Business intelligence : a managerial perspective on analytics / / Ramesh Sharda [and six others]

• Autore: Sharda Ramesh
• Pubblicazione: Harlow, England : , : Pearson, , [2014]; ©2014
• Edizione: Third, Global edition.
• Descrizione fisica: 1 online resource (416 pages) : illustrations
• Disciplina: 658.472
• Soggetto topico: Business intelligence
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Contents -- Preface -- About the Authors -- Chapter 1 An Overview of Business -- 1.1 Opening Vignette: Magpie Sensing -- 1.2 Changing Business Environments and Computerized -- 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 -- 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 -- Real-Time, On-Demand BI Is Attainable -- Developing or Acquiring BI Systems -- 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 -- Application Case 1.3 Analysis at the Speed -- Prescriptive Analytics -- Application Case 1.4 Moneyball: Analytics in Sports -- Application Case 1.5 Analyzing Athletic Injuries -- Analytics Applied to Different Domains -- Application Case 1.6 Industrial and Commercial Bank of China -- 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 -- 1.9 Plan of the Book -- 1.10 Resources, Links, and the Teradata University -- Resources and Links -- 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 -- Chapter 2 Data Warehousing -- 2.1 Opening Vignette: Isle of Capri Casinos -- 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 -- Metadata -- 2.3 Data Warehousing Process Overview -- Application Case 2.2 Data Warehousing Helps -- 2.4 Data Warehousing Architectures -- Alternative Data Warehousing Architectures -- Which Architecture Is the Best? -- 2.5 Data Integration and the Extraction, Transformation -- 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 Development Approaches -- Application Case 2.5 Starwood Hotels & Resorts Manages -- 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 -- Massive Data Warehouses and Scalability -- 2.8 Real-Time Data Warehousing -- Application Case 2.7 Egg Plc Fries the Competition -- 2.9 Data Warehouse Administration, Security -- The Future of Data Warehousing -- 2.10 Resources, Links, and the Teradata University Network -- 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 -- Chapter 3 Business Reporting, Visual Analytics, and Business. ; 3.1 Opening Vignette: Self-Service Reporting -- 3.2 Business Reporting Definitions and Concepts -- What Is a Business Report? -- Application Case 3.1 Delta Lloyd Group Ensures Accuracy -- Components of Business Reporting Systems -- Application Case 3.2 Flood of Paper -- 3.3 Data and Information Visualization -- Application Case 3.3 Tableau Saves Blastrac -- A Brief History of Data Visualization -- Application Case 3.4 TIBCO Spotfire Provides -- 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 -- Application Case 3.6 Saudi Telecom Company Excels -- What to Look For in a Dashboard -- Best Practices in Dashboard Design -- Benchmark Key Performance Indicators -- Wrap the Dashboard Metrics with Contextual -- Validate the Dashboard Design by a Usability -- Prioritize and Rank Alerts/Exceptions Streamed -- Enrich Dashboard with Business-User Comments -- Present Information in Three Different Levels -- 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 -- 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 Expedia.com's Customer Satisfaction -- Key Terms -- Questions for Discussion. ; Exercises -- End-of-Chapter Application Case -- References -- Chapter 4 Data Mining -- 4.1 Opening Vignette: Cabela's Reels in More -- 4.2 Data Mining Concepts and Applications -- Definitions, Characteristics, and Benefits -- Application Case 4.1 Smarter Insurance: Infinity -- How Data Mining Works -- Application Case 4.2 Harnessing Analytics to Combat -- 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 -- 4.5 Data Mining Methods -- Classification -- Estimating the True Accuracy of Classification Models -- Application Case 4.5 2degrees Gets a 1275 Percent -- Cluster Analysis for Data Mining -- Association Rule Mining -- 4.6 Data Mining Software Tools -- Application Case 4.6 Data Mining Goes to Hollywood: -- 4.7 Data Mining Privacy Issues, Myths, and Blunders -- Data Mining and Privacy Issues -- Application Case 4.7 Predicting Customer Buying -- Data Mining Myths and Blunders -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- Chapter 5 Text and Web Analytics -- 5.1 Opening Vignette: Machine Versus Men -- 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 -- 5.4 Text Mining Applications -- Marketing Applications -- Security Applications -- Application Case 5.3 Mining for Lies -- Biomedical Applications -- Academic Applications -- Application Case 5.4 Text mining and Sentiment -- 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 -- 5.6 Sentiment Analysis -- Application Case 5.6 Whirlpool Achieves Customer -- Sentiment Analysis Applications -- Sentiment Analysis Process -- Methods for Polarity Identification -- Using a Lexicon -- Using a Collection of Training Documents -- Identifying Semantic Orientation of Sentences -- 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 -- 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 -- 5.9 Web Usage Mining (Web Analytics) -- Web Analytics Technologies -- Application Case 5.8 Allegro Boosts Online Click-Through -- 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 -- Connections -- Distributions -- Segmentation -- Social Media Analytics -- How Do People Use Social Media? -- Application Case 5.10 Measuring the Impact of Social -- Measuring the Social Media Impact -- Best Practices in Social Media Analytics -- Application Case 5.11 eHarmony Uses Social -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case -- References -- Chapter 6 Big Data and Analytics -- 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. ; 6.3 Fundamentals of Big Data Analytics.
• Sommario/riassunto: For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.
• Titolo autorizzato: Business intelligence
• ISBN: 1-292-00502-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910153069003321
• Lo trovi qui: Univ. Federico II