Unstructured Data Analytics

JEAN PAUL ISSON is a recognized worldwide expert and evangelist in Big Data Analytics and Advanced Business Analytics, with over 22 years of experience. He is the author of Win with Advanced Business Analytics and People Analytics in the Era of Big Data. As Global Vice President of Predictive Analytics & BI at Monster Worldwide Inc. he has built his team from the ground up and successfully conceived and implemented global customer scoring, predictive models and segmentation, machine learning and deep learning solutions, web mining applications, and people analytics solutions for Monster across North America, Europe, and Asia/Pacific. He is also the Founder of the People Analytics Institute.

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

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