Applied Business Analytics

Dr. Nathaniel Lin is a recognized leader in marketing and business analytics across various industries worldwide. He has over 20 years of frontline experience applying actionable advanced analytics strategies to the world’s largest companies in technology, finance, automotive, telecommunications, retail, and across many other businesses, including IBM, Fidelity Investments, OgilvyOne, and Aspen Marketing Analytics.   Nathaniel is currently the Chief Customer Insights Officer of Attract China. He is leading the efforts to develop leading edge Big Data Analytics technology and knowledge assets to deliver unparalleled values to Chinese travelers and U.S. clients. Nathaniel is widely recognized as an expert, teacher, author, and hands-on leader and senior executive in the application of data and advanced analytics in a wide variety of businesses. He is also the Founder and President of Analytics Consult, LLC (www.analyticsconsult.com). He leverages his rich and unique expertise in business analytics to help companies optimize their customer, marketing, and sales strategies. Together with his team, Nathaniel serves as a trusted strategic advisor to senior management teams. He is frequently invited as the keynote speaker in analytics events and advised over 150 CEOs in the U.S. and aboard on analytics and Big Data issues. He was invited by WWW2010 as one of the four expert panelists (together with the heads of Google Analytics, eBay Analytics, and Web Analytics Association) on the Future of Predictive Analytics.   As a recognized analytics expert, Nathaniel has partnered with Professor Tom Davenport to benchmark analytics competencies of major corporations across different industries. He also demonstrates his passion in cultivating future analytics leaders by teaching Strategic CRM and Advanced Business Analytics for MBA students at the Georgia Tech College of Management, Boston College Carroll School of Management, and Quant III Advanced Business Analytics at Bentley University.   Nathaniel holds a PhD in Engineering from Birmingham University (UK) and an MBA from MIT Sloan School of Management.  

Foreword xv

Acknowledgments xviii

About the Author xix

Preface xxi

Why Another Book on Analytics? xxi

How This Book Is Organized xxii

After Reading and Working Through This Book xxvi

Chapter 1: Introduction 1

Raw Data, the New Oil 1

Data Big and Small Is Not New 2

Definition of Analytics 3

Top 10 Business Questions for Analytics 5

Financial Management 6

Customer Management 8

HR Management 11

Internal Operations 11

Vital Lessons Learned 12

Use Analytics 13

Reasons Why Analytics Are Not Used 13

Linking Analytics to Business 14

Business Analytics Value Chain 14

Integrated Approach 17

Hands-on Exercises 17

Reasons for Using KNIME Workflows 17

Conclusion 18

Chapter 2: Know Your Ingredients—Data Big and Small 21

Garbage in, Garbage out 21

Data or Big Data 22

Definition of Big Data 22

Data Types 23

Company Data 24

Individual Customer Data 31

Sensor Data 34

Syndicated Data 35

Data Formats 38

Structured, Poorly Structured, and Unstructured Data 39

Conclusion 42

Chapter 3: Data Management—Integration, Data Quality, and Governance 43

Data Integration 44

Data Quality 45

Data Security and Data Privacy 46

Data Security 47

Data Privacy 48

Data Governance 53

Data Preparation 56

Data Manipulation 58

Types of Data 58

Categorize Numerical Variables 59

Dummy Variables 60

Missing Values 60

Data Normalization 61

Data Partitions 62

Exploratory Data Analysis 64

Multidimensional Cube 65

Slicing 65

Dicing 65

Drilling Down/Up 66

Pivoting 66

Visualization of Data Patterns and Trends 66

Popularity of BI Visualization 66

Selecting a BI Visualization Tool    67

Beyond BI Visualizations 70

Conclusion 70

Chapter 4: Handle the Tools: Analytics Methodology and Tools 73

Getting Familiar with the Tools 73

Master Chefs Who Can’t Cook 74

Types of Analytics 75

Descriptive and Diagnostic Tools: BI Visualization and Reporting 75

Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery 77

A Unified View of BI Analysis, Advanced Analytics, and Visualization 77

Two Ways of Knowledge Discovery 79

Types of Advanced Analytics and Applications 81

Analytics Modeling Tools by Functions 81

Modeling Likelihood 82

Modeling Groupings 86

Supervised Learning 87

Value Prediction 97

Other Models 102

Conclusion 111

Chapter 5: Analytics Decision-Making Process and the Analytics Deciders 115

Time to Take Off the Mittens 115

Overview of the Business Analytics Process (BAP) 116

Analytics Rapid Prototyping 120

Analytics Sandbox for Instant Business Insights 122

Analytics IT Sandbox Database 125

People and the Decision Blinders 125

Risks of Crossing the Chasms 126

The Medici Effect 127

Analytics Deciders 129

How to Find Analytics Deciders 130

Becoming an Analytics Decider 132

Conclusion 139

Chapter 6: Business Processes and Analytics (by Alejandro Simkievich) 141

Overview of Process Families 142

Enterprise Resource Planning 143

Customer Relationship Management 145

Product Lifecycle Management 147

Shortcomings of Operational Systems 147

Embedding Advanced Analytics into Operational Systems 150

Example 1: Forecast 152

Example 2: Improving Salesforce Decisions 154

Example 3: Engineers Get Instant Feedback on Their Design Choices 155

Conclusion 155

Chapter 7: Identifying Business Opportunities by Recognizing Patterns 157

Patterns of Group Behavior 157

Importance of Pattern Recognition in Business 158

Group Patterns by Clustering and Decision Trees 161

Three Ways of Grouping 162

Recognize Purchase Patterns: Association Analysis 167

Association Rules 167

Business Case 169

Patterns over Time: Time Series Predictions 173

Time Series Models 174

Conclusion 179

Chapter 8: Knowing the Unknowable 181

Unknowable Events 181

Unknowable in Business 182

Poor or Inadequate Data 185

Data with Limited Views 185

Business Case 186

Predicting Individual Customer Behaviors in Real-Time 192

Lever Settings and Causality in Business 197

Start with a High Baseline 199

Causality with Control Groups 199

Conclusion 201

Chapter 9: Demonstration of Business Analytics Workflows: Analytics Enterprise 203

A Case for Illustration 204

Top Questions for Analytics Applications 209

Financial Management 210

Human Resources 212

Internal Operations 213

Conclusion 218

Chapter 10: Demonstration of Business Analytics Workflows—Analytics CRM 219

Questions About Customers 220

Know the Customers 220

Actionable Customer Insights 222

Social and Mobile CRM Issues 226

CRM Knowledge Management 227

Conclusion 228

Chapter 11: Analytics Competencies and Ecosystem 231

Analytics Maturity Levels 233

Analytics Organizational Structure 234

The Centralized Model 236

The Consulting Model 237

The Decentralized Model 238

The Center of Excellence Model 239

Reporting Structures 241

Roles and Responsibilities 242

Analytics Roles 242

Business Strategy and Leadership Roles 243

Data and Quantitative Roles 247

Analytics Ecosystem 250

The In-House IT Function 250

External Analytics Advisory and Consulting

Resources 251

Analytics Talent Management 256

Conclusion 260

Chapter 12: Conclusions and Now What? 263

Analytics Is Not a Fad 263

Acquire Rich and Effective Data 264

Start with EDA and BI Analysis 265

Gain Firsthand Analytics Experience 265

Become an Analytics Decider and Recruit Others 266

Empower Enterprise Business Processes with Analytics 266

Recognize Patterns with Analytics 267

Know the Unknowable 268

Imbue Business Processes with Analytics 269

Acquire Competencies and Establish Ecosystem 270

Epilogue 271

Appendix A: KNIME Basics 273

Data Preparation 274

Types of Variable Values 274

Dummy Variables 275

Missing Values 275

Data Partitions 277

Exploratory Data Analysis (EDA) 279

Multi-Dimensional Cube 279

Slicing 281

Dicing 281

Drilling Down or Up 281

Pivoting 281

Index 285