Description
Book SynopsisTurn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data.
Table of ContentsForeword xiii
Preface xv
Acknowledgments xix
Chapter 1 The Age of Advanced Business Analytics 1
Introduction 1
Why the Analytics Hype Today? 5
A Short History of Data Analytics 15
What Is the Analytics Age? 22
Interview with Wayne Thompson, Chief Data Scientist at
SAS Institute 23
Key Takeaways 28
Notes 29
Further Reading 30
Chapter 2 Unstructured Data Analytics: The Next Frontier of Analytics Innovation 33
Introduction 33
What Is UDA? 35
Why UDA Today? 39
The UDA Industry 48
Uses of UDA 51
How UDA Works 52
Why UDA Is the Next Analytical Frontier? 54
Interview with Seth Grimes on Analytics as the Next
Business Frontier 58
UDA Success Stories 60
The Golden Age of UDA 64
Key Takeaways 65
Notes 66
Further Reading 67
Chapter 3 The Framework to Put UDA to Work 69
Introduction 69
Why Have a Framework to Analyze Unstructured Data? 70
The IMPACT Cycle Applied to Unstructured Data 72
Text Parsing Example 81
Interview with Cindy Forbes, Chief Analytics Officer and Executive Vice President at Manulife Financial 84
Case Study 90
Key Takeaways 106
Notes 107
Further Reading 108
Chapter 4 How to Increase Customer Acquisition and Retention with UDA 109
The Voice of the Customer: A Goldmine for
Understanding Customers 109
Why Should You Care about UDA for Customer
Acquisition and Retention? 111
Predictive Models and Online Marketing 117
How Does UDA Applied to Customer Acquisition Work? 118
The Power of UDA for E-mail Response and Ad Optimization 124
How to Drive More Conversion and Engagement with UDA Applied to Content 124
How UDA Applied to Customer Retention (Churn) Works 125
What Is UDA Applied to Customer Acquisition? 129
What Is UDA Applied to Customer Retention (Churn)? 135
The Power of UDA Powered by Virtual Agent 136
Benefits of a Virtual Agent or AI Assistant for Customer Experience 138
Benefits and Case Studies 139
Applying UDA to Your Social Media Presence and Native Ads to Increase Acquisitions 151
Key Takeaways 153
Notes 154
Chapter 5 The Power of UDA to Improve Fraud Detection and Prevention 157
Introduction 157
Why Should You Care about UDA for Fraud Detection and Prevention? 159
Benefits of UDA 163
What Is UDA for Fraud? 168
How UDA Works in Fraud Detection and Prevention 170
UDA Framework for Fraud Detection and Prevention:
Insurance 173
Major Fraud Detection and Prevention Techniques 176
Best Practices Using UDA for Fraud Detection and Prevention 179
Interview with Vishwa Kolla, Assistant Vice President Advanced Analytics at John Hancock Financial Services 182
Interview with Diane Deperrois, General Manager South-East and Overseas Region, AXA 184
Key Takeaways 187
Notes 189
Further Reading 189
Chapter 6 The Power of UDA in Human Capital Management 191
Why Should You Care about UDA in Human Resources? 191
What Is UDA in HR? 193
What Is UDA in HR Really About? 195
The Power of UDA in Online Recruitment: Supply and Demand Equation 196
The Power of UDA in Talent Sourcing Analytics 197
The Power of UDA in Talent Acquisition Analytics 205
Artificial Intelligence as a Hiring Assistant 206
The Power of UDA in Talent Retention 207
Interview with Arun Chidambaram, Director of Global workforce intelligence, Pfizer 208
Employee Performance Appraisal Data Review Feedback 210
How UDA Works 211
Benefits of UDA in HR 212
Case Studies 213
Interview with Stephani Kingsmill, Executive Vice President and Chief Human Resource Officer, Manulife 213
Key Takeaways 216
Further Reading 217
Chapter 7 The Power of UDA in the Legal Industry 219
Why Should You Care about UDA in Legal Services? 219
What Is UDA Applied to Legal Services? 224
How Does It Work? 224
Benefits and Challenges 231
Key Takeaways 234
Notes 235
Further Reading 235
Chapter 8 The Power of UDA in Healthcare and Medical Research 237
Why Should You Care about UDA in Healthcare? 237
What’s UDA in Healthcare? 245
How UDA Works 250
Benefits 255
Interview with Mr. François Laviolette, Professor of Computer Science/Director of Big Data Research Centre at Laval University (QC) Canada 257
Interview with Paul Zikopolous, Vice President Big Data Cognitive System at IBM 258
Case Study 262
Key Takeaways 263
Notes 264
Further Reading 265
Chapter 9 The Power of UDA in Product and Service Development 267
Why Should You Care about UDA for Product and Service Development? 267
UDA and Big Data Analytics 268
Interview with Fiona McNeill, Global Product Marketing Manager at SAS Institute 283
What Is UDA Applied to Product Development? 297
How Is UDA Applied to Product Development? 300
How UDA Applied to Product Development Works 301
Key Takeaways 303
Notes 304
Chapter 10 The Power of UDA in National Security 307
National Security: Playground for UDA or Civil Liberty Threat? 307
What Is UDA for National Security? 310
Data Sources of the NSA 310
Why UDA for National Security? 314
Case Studies 320
How UDA Works 322
Key Takeaways 323
Notes 324
Further Reading 325
Chapter 11 The Power of UDA in Sports 327
The Short History of Sports Analytics: Moneyball 328
Why Should You Care about UDA in Sports? 333
What Is UDA in Sports? 338
How It Works 342
Interview with Winston Lin, Director of Strategy and Analytics for the Houston Rockets 343
Key Takeaways 347
Notes 347
Further Reading 348
Chapter 12 The Future of Analytics 349
Harnessing These Evolving Technologies Will Generate Benefits 350
Data Becomes Less Valuable and Analytics Becomes Mainstream 353
Predictive Analytics, AI, Machine Learning, and Deep Learning Become the New Standard 355
People Analytics Becomes a Standard Department in Businesses 358
UDA Becomes More Prevalent in Corporations and Businesses 359
Cognitive Analytics Expansion 359
The Internet of Things Evolves to the Analytics of Things 360
MOOCs and Open Source Software and Applications Will Continue to Explode 361
Blockchain and Analytics Will Solve Social Problems 362
Human-Centered Computing Will Be Normalized 364
Data Governance and Data Security Will Remain the Number-One Risk and Threat 365
Key Takeaways 366
Notes 367
Further Reading 367
Appendix A Tech Corner Details 369
Singular Value Decomposition (SVD) Algorithm and Applications 370
Principal Component Analysis (PCA) and Applications 382
PCA Application to Facial Recognition: EigenFaces 392
QR Factorization Algorithm and Applications 394
Note 399
Further Reading 399
About The Author 401
Index 403
