Nauman Sheikh is a veteran IT professional of 18 years with specialization and focus on data and analytics. His expertise range from data integration and data modeling in operational systems, to multiterabyte data warehousing systems, to analytics driven automated decisioning systems. He has worked in three continents solving data-centric problems in credit, risk, fraud, and customer analytics areas dealing with cultural, technological, and legal chal- lenges surrounding automated decisioning systems. Throughout his career, he has been a firm believer in innovation through simplification to encour- age better coordination between technical and business personnel,leading to innovative answers to pressing challenges.
He firmly believes in democratization of analytics and has been working diligently the last few years in building analytics systems using well-known and widely available components. He holds a bachelor's degree in computer science from F.A.S.T Institute of Computer Science, Pakistan and lives in Maryland with his wife and three lovely children.

ACKNOWLEDGMENTS xi

AUTHOR BIOGRAPHY xii

INTRODUCTION xv

Part 1 Concept

CHAPTER 1 Defining Analytics 3

The Hype 3

The Challenge of Definition 4

Definition 1: Business Value Perspective 5

Definition 2:Technical Implementation Perspective 6

Analytics Techniques 7

Algorithm versus Analytics Model 8

Forecasting 9

Descriptive Analytics 11

Predictive Analytics 13

Decision Optimization 18

Conclusion of Definition 20

CHAPTER2 Information Continuum 21

Building Blocks of the Information Continuum 22

Theoretical Foundation in Data Sciences 23

Tools,Techniques,and Technology 24

Skilled Human Resources 24

Innovation and Need 25

Information Continuum Levels25

Search and Lookup 26

Counts and Lists 27

Operational Reporting 28

Summary Reporting 29

Historical (Snapshot) Reporting 30

Metrics,KPIs,and Thresholds 31

Analytical Applications 33

Analytics Models 36

Decision Strategies 36

Monitoring and Tuning—Governance 38

Summary 40

CHAPTER 3 Using Analytics 41

Healthcare 42

Emergency Room Visit 42

Patients with the Same Disease 43

Customer Relationship Management 44

Customer Segmentation 44

Propensity to Buy 45

Human Resource 46

Employee Attrition 46

Resumé Matching 47

Consumer Risk 48

Borrower Default 49

Insurance 49

Probability of a Claim.50

Telecommunication 51

Call Usage Patterns 51

Higher Education 51

Admission and Acceptance 52

Manufacturing 52

Predicting Warranty Claims 53

Analyzing Warranty Claims 54

Energy and Utilities 54

The New Power Management Challenge 55

Fraud Detection 57

Benefits Fraud 57

Credit Card Fraud 57

Patterns of Problems 58

How Much Data 59

Performance or Derived Variables 59

Part 2 Design

CHAPTER4 Performance Variables and Model Development 63

Performance Variables 63

What are Performance Variables? 64

Designing Performance Variables 70

Working Example 73

Model Development 75

What is a Model? 75

Model and Characteristics in Predictive Modeling 75

Model and Characteristics in Descriptive Modeling 78

Model Validation and Tuning 79

Champion-Challenger: A Culture of Constant Innovation 82

CHAPTER 5 Automated Decisions and Business Innovation 85

Automated Decisions 85

Decision Strategy 85

Business Rules in Business Operations 87

Decision Automation and Business Rules 88

Joint Business and Analytics Sessions for Decision Strategies 89

Examples of Decision Strategy 89

Decision Automation and Intelligent Systems 94

Learning versus Applying 94

Strategy Integration Methods 96

Strategy Evaluation 97

Retrospective Processing 97

Reprocessing 97

Champion-Challenger Strategies 98

Business Process Innovation 98

CHAPTER 6 Governance: Monitoring and Tuning of Analytics Solutions 101

Analytics and Automated Decisions 101

The Risk of Automated Decisions 102

Monitoring Layer 102

Audit and Control Framework 103

Organization and Process 103

Audit Datamart 104

Control Definition 106

Reporting and Action 108

Part 3 Implementation

CHAPTER 7 Analytics Adoption Roadmap 113

Learning from Success of Data Warehousing 113

      Lesson 1: Simplification 113

      Lesson 2: Quick Results 114

      Lesson 3: Evangelize 114

      Lesson 4: Eficient Data Acquisition 115

      Lesson 5:Holistic View 115

      Lesson 6: Data Management 115

The Pilot 117

Business Problem 117

Management Attention and Champion 118

The Project 119

Results,Roadshow,and Case for Wider Adoption 125

CHAPTER 8 Requirements Gathering for Analytics Projects 129

Purpose of Requirements 129

equirements: Historical Perspective 129

Calculations 130

Process Automation 132

Analytical and Reporting Systems 132

Analytics and Decision Strategy 133

Requirements Extraction 134

Problem Statement and Goal 135

Data Requirements 139

Model and Decision Strategy Requirements 142

Business Process Integration Requirements 144

CHAPTER 9 Analytics Implementation Methodology 147

Centralized versus Decentralized 148

Centralized Approach 148

Decentralized Approach 149

A Hybrid Approach 149

Building on the Data Warehouse 149

Methodology 151

Requirements 152

Analysis153

Design 158

Implementation 164

Deployment 165

Execution and Monitoring 165

CHAPTER 10 Analytics Organization and Architecture 167

Organizational Structure 167

BICC Organization Chart 168

Roles and Responsibilities 170

Skills Summary 175

Technical Components in Analytics Solutions 176

Analytics Datamart 176

CHAPTER 11 Big Data,Hadoop,and Cloud Computing 185

Big Data 185

Velocity 186

Variety 187

Volume 187

Big Data Implementation Challenge 188

Hadoop 189

Hadoop Technology Stack 189

Hadoop Solution Architecture 191

Hadoop as an Analytical Engine 193

Cloud Computing (For Analytics) 196

Disintegration in Cloud Computing 196

Analytics in Cloud Computing 197

CONCLUSION 199

REFERENCES 203

INDEX 207

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