Continental Airline Casestudy
Read the Case Study, Continental Airlines: Flying High with Its Data Warehouse, at the end of Ch. 4 in the text. Write a 350- to 700-word paper as a Microsoft Word document, describing Continental’s strategy.
Why have other airlines not adopted a program similar to Continental’s? How can IT be used to improve customer service and satisfaction? Describe the Data Warehouse components Continental is using according to section 4. 4 in the text. Describe the relationship amongst data, information, and knowledge, and how Continental addresses each. Format your paper consistent with APA guidelines 4. 4 Data Warehousing
Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers, as shown in the Continental Airlines case at the end of the chapter.
The problem is providing users with access to corporate data so that they can analyze it. Let’s look at an example. 116 CHAPTER 4 Data and Knowledge Management SECTION 4. 4 Data Warehousing 117 If the manager of a local bookstore wanted to know the profit margin on used books at her store, she could find out from her database, using SQL or QBE.
However, if she needed to know the trend in the profit margins on used books over the last 10 years, she would have a very difficult query to construct in SQL or QBE.
The bookstore manager’s problem shows us two reasons why organizations are building data warehouses. First, the organization’s databases have the necessary information to answer her query, but it is not organized in a way that makes it easy for her to search for needed information and insight. Also, the organization’s databases are designed to process millions of transactions per day.
Therefore, complicated queries might take a long time to answer and also might degrade the performance of the databases. As a result of these problems, companies are using data warehousing and data mining tools to make it easier and faster for users to access, analyze, and query data.
Data mining tools (discussed in Chapter 9) allow users to search for valuable business information in a large database or data warehouse. Describing the Data Warehouse A data warehouse is a repository of historical data that are organized by subject to support decision makers in the organization.
Data warehouses facilitate business intelligence activities, such as data mining, decision support, and querying applications (discussed in Chapter 9). The basic characteristics of a data warehouse include: • Organized by business dimension or subject. Data are organized by subject (e.
g. , by customer, vendor, product, price level, and region) and contain information relevant for decision support and data analysis. • Consistent. Data in different databases may be encoded differently. For example, gender data may be encoded 0 and 1 in one operational system and “m” and “f ” in another.
In the data warehouse, though, all data must be coded in a consistent manner. • Historical. The data are kept for many years so that they can be used for identifying trends, forecasting, and making comparisons over time. • Nonvolatile. Data are not updated after they are entered into the warehouse. • Use online analytical processing.
Typically, organizational databases are oriented toward handling transactions. That is, databases use online transaction processing (OLTP), where business transactions are processed online as soon as they occur.
The objectives are speed and efficiency, which are critical to a successful Internet-based business operation. Data warehouses, which are not designed to support OLTP but to support decision makers, use online analytical processing. Online analytical processing (OLAP) involves the analysis of accumulated data by end users.
• Multidimensional. Typically, the data warehouse uses a multidimensional data structure. Recall that relational databases store data in two-dimensional tables. In contrast, data warehouses store data in more than two dimensions.
For this reason, the data are said to be stored in a multidimensional structure.
A common representation for this multidimensional structure is the data cube. The data in the data warehouse are organized by business dimensions, which are the edges of the data cube and are subjects such as functional area, vendor, product, geographic area, or time period (look ahead briefly to Figure 4. 11). Users can view and analyze data from the perspective of the various business dimensions. This analysis is intuitive because the dimensions are in business terms, easily understood by users. Relationship with relational databases.
The data in data warehouses come from the company’s operational databases, which can be relational databases. Figure 4. 9 illustrates the process of building and using a data warehouse. The organization’s data are stored in operational systems (left side of the figure). Using special software called extract, transform, and load (ETL), the system processes data and then stores them in a data warehouse.
Not data is transferred. Within the warehouse the data are organized in a form that is easy for end users to access.
To differentiate between relational and multidimensional databases, suppose your company has four products—nuts, screws, bolts, and washers—which have been sold in three territories— East, West, and Central—for the previous three years—2006, 2007, and 2008. In a relational database, these sales data would look like Figures 4. 10a, b, and c.
In a multidimensional database, these data would be represented by a three-dimensional matrix (or data cube), as shown in Figure 4. 11. We would say that this matrix represents sales dimensioned by products and regions and year. Notice that in Figure 4. 0a we can see only sales for 2006. Therefore, sales for 2007 and 2008 are shown in Figures 4.
10b and 4. 10c, respectively. Figure 4. 12 shows the equivalence between these relational and multidimensional databases. Companies have reported hundreds of successful data-warehousing applications. For example, you can read client success stories and case studies at the Web sites of vendors such as NCR Corp.
(www. ncr. com) and Oracle (www. oracle. com). For a more detailed discussion, visit the Data Warehouse Institute (www.
tdwi. org). Some of the benefits of data warehousing include the following: End users can access needed data quickly and easily via Web browsers because they are located in one place. • End users can conduct extensive analysis with data in ways that may not have been possible before. • End users can have a consolidated view of organizational data. These benefits can improve business knowledge, provide competitive advantage, enhance customer service and satisfaction, facilitate decision making, and streamline business processes.
IT’s about Business 4. 2 demonstrates the benefits of data warehousing at the New York Police Department.
Data warehouses do have problems. First, they can be very expensive to build and to maintain. Second, incorporating data from obsolete mainframe systems may be difficult and inexpensive. Finally, people in one department might be reluctant to share data with other departments.
Data Marts Because data warehouses are so expensive, they are used primarily by large companies. Many other firms employ a lower-cost, scaled-down version of a data warehouse called a data mart. A data mart is a small data warehouse that is designed for end-user needs in a strategic business unit (SBU) or a department.