Journal of Theoretical and Applied Information Technology © 2005 - 2009 JATIT. All rights reserved. www.jatit.org BUSINESS INTELLIGENCE: CONCEPTS‚ COMPONENTS‚ TECHNIQUES AND BENEFITS JAYANTHI RANJAN Institute of Management Technology‚ Ghaziabad‚ Uttar Pradesh‚ India Email: jranjan@imt.edu ABSTRACT For companies maintaining direct contact with large numbers of customers‚ however‚ a growing number channel-oriented applications (e.g. e-commerce support‚ call center support) create a new data
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A Seminar Report on BUSINESS INTELLIGENCE Prepared by: Guided By: Arpan Solanki Prof.Yagnik A. Rathod 100410107063 Assistant professor TY C.E SVIT-VASAD Certificate
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Tutorial – Concepts and Principles of BI Objectives - On completion of this tutorial‚ you should be able to: 1. understand the importance of a data warehouse 2. identify strategic advantages that an organization can derive from a data warehouse Case : Continental Airlines Flies High with its Real-time Data Warehouse As Business Intelligence (BI) becomes a critical component of daily operations‚ real-time data warehouses (DW) that provide end users with rapid updates and alerts
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Business intelligence (BI) processes monitor and analyze business transaction processes to ensure that they are optimized to meet the business goals of the organization. These goals may be operational goals that affect daily business operations‚ tactical goals that involve short-term programs such as marketing campaigns‚ or strategic goals that entail long-term objectives like increasing revenues and reducing costs. This is a kind of predictive analytics which helps to give idea about most critical
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Effective Business Intelligence for SME As smaller firms face competition and grow‚ it’s imperative they make good decisions based on even better information When asked what business intelligence (BI) tools are used to measure their organizational performance‚ the common response by entrepreneurs might include Excel spreadsheets‚ report writers and canned reports. BI can be defined as the ability to extract actionable insight from data available to the organization‚ both internal and external‚
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DISSERTATION of the University of St.Gallen‚ Graduate School of Business Administration‚ Economics‚ Law and Social Sciences (HSG) to obtain the title of Doctor of Business Administration submitted by Florian Fuhl from Germany Approved on the application of Prof. Dr. Li Choy Chong and Prof. Dr. Narendra Agrawal Dissertation No. 3211 Druckerei Lauterberg‚ Ketzin‚ 2006 The University of St.Gallen‚ Graduate School of Business Administration‚ Economics‚ Law and Social Sciences (HSG) hereby
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Business analytics and data mining provided 1-800-Flowers with all of the following benefits except: Select one: a. more efficient marketing campaigns b. increased mailings and response rates c. increased repeat sales d. better customer experience and retention On the commercial side‚ the most common use of data mining has been in ________ sectors. Select one: a. manufacturing and heath care b. online retail and government c. R&D and scientific d. finance‚ retail‚ and health
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Data mining and OLAP are the most common Business Intelligence technologies. The term Business Intelligence refers to computer based methods to identify and extract useful information from business data. Online Analytical Processing commonly known as OLAP provides summary data and generates rich calculations. OLAP is a class of systems that provide answers to multidimensional queries. OLAP is typically used in business reporting for sales‚ marketing and various such domains. OLAP enables the users
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Business Intelligence Definitions • Data mining (knowledge discovery in databases): – Extraction of interesting (non-trivial‚ implicit‚ previously unknown and potentially useful) information or patterns from data in large databases • Data mining helps end users extract useful business information from large databases • Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. • The goal of data mining may be to allow a corporation
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6497 instances was considered for training. The entire set of data was again considered for cross validation of the model created from training data. From the data set‚ 21 records was chosen for prediction of class. Data mining technique used Classification technique has been used for the project which incorporates analysis of training set and test set to determine the relationship between various attributes with the class and also determines the accuracy of the training set analysis and test
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