EXECUTIVE SUMMARY Apple Computer’s 30-year history is full of highs and lows‚ which is what we would expect in a highly innovative company. They evolved throughout the years into an organization that is very much a representation of its leader‚ Steven Jobs. Apple made several hugely successful product introductions over the years. They have also completely fallen on their face on several occasions. They struggled mightily while Jobs was not a part of the organization. Apple reached a point where
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Introduction: The report focuses on data mining approach to predict human wine taste preferences. A large data set is considered with white and red wine samples (“Vinho Verde” wine from Portugal). The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Due to privacy and logistic issues‚ only physicochemical (inputs) and
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Business Intelligence projects start out as a simple report or request for an extract of data. Once the base data is aggregated then the next request usually is about summing data or creating more reports that have different views to the data sets. Before long complex logic comes into play and the metrics coming out of the system are very important to many corporate wide citizens. "Centrally managed business rules enable BI projects to draw from the business know-how of a company and to work with
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Multiple Linear Regression Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors (variable selection) Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target Familiar use of regression in data analysis Multiple linear
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Assignment 3: Business Intelligence and Data Warehouses Instructor Name: Jan Felton CIS 111 6/23/2014 Strayer University: Piscataway Difference between the structure of database and warehouse transaction Database is designed to make transactional systems that run efficiently. Characteristically‚ this is type of database that is an online transaction processing database. An electronic strength record system is a big example of a submission that runs on an OLTP database. An OLTP database is typically
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2.1. DATA AND INFORMATION Data Data is the raw materials from which information is generated. Data are raw facts or observations typically about physical phenomena or business transactions. It appears in the form of text‚ number‚ figures or any combination of these. More specifically data are objective measurements of the attributes (the characteristics) of entities (such as people‚ places‚ things and events) According to Loudon and Loudon- “Streams of raw facts representing events
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Touro University International ITM501 - Management Information Systems and Business Strategy Module 2 Case Assignment: Business Intelligence Systems 04 June 2010 Business intelligence: Definition Business Intelligence (BI) is defined by IBM as‚ “the discipline that combines services‚ applications and technologies to gather‚ manage and analyze data‚ transforming it into usable information to develop insight and understanding needed to make informed decisions.” (IBM.com‚ 2006) In its most
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Interdisciplinary Journal of Information‚ Knowledge‚ and Management Volume 1‚ 2006 Business Intelligence Systems in the Holistic Infrastructure Development Supporting Decision-Making in Organisations Celina M. Olszak and Ewa Ziemba University of Economics‚ Katowice‚ Poland olszak@ae.katowice.pl ewa@ae.katowice.pl Abstract The paper aims at analysing Business Intelligence Systems (BI) in the context of opportunities for improving decision-making in a contemporary organisation. The
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Managing Knowledge in organizations G.O. KAYODE-ADEDEJI SCHOOL OF ENGINEERING‚ DESIGN and TECHNOLOGY UNIVERSITY OF BRADFORD G.O.Kayode-Adedeji@bradford.ac.uk 2011 [Type the company name] 1/1/2011 Contents Introduction 2 KNOWLEDGE MANAGEMENT VS INFORMATION MANAGEMENT 5 KNOWLEDGE MANAGEMENT CONTROVERSIES 5 POSSIBLE CONSTRAINTS IN THE IMPLEMENTATION OF A KNOWLEDGE MANAGEMENT PROGRAM 6 CASE STUDY ON THE SUCCESSFUL IMPLEMENTATION OF KM: 6 THE EVOLUTION OF KM AT BUCKMAN LABORATORIES
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Strategic Business Objectives Operational Excellence New products‚ services and business models Customer and Supplier Intimacy Improved Decision Making Competitive Advantage Survival Value Chain Model Primary Activities Inbound Logistics (warehousing systems) Operations (machining systems) Sales and Marketing (electronic ordering) Service (equipment maintenance) Outbound Logistics (automated shipment scheduling) Support Activities Admin/Management (messaging/scheduling) Infrastructure
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