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Business intelligence : a managerial perspective on analytics / / Ramesh Sharda [and six others]
| Business intelligence : a managerial perspective on analytics / / Ramesh Sharda [and six others] |
| Autore | Sharda Ramesh |
| Edizione | [Third, Global edition.] |
| Pubbl/distr/stampa | Harlow, England : , : Pearson, , [2014] |
| Descrizione fisica | 1 online resource (416 pages) : illustrations |
| Disciplina | 658.472 |
| Soggetto topico | Business intelligence |
| ISBN | 1-292-00502-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910153069003321 |
Sharda Ramesh
|
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| Harlow, England : , : Pearson, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Business intelligence and analytics : systems for decision support / / Ramesh Sharda, Dursun Delen, Efraim Turban ; with contributions by J. E. Aronson, Ting-Peng Liang, David King
| Business intelligence and analytics : systems for decision support / / Ramesh Sharda, Dursun Delen, Efraim Turban ; with contributions by J. E. Aronson, Ting-Peng Liang, David King |
| Autore | Sharda Ramesh |
| Edizione | [Tenth edition.] |
| Pubbl/distr/stampa | Boston, [Massachusetts] : , : Pearson, , 2014 |
| Descrizione fisica | 1 online resource (689 pages) : illustrations, tables |
| Disciplina | 658.403 |
| Collana | Always Learning |
| Soggetto topico |
Decision support systems
Expert systems (Computer science) Business intelligence |
| ISBN |
1-292-00926-8
9781292009209 9781292009261 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Contents -- Preface -- About the Authors -- Part I Decision Making and Analytics: An Overview -- Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support -- 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 Managerial Decision Making -- The Nature of Managers' Work -- The Decision-Making Process -- 1.4 Information Systems Support for Decision Making -- 1.5 An Early Framework for Computerized Decision Support -- The Gorry and Scott-Morton Classical Framework -- Computer Support for Structured Decisions -- Computer Support for Unstructured Decisions -- Computer Support for Semistructured Problems -- 1.6 The Concept of Decision Support Systems (DSS) -- DSS as an Umbrella Term -- Evolution of DSS into Business Intelligence -- 1.7 A Framework for Business Intelligence (BI) -- Definitions of BI -- A Brief History of BI -- The Architecture of BI -- Styles of BI -- The Origins and Drivers of BI -- A Multimedia Exercise in Business Intelligence -- Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics -- The DSS-BI Connection -- 1.8 Business Analytics Overview -- Descriptive Analytics -- Application Case 1.2 Eliminating Inefficiencies at Seattle Children's Hospital -- Application Case 1.3 Analysis at the Speed of Thought -- Predictive Analytics -- Application Case 1.4 Moneyball: Analytics in Sports and Movies -- Application Case 1.5 Analyzing Athletic Injuries -- Prescriptive Analytics -- Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network -- Analytics Applied to Different Domains -- Analytics or Data Science?.
1.9 Brief Introduction to Big Data Analytics -- What Is Big Data? -- Application Case 1.7 Gilt Groupe's Flash Sales Streamlined by Big Data Analytics -- 1.10 Plan of the Book -- Part I: Business Analytics: An Overview -- Part II: Descriptive Analytics -- Part III: Predictive Analytics -- Part IV: Prescriptive Analytics -- Part V: Big Data and Future Directions for Business Analytics -- 1.11 Resources, Links, and the Teradata University Network Connection -- Resources and Links -- Vendors, Products, and Demos -- Periodicals -- The Teradata University Network Connection -- The Book's Web Site -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Nationwide Insurance Used BI to Enhance Customer Service -- References -- Chapter 2 Foundations and Technologies for Decision Making -- 2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets -- 2.2 Decision Making: Introduction and Definitions -- Characteristics of Decision Making -- A Working Definition of Decision Making -- Decision-Making Disciplines -- Decision Style and Decision Makers -- 2.3 Phases of the Decision-Making Process -- 2.4 Decision Making: The Intelligence Phase -- Problem (or Opportunity) Identification -- Application Case 2.1 Making Elevators Go Faster! -- Problem Classification -- Problem Decomposition -- Problem Ownership -- 2.5 Decision Making: The Design Phase -- Models -- Mathematical (Quantitative) Models -- The Benefits of Models -- Selection of a Principle of Choice -- Normative Models -- Suboptimization -- Descriptive Models -- Good Enough, or Satisficing -- Developing (Generating) Alternatives -- Measuring Outcomes -- Risk -- Scenarios -- Possible Scenarios -- Errors in Decision Making -- 2.6 Decision Making: The Choice Phase -- 2.7 Decision Making: The Implementation Phase -- 2.8 How Decisions Are Supported. Support for the Intelligence Phase -- Support for the Design Phase -- Support for the Choice Phase -- Support for the Implementation Phase -- 2.9 Decision Support Systems: Capabilities -- A DSS Application -- 2.10 DSS Classifications -- The AIS SIGDSS Classification for DSS -- Other DSS Categories -- Custom-Made Systems Versus Ready-Made Systems -- 2.11 Components of Decision Support Systems -- The Data Management Subsystem -- The Model Management Subsystem -- Application Case 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data -- Application Case 2.3 SNAP DSS Helps OneNet MakeTelecommunications Rate Decisions -- The User Interface Subsystem -- The Knowledge-Based Management Subsystem -- Application Case 2.4 From a Game Winner to a Doctor! -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Logistics Optimization in a Major Shipping Company (CSAV) -- References -- Part II Descriptive Analytics -- Chapter 3 Data Warehousing -- 3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse -- 3.2 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) -- Metadata -- Application Case 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry -- 3.3 Data Warehousing Process Overview -- Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives -- 3.4 Data Warehousing Architectures -- Alternative Data Warehousing Architectures -- Which Architecture Is the Best? -- 3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes -- Data Integration. Application Case 3.3 BP Lubricants Achieves BIGS Success -- Extraction, Transformation, and Load -- 3.6 Data Warehouse Development -- Application Case 3.4 Things Go Better with Coke's Data Warehouse -- Data Warehouse Development Approaches -- Application Case 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing -- Additional Data Warehouse Development Considerations -- Representation of Data in Data Warehouse -- Analysis of Data in the Data Warehouse -- OLAP Versus OLTP -- OLAP Operations -- 3.7 Data Warehousing Implementation Issues -- Application Case 3.6 EDW Helps Connect State Agencies in Michigan -- Massive Data Warehouses and Scalability -- 3.8 Real-Time Data Warehousing -- Application Case 3.7 Egg Plc Fries the Competition in Near Real Time -- 3.9 Data Warehouse Administration, Security Issues, and Future Trends -- The Future of Data Warehousing -- 3.10 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 -- Chapter Highlights -- Key Terms -- Questions for Discussion -- Exercises -- End-of-Chapter Application Case Continental Airlines Flies High with Its Real-Time Data Warehouse -- References -- Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management -- 4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers -- 4.2 Business Reporting Definitions and Concepts -- What Is a Business Report? -- Application Case 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting -- Components of the Business Reporting System -- Application Case 4.2 Flood of Paper Ends at FEMA -- 4.3 Data and Information Visualization. Application Case 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing -- A Brief History of Data Visualization -- Application Case 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials -- 4.4 Different Types of Charts and Graphs -- Basic Charts and Graphs -- Specialized Charts and Graphs -- 4.5 The Emergence of Data Visualization and Visual Analytics -- Visual Analytics -- High-Powered Visual Analytics Environments -- 4.6 Performance Dashboards -- Application Case 4.5 Dallas Cowboys Score Big with Tableau and Teknion -- Dashboard Design -- Application Case 4.6 Saudi Telecom Company Excels with Information Visualization -- 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 Users' Comments -- Present Information in Three Different Levels -- Pick the Right Visual Construct Using Dashboard Design Principles -- Provide for Guided Analytics -- 4.7 Business Performance Management -- Closed-Loop BPM Cycle -- Application Case 4.7 IBM Cognos Express Helps Mace for Faster -- 4.8 Performance Measurement -- Key Performance Indicator (KPI) -- Performance Measurement System -- 4.9 Balanced Scorecards -- The Four Perspectives -- The Meaning of Balance in BSC -- Dashboards Versus Scorecards -- 4.10 Six Sigma as a Performance Measurement System -- The DMAIC Performance Model -- Balanced Scorecard Versus Six Sigma -- Effective Performance Measurement -- Application Case 4.8 Expedia.com's Customer Satisfaction Scorecard -- Chapter Highlights -- Key Terms -- Questions for Discussion. |
| Record Nr. | UNINA-9910151592903321 |
|
Sharda Ramesh
|
||
| Boston, [Massachusetts] : , : Pearson, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||