Includes bibliographical references and index.

Preface xvi PART I: WHAT ARE BUSINESS ANALYTICS 1 Chapter 1: What Are Business Analytics? 3 1.1 Terminology 3 1.2 Business Analytics Process 7 1.3 Relationship of BA Process and Organization Decision-Making 10 1.4 Organization of This Book 12 Summary 13 Discussion Questions 13 References 14 PART II: WHY ARE BUSINESS ANALYTICS IMPORTANT 15 Chapter 2: Why Are Business Analytics Important? 17 2.1 Introduction 17 2.2 Why BA Is Important: Providing Answers to Questions 18 2.3 Why BA Is Important: Strategy for Competitive Advantage 20 2.4 Other Reasons Why BA Is Important 23 2.4.1 Applied Reasons Why BA Is Important 23 2.4.2 The Importance of BA with New Sources of Data 24 Summary 26 Discussion Questions 26 References 26 Chapter 3: What Resource Considerations Are Important to Support Business Analytics? 29 3.1 Introduction 29 3.2 Business Analytics Personnel 30 3.3 Business Analytics Data 33 3.3.1 Categorizing Data 33 3.3.2 Data Issues 35 3.4 Business Analytics Technology 36 Summary 41 Discussion Questions 41 References 42 PART III: HOW CAN BUSINESS ANALYTICS BE APPLIED 43 Chapter 4: How Do We Align Resources to Support Business Analytics within an Organization? 45 4.1 Organization Structures Aligning Business Analytics 45 4.1.1 Organization Structures 46 4.1.2 Teams 51 4.2 Management Issues 54 4.2.1 Establishing an Information Policy 54 4.2.2 Outsourcing Business Analytics 55 4.2.3 Ensuring Data Quality 56 4.2.4 Measuring Business Analytics Contribution 58 4.2.5 Managing Change 58 Summary 60 Discussion Questions 61 References . 61 Chapter 5: What Are Descriptive Analytics? 63 5.1 Introduction 63 5.2 Visualizing and Exploring Data 64 5.3 Descriptive Statistics 67 5.4 Sampling and Estimation 72 5.4.1 Sampling Methods 73 5.4.2 Sampling Estimation 76 5.5 Introduction to Probability Distributions 78 5.6 Marketing/Planning Case Study Example: Descriptive Analytics Step in the BA Process 80 5.6.1 Case Study Background 81 5.6.2 Descriptive Analytics Analysis 82 Summary 91 Discussion Questions 91 Problems 92 Chapter 6: What Are Predictive Analytics 93 6.1 Introduction 93 6.2 Predictive Modeling 94 6.2.1 Logic-Driven Models 94 6.2.2 Data-Driven Models 96 6.3 Data Mining 97 6.3.1 A Simple Illustration of Data Mining 98 6.3.2 Data Mining Methodologies 99 6.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Analytics Step in the BA Process 102 6.4.1 Case Study Background Review 103 6.4.2 Predictive Analytics Analysis 104 Summary 114 Discussion Questions 115 Problems 115 References 117 Chapter 7: What Are Prescriptive Analytics? 119 7.1 Introduction 119 7.2 Prescriptive Modeling 120 7.3 Nonlinear Optimization 122 7.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Step in the BA Analysis 129 7.4.1 Case Background Review 129 7.4.2 Prescriptive Analysis 129 Summary 134 Addendum 134 Discussion Questions 135 Problems 135 References .137 Chapter 8: A Final Business Analytics Case Problem 139 8.1 Introduction 139 8.2 Case Study: Problem Background and Data 140 8.3 Descriptive Analytics Analysis 141 8.4 Predictive Analytics Analysis 147 8.4.1 Developing the Forecasting Models 147 8.4.2 Validating the Forecasting Models 155 8.4.3 Resulting Warehouse Customer Demand Forecasts 157 8.5 Prescriptive Analytics Analysis 158 8.5.1 Selecting and Developing an Optimization Shipping Model 158 8.5.2 Determining the Optimal Shipping Schedule 159 8.5.3 Summary of BA Procedure for the Manufacturer 161 8.5.4 Demonstrating Business Performance Improvement 162 Summary 163 Discussion Questions 164 Problems 164 PART IV: APPENDIXES 165 Appendix A: Statistical Tools 167 A.1 Introduction 167 A.2 Counting 167 A.3 Probability Concepts 171 A.4 Probability Distributions 177 A.5 Statistical Testing 193 Appendix B: Linear Programming 201 B.1 Introduction 201 B.2 Types of Linear Programming Problems/Models 201 B.3 Linear Programming Problem/Model Elements 202 B.4 Linear Programming Problem/Model Formulation Procedure 207 B.5 Computer-Based Solutions for Linear Programming Using the Simplex Method 217 B.6 Linear Programming Complications 227 B.7 Necessary Assumptions for Linear Programming Models 232 B.8 Linear Programming Practice Problems 233 Appendix C: Duality and Sensitivity Analysis in Linear Programming 241 C.1 Introduction 241 C.2 What Is Duality? 241 C.3 Duality and Sensitivity Analysis Problems 243 C.4 Determining the Economic Value of a Resource with Duality 258 C.5 Duality Practice Problems 259 Appendix D: Integer Programming 263 D.1 Introduction 263 D.2 Solving IP Problems/Models 264 D.3 Solving Zero-One Programming Problems/Models 268 D.4 Integer Programming Practice Problems 270 Appendix E: Forecasting 271 E.1 Introduction 271 E.2 Types of Variation in Time Series Data 272 E.3 Simple Regression Model 276 E.4 Multiple Regression Models 281 E.5 Simple Exponential Smoothing 284 E.6 Smoothing Averages 286 E.7 Fitting Models to Data 288 E.8 How to Select Models and Parameters for Models 291 E.9 Forecasting Practice Problems 292 Appendix F: Simulation 295 F.1 Introduction 295 F.2 Types of Simulation 295 F.3 Simulation Practice Problems 302 Appendix G: Decision Theory 303 G.1 Introduction 303 G.2 Decision Theory Model Elements 304 G.3 Types of Decision Environments 304 G.4 Decision Theory Formulation 305 G.5 Decision-Making Under Certainty 306 G.6 Decision-Making Under Risk 307 G.7 Decision-Making under Uncertainty 311 G.8 Expected Value of Perfect Information 315 G.9 Sequential Decisions and Decision Trees 317 G.10 The Value of Imperfect Information: Bayes' Theorem 321 G.11 Decision Theory Practice Problems 328 Index 335

College of Business and Accountancy