Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives.   They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.   Unlike most competitive guides, this text demonstrates the use of IBM's menu-based SPSS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.   A valuable resource for all beginning-to-intermediate-level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Table of Contents:
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