determines that regardless the definition is a “means” to “end”, which sound business or organizational decision making, specifically in strategic planning and management. It also presents several important differentiation of Business Intelligence from data-centric technologies, and enterprise applications. It describes business intelligence architecture and its components. It identifies capabilities and benefits to be derive from it, barriers to its successful implementation, and critical success factors
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NIBCO's customers? 4. What new initiatives would you recommend that NIBCO pursue next to continue to take advantage of its current IT capabilities? Case Study 11-2- Real-Time Business Intelligence at Continental Airlines 1. Describe "active" data warehousing as it is applied at Continental
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Data warehousing is a fairly new but not so new development in the information systems field. Data warehousing can be traced back being in existence since the 1980’s when Teradata in 1983 introduced a database management system (DBMS) designed for decision support systems (Ponniah, 2010). The influence from the two Irish IBM architects Barry Devlin and Paul Murphy who in 1988 laid the foundations for what we call today a data warehouse in their original article “An Architecture for a Business and
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division of NCR Corporation (NYSE: NCR), today announced that Wal-Mart has expanded its Teradata® data warehouse, increasing its lead as the largest retail data warehouse in the world” (para. 1). This Teradata warehouse was the basing for the company’s Retail Link decision support system between vendors and Wal-Mart. This grants suppliers to access large amounts of online, real-time data. This data helps those companies improve their operations. Jakovljevic (2005), " While Wal-Mart can match
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Data Warehousing Essay, Research Paper Contents 1. Introduction 2. What is a data warehouse 3. Past, Present and Future 4. Data Warehouses and Business Organisations 5. Conclusion 6. Bibliography 1.0 Introduction In recent years, data warehousing has emerged as the primary method of analysing sales and marketing data for a competitive advantage. As the number of knowledge workers using the data warehouse/data mart grows and the amount of data increases daily, performance problems
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A database is an organized collection of data. The data are typically organized to model aspects of reality in a way that supports processes requiring information. For example, modelling the availability of rooms in hotels in a way that supports finding a hotel with vacancies. Database management systems (DBMSs) are computer software applications that interact with the user, other applications, and the database itself to capture and analyze data. A general-purpose DBMS is designed to allow the
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Data Warehousing Methodologies Using a common set of attributes to determine which methodology to use in a particular data warehousing project. DATA INTEGRATION TECHNOLOGIES have experienced explosive growth in the last few years, and data warehousing has played a major role in the integration process. A data warehouse is a subjectoriented, integrated, time-variant, and nonvolatile collection of data that supports managerial decision making [4]. Data warehousing has been cited as the highest-priority
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Reynolds(Continental Airlines) Data management for decision support has moved through three generations, with the latest being real-time data warehousing. This latest generation is significant because of its potential for affecting tactical decision making and business processes. Continental Airlines is a leader in real-time business intelligence and much can be learned from how they have implemented it. The movement to real-time is the latest development in business intelligence (BI) and data warehousing.
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providing access to data that helps users to gain insights and make better fact-based business decisions. The basic components of Business Intelligence are gathering, storing, analysing and providing access to data (see Figure). Gathering Data Gathering data is concerned with collecting or accessing data which can then be used to inform decision making. Gathering data can come in many formats and basically refers to the automated measurement and collection of performance data. For example, these
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building and deploying DSS. The journey begins with building model-driven DSS in the late 1960s, theory developments in the 1970s, and implementation of financial planning systems, spreadsheet-based DSS and Group DSS in the early and mid 1980s. Data warehouses, Executive Information Systems, OLAP and Business Intelligence evolved in the late 1980s and early 1990s. Finally, the chronicle ends with knowledge-driven DSS and the implementation of Web-based DSS beginning in the mid-1990s. The field of computerized
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