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Understanding the Predictive Analytics Lifecycle

Understanding the Predictive Analytics Lifecycle

Alberto Cordoba

ISBN: 978-1-118-93892-8

Jul 2014

240 pages

$32.99

Description

A high-level, informal look at the different stages of the predictive analytics cycle

Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects.

Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes:

  • An overview of all relevant phases: design, prepare, explore, model, communicate, and measure
  • Coverage of the stages of the predictive analytics cycle across different industries and countries
  • A chapter dedicated to each of the phases of the development of a predictive initiative
  • A comprehensive overview of the entire analytic process lifecycle

If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide.

Foreword xi

Preface xiii

Acknowledgments xv

Chapter 1 Problem Identification and Definition 1

Importance of Clear Business Objectives 4

Office Politics 8

Note 13

Chapter 2 Design and Build 15

Managing Phase 16

Planning Phase 18

Delivery Phase 19

Notes 32

Chapter 3 Data Acquisition 33

Data: The Fuel for Analytics 36

A Data Scientist’s Job 41

Notes 53

Chapter 4 Exploration and Reporting 55

Visualization 57

Cloud Reporting 61

Chapter 5 Modeling 69

Churn Model 71

Risk Scoring Model 77

Notes 99

Chapter 6 Actionable Analytics 101

Digital Asset Management 104

Social Media 104

Chapter 7 Feedback 129

What the Different Software Components Should Do 132

Note 148

Conclusion 149

Appendix: Useful Questions 155

Bibliography 209

About the Author 211

Index 213