Business Analytics Principles, Concepts, and Applications: What, Why, and How

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.

Business analytics can help you sort through massive amounts of data, identify meaningful patterns, transform patterns into insights, and use those insights to make more profitable decisions. This up-to-date primer explains what business analytics is, why it is so important, and how to use it.

The authors start with a complete conceptual introduction, explaining how business analytics can drive competitive advantage, and how to align your organization to make the most of it. You’ll find realistic coverage of key managerial and technical issues, from organizational structure to data quality.

Next, you’ll master practical methodologies, strategies, and best practices for performing each leading form of analytics: descriptive, predictive, and prescriptive. Throughout, you’ll learn from realistic case studies and solved problems, and gain familiarity with a wide variety of statistical and quantitative techniques, and software tools. A capstone project integrates key processes and techniques, preparing you to succeed on your own.

• What is business analytics?

• What questions can it answer?

• How can it drive competitive advantage?

• What resources and skills do you need?

• How do you prepare the organization for it?

• What obstacles must you overcome?

• What are the key statistical techniques?

• How do they work?

• How does it all fit together?

For all beginning-to-intermediate-level analysts and analytics managers, and for MBA/Masters or advanced undergraduate students.

Dara G. Schniederjans is an assistant professor of Supply Chain Management at the University of Rhode Island, College of Business Administration. She has published articles in journals such as Decision Support Systems, Journal of the Operational Research Society, and Business Process Management Journal. She has also co-authored two text books and co-edited a readings book. She has contributed chapters to readings utilizing quantitative and statistical methods. Dara has served as a guest co-editor for a special issue on Business Ethics in Social Sciences in the International Journal of Society Systems Science. She has also served as a website coordinator for Decisions Sciences Institute. She currently teaches courses in Supplier Relationship Management and Operations Management.

Christopher M. Starkey is an Economics student at the University of Connecticut-Storrs. He has presented papers at the Academy of Management and Production and Operations Management Society meetings. He currently teaches courses in Principles of Microeconomics and has taught Principles of Macroeconomics. His current research interests include macroeconomic and monetary policy, as well as other decision-making methodologies.

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