Data Warehouse and State-of-the-art Application - Essay Example

 Data Warehouse In computing the term data warehouse (DW) refers to a centrally managed and integrated database containing both raw and formatted data received from various operational sources (in an organization), generally stored in an archival manner for easy access and used for reporting and analysis purposes as shown in fig. 1. Data warehousing is a process of centralized data management and retrieval (Greenfield, par.1). Data warehouses can have both current as well as historical data. The data received from various operational resources in an organization is analyzed using different data analysis tools like ETL, OLAP etc. to create various reports for senior management to take business decisions (Data Warehousing, par. 2). Fig. 1 Data warehousing empowers an organization to maintain and retrieve its operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Also nonoperational data, such as industry sales, forecast data, and macro-economic and meta data can be maintained and retrieved through proper data warehousing. An organization collects data about various market trends, consumer preference and buying patterns through various resources and managed through data warehouse. The collected data is then retrieved and analyzed using various data analysis tools. Finally based on data analysis findings new much improved business strategies are derived. Many organizations have incorporated data warehousing practices to increase its revenue and to improve investment strategies (Palace, par.16). What Is Considered a State‐of‐the‐art Application? The data warehousing is a multi-task activity and it quite different from traditional transaction-oriented operational database management activity. It involves various state – of – art application tools and techniques for aggregating and thereby summarizing large amounts of data that assistant in an effective data management and retrial. A state-of-the art application in the data warehousing is one that can achieve a near-real-time situation. Most organizations call for immediate decision making, which needs real time analysis, and presentation of decision making. Many data warehouse applications achieve this by shortening the loading cycles using micro batch ETL (extraction transformation and loading). These applications have unique characteristics which include: highly scalable data mining algorithms, ease of integration with other components, efficient and secure in data base processes, ease of use and user friendliness (may not require an in-depth database knowledge) and can support relevant standards. It is, however, important to have proper considerations while choosing the methodology to use in the implementation of the applications and also the entire system. Two options are available: to build one’s own system or purchase a system. Both of the approaches have both advantages and disadvantages; therefore, the organization should consider the individual components and the nature of activities before deciding which approach to use. In any case it is important to consider factors such as volume of work, user technical skills, availability of budget and the time available to carry out the activity (Prabhu, and Venkatesan, 20). What Have Been Notable Successes that We Can Emulate? There are numerous notable success stories of organization that can be emulated. One of such stories is Hallmark Cards. Hallmark Cards with $3.6 billion in annual sales and 20,000 employees, is a leader in the personal expressions industry. It has approximately 40,000 products selling through over 40,000 retail outlets. Since as a personal expression industry, it was very critical for the company business to understand what is happening at retail outlets in order to react quickly to changes in the environment. Hallmark utilized the warehouse environment with data mart along with decision support system (DSS) to help his management to understand market environment to launch a major new product line. By using data mart, Hallmark got a better understanding of marketplace hence it experiences an enormous increase in its returns and business (Marshall, 1). Another example of success is American Airlines that uses Sybase IQ to efficiently and effectively collects, stores and manages data in the database. The technology also allows for timely and fast querying of the database. This has enabled the organization to detect fraudulent ticket processing, properly monitor and track ticket sales and also ensure that a proper flow of revenue is realized in the company. The Airlines has received huge benefits and business advantages in the airline sector due to data warehousing (Hagen, 1). Have there been Failures from which We Can Learn?  Contrary to success, several failures have been experienced by other companies and organizations though this has not been publicized and published widely. The failure is, however, attributed to key considerations that may have been overlooked during the design and installation of the system. They include constituting a poor team, unrealistic goals, poor planning, and particularly the lack of resources. The data warehousing can be effectively applied in the Suite Spot organization purposely for reservation and client support and services. The application can be used to minimize errors and omissions during reservation at the hotel. This could be effectively implemented at the corporate level to allow reservations to be done from any point to any hotel under the organization. Just as American Airlines utilized the Sybase IQ, the organization can also utilize the system to manage its reservation by eliminating duplication, lost records and unrecorded transactions. The organization will have effective and efficient data collection storage, access and retrieval of data and information, improve customer satisfaction, eliminate data duplication, and more importantly, realize increased returns on investment. What We Should be Aware of? ETL processes represent the number one success factor for any data warehouse project as it can absorb up to 70 percent of the time spent on a typical warehousing project. Hence it is very important to aware and analyzes the factors that we should consider when determining and selecting an ETL tool to a data warehouse project. The first thing we should be aware in determining the ETL tools, is the complexity of the data transformation process. As ETL tools along with other common design and implementation tools, promise quick results, improved manageability and meta data integration hence it is more suitable to purchase the ETL tool when data transformation is more complex. Secondly as ETL tools selection involves potentially huge amounts of money in terms of purchase and licensing and may choosing the correct but expensive ETL tool for a data warehouse may ease the complexity of data warehousing yet it can present a daunting challenge to the project in terms of finance. The problem can be solved with a bit of internal questioning in advance followed by a careful review of organization key needs against the choices available on the market we can choose the most effective ETL tool for our project. Except above mentions aspect it is also important to consider data cleansing tools as most ETL tools provide only limited true data cleansing functions. If the data collected needs more cleansing before stored in the database, then it is advisable to purchase a tool with more cleansing functionalities or build one from scratch. The data volume to be processed is another major consideration that will determine whether to purchase the tool depending on the volume of data to be processed and transferred (Meyer, n. d.). Some of the common ETL tools include: IBM Web Sphere Information Integration (Accentual Data Stage), Ab Initio, Informatica, and Talent. Technology We Can Invest in Suite Spot One strategy that can help Suite Spot in their decision making process is that of CRM (Customer Relationship Management). For organization long term profitability it is very important that it must maintain long term relation to its customers. CRM integrates the concepts of Knowledge Management, Data Mining, and Data Warehousing in order to support the organization’s decision-making process to retain long- term and profitable relationships with its customers. This is an important application that will enable an organization to understand the customer dynamics and how the clients operate. It will help in analyzing the patterns of the customer. It will also aid in understanding the market trends and implement the right decisions (Cunningham, Il-Yeol Song, and Peter P. Chen, 1). Benefits of Investing on It There are benefits that will be achieved with the use of this technology. One is that the knowledge regarding the customers will be made simpler. The organization will be able to understand their customers well. Having enough customer knowledge is an important aspect of business. This way, they will be able to make the right decisions. For this reason, it is worth investing in this technology. Works Cited Cunningham, Il-Yeol Song, and Peter P. Chen. Data Warehouse Design to Support Customer Relationship Management Analyses. p. 1-3. Web. 8 Dec. 2012. Place, Bill. Data Mining. Anderson Graduate School of Management at UCLA, Spring 1996. Web. 7 Dec. 2012. Meyer, Steven R. “Which ETL Tool is Right for You?”. Information Management andsourceMedia. 1March, 2001: Web. 8 Dec. 2012. Marshall, Tony. Data Warehouse Success Story Hallmark Cards and MicroStrategy Hallmark Cards and MicroStrategy. n. d: 1- 3. Web. 7 Dec. 2012. Hagen, Johan. American Airlines,. n. d: 1-2. Web.7 Dec.2012. "Data Warehousing." 1keydata.com. 2012. Web. 25 Nov. 2012. Read More