central repositories of organization/company information for data, which is obtained from a variety of operational data sources. Business applications will find data warehouses more beneficial and rely on them as the main source of information as they progress. These applications are able to perform all sorts of data analysis, with increasing customer demands for having the most up-to-date information available in data warehouses. Improving data freshness within short time frames is essential to meeting such demands. According to Hong et al, virtually all Fortune 1000 companies, today, have data warehouses, and many medium and small sized firms are developing them. The desire to improve decision-making and organizational performance is the fundamental business driver behind data warehouses. DW help managers easily discover problems and opportunities sooner, and widen the scope of their analysis. Hong also mentions that data warehouse is user-driven, meaning that users are allowed to be in control of the data and will have the responsibility of determining and finding the data they need. But however, the data warehouses have to be designed and evaluated from the user perspective in order to motivate users to be responsible for finding the data they need. Data warehouse is said to be “one of the most powerful decision-support tools to have emerged in the last decade” (Ramamurthy, 2008). They are developed by firms to help managers answer important business questions which require analytics including data slicing and dicing, pivoting, drill-downs, roll-ups and aggregations. And these analytics are best supported by online-analytical processing (OLAP) tools. A data warehouse appliance, which is the main topic of discussion in this research, is referred to as an integrated collection of hardware and software...
References: O 'Brien, J. A. & Marakas, G. M. (1999). Management Information Systems (9th edition). 190- 193. New York, NY: McGraw-Hill/Irwin.
Kaula, R. (2009). Business Rules for Data Warehouse. International Journal of Information Technology. Retrieved March 22, 2011, from Business Source Complete.
McKnight, W. (2005). Introducing the Data Warehouse Appliance. DM Review. 15(3), 16-16. Retrieved March 22, 2011, from Business Source Complete.
Hong, S., Katerattanakul, P., Hong, S. & Cao, Q. (2006). USAGE AND PERCEIVED IMPACT OF DATA WAREHOUSES: A STUDY IN KOREAN FINANCIAL COMPANIES. International Journal of Information Technology & Decision Making. 5(2), 297-315. Retrieved March 22, 2011, from ABI/Inform Complete.
Hinshaw, F. (2004). Data Warhouse Appliances: Driving the Business Intelligence Revolution. DM Review. Retrieved March 22, 2011, from Business Source Complete.
Ramamurthy, K., Sen, A. & Sinha, A. P. (2008). An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems. 44(4), 817-841. Retrieved March 22, 2011, from ScienceDirect.
Watson, H. J. & Wixom, B. H. (2007). The current state of Business Intelligence. IT Systems Perspective. 96-99. Retrieved April 23, 2011, from http://www.teradata.com/library/pdf/IEEEComputerWatsonWixom.pdf
Wikipedia. (2011). Data warehouse appliance. Wikimedia Foundation Inc. Retrieved April 24, 2011, from http://en.wikipedia.org/wiki/Data_warehouse_appliance#Benefits
Briggs, L. L. & Eve, R. (2010). Q&A: Pair Appliances and Virtualization to Beef Up Performance. TDWI.org. Retrieved April 24, 2011, from http://tdwi.org/Articles/2010/08/25/Appliances-and-Virtualization.aspx?Page=2
Please join StudyMode to read the full document