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Business Analytics Principles, Concepts, and Applications: What, Why, and How [NOOK Book]


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 ...

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

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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.

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Product Details

  • ISBN-13: 9780133552249
  • Publisher: Pearson Education
  • Publication date: 5/7/2014
  • Series: FT Press Analytics
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 368
  • Sales rank: 869,469
  • File size: 17 MB
  • Note: This product may take a few minutes to download.

Meet the Author

Marc J. Schniederjans is the C. Wheaton Battey Distinguished Professor of Business in the College of Business Administration at the University of Nebraska-Lincoln and has served on the faculty of three other universities. Professor Schniederjans is a Fellow of the Decision Sciences Institute (DSI) and in 2014–2015 will serve as DSI’s President. His prior experience includes owning and operating his own truck leasing business. He is currently a member of the Institute of Supply Management (ISM), the Production and Operations Management Society (POMS), and Decision Sciences Institute (DSI). Professor Schniederjans has taught extensively in operations management and management science. He has won numerous teaching awards and is an honorary member of the Golden Key honor society and the Alpha Kappa Psi business honor society. He has published more than one hundred journal articles and has authored or coauthored twenty books in the field of management. The title of his most recent book is Reinventing the Supply Chain Life Cycle, and his research has encompassed a wide range of operations management and decision science topics. He has also presented more than one hundred research papers at academic meetings. Professor Schniederjans is serving on five journal editorial review boards, including Computers & Operations Research, International Journal of Information & Decision Sciences, International Journal of Information Systems in the Service Sector, and Journal of Operations Management, Production, and Operations Management. He is also serving as an area editor for the journal Operations Management Research and as an associate editor for the International Journal of Strategic Decision Sciences and International Journal of the Society Systems Science and Management Review : An International Journal (Korea). Professor Schniederjans has served as a consultant and trainer to various business and government agencies.

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.

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Table of Contents

Preface     xvi
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

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

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

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


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