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Book Cover
Author Sharda, Ramesh.

Title Business intelligence : a managerial perspective on analytics / Ramesh Sharda, Oklahoma State University, Dursun Delen, Oklahoma State University, Efraim Turban, University of Hawaii ; with contributions by J. E. Aronson, The University of Georgia, Ting-Peng Liang, National Sun Yat-sen University, David King, JDA Software Group, Inc
Edition Third edition
Published Boston Pearson, [2014]


Location Call no. Vol. Availability
 W'BOOL  658.472 Sha/Bia 2014  AVAILABLE
 W'BOOL  658.472 Sha/Bia 2014  AVAILABLE
 MELB  658.472 Sha/Bia 2014  AVAILABLE
 MELB  658.472 Sha/Bia 2014  AVAILABLE
 MELB  658.472 Sha/Bia 2014  AVAILABLE
 MELB  658.472 Sha/Bia 2014  AVAILABLE
 MELB  658.472 Sha/Bia 2014  AVAILABLE
Description 386 pages : illustrations ; 26 cm
Contents Contents note continued: 2.5.Data Integration and the Extraction, Transformation, and Load (ETL) Processes -- Data Integration -- Application Case 2.3 BP Lubricants Achieves BIGS Success -- Extraction, Transformation, and Load -- 2.6.Data Warehouse Development -- Application Case 2.4 Things Go Better with Coke's Data Warehouse -- Data Warehouse Development Approaches -- Application Case 2.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing -- Additional Data Warehouse Development Considerations -- Representation of Data in Data Warehouse -- Analysis of Data in Data Warehouse -- OLAP Versus OLTP -- OLAP Operations -- 2.7.Data Warehousing Implementation Issues -- Application Case 2.6 EDW Helps Connect State Agencies in Michigan -- Massive Data Warehouses and Scalability -- 2.8.Real-Time Data Warehousing -- Application Case 2.7 Egg Plc Fries the Competition in Near Real Time -- 2.9.Data Warehouse Administration, Security Issues, and Future Trends --
Contents note continued: 4.2.Data Mining Concepts and Applications -- Definitions, Characteristics, and Benefits -- Application Case 4.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics -- How Data Mining Works -- Application Case 4.2 Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources -- Data Mining Versus Statistics -- 4.3.Data Mining Applications -- Application Case 4.3 A Mine on Terrorist-Funding -- 4.4.Data Mining Process -- Step 1: Business Understanding -- Step 2: Data Understanding -- Step 3: Data Preparation -- Step 4: Model Building -- Step 5: Testing and Evaluation -- Step 6: Deployment -- Application Case 4.4 Data Mining in Cancer Research -- Other Data Mining Standardized Processes and Methodologies -- 4.5.Data Mining Methods -- Classification -- Estimating the True Accuracy of Classification Models --
Contents note continued: 7.7.Impacts of Analytics In Organizations: An Overview -- New Organizational Units -- Restructuring Business Processes and Virtual Teams -- Job Satisfaction -- Job Stress and Anxiety -- Analytics' Impact on Managers' Activities and Their Performance -- 7.8.Issues of Legality, Privacy, and Ethics -- Legal Issues -- Privacy -- Recent Technology Issues in Privacy and Analytics -- Ethics in Decision Making and Support -- 7.9.An Overview of the Analytics Ecosystem -- Analytics Industry Clusters -- Data Infrastructure Providers -- Data Warehouse Industry -- Middleware/BI Platform Industry -- Data Aggregators/Distributors -- Analytics-Focused Software Developers -- Reporting/Analytics -- Predictive Analytics -- Prescriptive Analytics -- Application Developers or System Integrators: Industry Specific or General -- Analytics User Organizations -- Analytics Industry Analysis and Influencers -- Academic Providers and Certification Agencies -- Key Terms --
Contents note continued: Application Case 3.4 TIBCO Spotfire Trovides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials -- 3.4.Different Types of Charts and Graphs -- Basic Charts and Graphs -- Specialized Charts and Graphs -- 3.5.The Emergence of Data Visualization and Visual Analytics -- Visual Analytics -- High-Powered Visual Analytics Environments -- 3.6.Performance Dashboards -- Dashboard Design -- Application Case 3.5 Dallas Cowboys Score Big with Tableau and Teknion -- Application Case 3.6 Saudi Telecom Company Excels with Information Visualisation -- What to Look For in a Dashboard -- Best Practices in Dashboard Design -- Benchmark Key Performance Indicators with Industry Standards -- Wrap the Dashboard Metrics with Contextual Metadata -- Validate the Dashboard Design by a Usability Specialist -- Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard -- Enrich Dashboard with Business-User Comments --
Contents note continued: Application Case 4.5 2degrees Gets a 1275 Percent Boost in Churn Identification -- Cluster Analysis for Data Mining -- Association Rule Mining -- 4.6.Data Mining Software Tools -- Application Case 4.6 Data Mining Goes to Hollywood: Predicting Financial Success of Movies -- 4.7.Data Mining Privacy Issues, Myths, and Blunders -- Data Mining and Privacy Issues -- Application Case 4.7 Predicting Customer Buying Patterns The Target Story -- Data Mining Myths and Blunders -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- 5.1.Opening Vignette: Machine Versus Men on Jeopardy!: The Story of Watson -- 5.2.Text Analytics and Text Mining Overview -- Application Case 5.1 Text Mining for Patent Analysis -- 5.3.Natural Language Processing -- Application Case 5.2 Text Mining Improves Hong Kong Government's Ability to Anticipate and Address Public Complaints -- 5.4.Text Mining Applications --
Contents note continued: Application Case 6.4 Big Data and Analytics in Politics -- 6.6.Big Data and Data Warehousing -- Use Cases for Hadoop -- Use Cases for Data Warehousing -- The Gray Areas (Any One of the Two Would Do the Job) -- Coexistence of Hadoop and Data Warehouse -- 6.7.Big Data Vendors -- Application Case 6.5 Dublin City Council Is Leveraging Big Data to Reduce Traffic Congestion -- Application Case 6.6 Creditreform Boosts Credit Rating Quality with Big Data Visual Analytics -- 6.8.Big Data And Stream Analytics -- Stream Analytics Versus Perpetual Analytics -- Critical Event Processing -- Data Stream Mining -- 6.9.Applications of Stream Analytics -- e-Commerce -- Telecommunications -- Application Case 6.7 Turning Machine-Generated Streaming Data into Valuable Business Insights -- Law Enforcement and Cyber Security -- Power Industry -- Financial Services -- Health Sciences -- Government -- Key Terms -- Questions for Discussion -- Exercises --
Contents note continued: Development Cycle -- Web Crawler -- Document Indexer -- Response Cycle -- Query Analyzer -- Document Matcher/Ranker -- Search Engine Optimization -- Methods for Search Engine Optimization -- Application Case 5.7 Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sales Increase -- 5.9.Web Usage Mining (Web Analytics) -- Web Analytics Technologies -- Application Case 5.8 Allegro Boosts Online Click-Through Rates by 500 Percent with Web Analysis -- Web Analytics Metrics -- Web Site Usability -- Traffic Sources -- Visitor Profiles -- Conversion Statistics -- 5.10.Social Analytics -- Social Network Analysis -- Social Network Analysis Metrics -- Application Case 5.9 Social Network Analysis Helps Telecommunication Firms -- Connections -- Distributions -- Segmentation -- Social Media Analytics -- How Do People Use Social Media? -- Application Case 5.10 Measuring the Impact of Social Media at Lollapalooza --
Contents note continued: End-of-Chapter Application Case -- References -- 7.1.Opening Vignette: Oklahoma Gas and Electric Employs Analytics to Promote Smart Energy Use -- 7.2.Location-Based Analytics for Organizations -- Geospatial Analytics -- Application Case 7.1 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions -- Real-Time Location Intelligence -- Application Case 7.2 Quiznos Targets Customers for its Sandwiches -- 7.3.Analytics Applications for Consumers -- Application Case 7.3 A Life Coach in Your Pocket -- 7.4.Recommendation Engines -- 7.5.The Web 2.0 Revolution and Online Social Networking -- Representative Characteristics of Web 2.0 -- Social Networking -- A Definition and Basic Information -- Implications of Business and Enterprise Social Networks -- 7.6.Cloud Computing and BI -- Service-Oriented DSS -- Data-as-a-Service (DaaS) -- Information-as-a-Service (Information on Demand) (IaaS) -- Analytics-as-a-Service (AaaS) --
Contents note continued: Justification and Cost-Benefit Analysis -- Security and Protection of Privacy -- Integration of Systems and Applications -- 1.7.Analytics Overview -- Descriptive Analytics -- Predictive Analytics -- Application Case 1.2 Eliminating Inefficiencies at Seattle Children's Hospital -- Application Case 1.3 Analysis at the Speed of Thought -- Prescriptive Analytics -- Application Case 1.4 Moneyball: Analytics in Sports and Movies -- Application Case 1.5 Analyzing Athletic Injuries -- Analytics Applied to Different Domains -- Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network -- Analytics or Data Science? -- 1.8.Brief Introduction to Big Data Analytics -- What Is Big Data? -- Application Case 1.7 Gilt Groupe's Flash Sales Streamlined by Big Data Analytics -- 1.9.Plan of the Book -- 1.10.Resources, Links, and the Teradata University Network Connection -- Resources and Links --
Contents note continued: Marketing Applications -- Security Applications -- Application Case 5.3 Mining for Lies -- Biomedical Applications -- Academic Applications -- Application Case 5.4 Text mining and Sentiment Analysis Help Improve Customer Service Performance -- 5.5.Text Mining Process -- Task 1: Establish the Corpus -- Task 2: Create the Term-Document Matrix -- Task 3: Extract the Knowledge -- Application Case 5.5 Research Literature Survey with Text Mining -- 5.6.Sentiment Analysis -- Application Case 5.6 Whirlpool Achieves Customer Loyalty and Product Success with Text Analytics -- Sentiment Analysis Applications -- Sentiment Analysis Process -- Methods for Polarity Identification -- Using a Lexicon -- Using a Collection of Training Documents -- Identifying Semantic Orientation of Sentences and Phrases -- Identifying Semantic Orientation of Document -- 5.7.Web Mining Overview -- Web Content and Web Structure Mining -- 5.8.Search Engines -- Anatomy of a Search Engine --
Contents note continued: Measuring the Social Media Impact -- Best Practices in Social Media Analytics -- Application Case 5.11 eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- 6.1.Opening Vignette: Big Data Meets Big Science at CERN -- 6.2.Definition of Big Data -- The Vs That Define Big Data -- Application Case 6.1 BigData Analytics Helps Luxottica Improvement its Marketing Effectiveness -- 6.3.Fundamentals of Big Data Analytics -- Business Problems Addressed by Big Data Analytics -- Application Case 6.2 Top 5 Investment Bank Achieves Single Source of the Truth -- 6.4.Big Data Technologies -- MapReduce -- Why Use MapReduce? -- Hadoop -- How Does Hadoop Work? -- Hadoop Technical Components -- Hadoop: The Pros and Cons -- NoSQL -- Application Case 6.3 eBay's Big Data Solution -- 6.5.Data Scientist -- Where Do Data Scientists Come From? --
Contents note continued: Present Information in Three Differenttevels -- Pick the Right Visual Construct Using Dashboard Design Principles -- Provide for Guided Analytics -- 3.7.Business Performance Management -- Closed-Loop BPM Cycle -- Application Case 3.7 IBM Cognos Express Helps Mace for Faster and Better Business Reporting -- 3.8.Performance Measurement -- Key Performance Indicator (KPI) -- Performance Measurement System -- 3.9.Balanced Scorecards -- The Four Perspectives -- The Meaning of Balance in BSC -- Dashboards Versus Scorecards -- 3.10.Six Sigma as a Performance Measurement System -- The DMAIC Performance Model -- Balanced Scorecard Versus Six Sigma -- Effective Performance Measurement -- Application Case 3.8's Customer Satisfaction Scorecard -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- 4.1.Opening Vignette: Cabela's Reels in More Customers with Advanced Analytics and Data Mining --
Contents note continued: Questions for Discussion -- Exercises -- End-of-Chapters Application Case -- References
Contents note continued: The Future of Data Warehousing -- 2.10.Resources, Links, and the Teradata University Network Connection -- Resources and Links -- Cases -- Vendors, Products, and Demos -- Periodicals -- Additional References -- The Teradata University Network (TUN) Connection -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- 3.1.Opening Vignette: Self-Service Reporting Environment Saves Millions for Corporate Customers -- 3.2.Business Reporting Definitions and Concepts -- What Is a Business Report? -- Application Case 3.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting -- Components of Business Reporting Systems -- Application Case 3.2 Flood of Paper Ends at FEMA -- 3.3.Data and Information Visualization -- Application Case 3.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing -- A Brief History of Data Visualization --
Contents note continued: Vendors, Products, and Demos -- Periodicals -- The Teradata University Network Connection -- The Book's Web Site -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- 2.1.Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse -- 2.2.Data Warehousing Definitions and Concepts -- What Is a Data Warehouse? -- A Historical Perspective to Data Warehousing -- Characteristics of Data Warehousing -- Data Marts -- Operational Data Stores -- Enterprise Data Warehouses (EDW) -- Application Case 2.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry -- Metadata -- 2.3.Data Warehousing Process Overview -- Application Case 2.2 Data Warehousing Helps MultiCare Save More Lives -- 2.4.Data Warehousing Architectures -- Alternative Data Warehousing Architectures -- Which Architecture Is the Best? --
Machine generated contents note: 1.1.Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely -- 1.2.Changing Business Environments and Computerized Decision Support -- The Business Pressures-Responses-Support Model -- 1.3.A Framework for Business Intelligence (BI) -- Definitions of BI -- A Brief History of BI -- The Architecture of BI -- The Origins and Drivers of BI -- Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics -- A Multimedia Exercise in Business Intelligence -- 1.4.Intelligence Creation, Use, and BI Governance -- A Cyclical Process of Intelligence Creation and Use -- Intelligence and Espionage -- 1.5.Transaction Processing Versus Analytic Processing -- 1.6.Successful BI Implementation -- The Typical BI User Community -- Appropriate Planning and Alignment with the Business Strategy -- Real-Time, On-Demand BI Is Attainable -- Developing or Acquiring BI Systems --
Summary This book is for courses on Business Intelligence or Decision Support Systems. It gives a managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, this text provides students with a solid foundation of Business Intelligence that is reinforced with hands-on practice
Notes Includes bibliographical references and index
Bibliography Includes bibliographical references and index
Subject Business intelligence.
Industrial management.
Author Delen, Dursun, author
Turban, Efraim, author
LC no. 2013030282
ISBN 0133051056