Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics

Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics

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Overview

Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics by Jeff Deal, Gerhard Pilcher

Data Mining and Predictive Analytics –

21st Century’s Most Powerful New Management Tools

In this practical guide for organizational leaders and top-level executives, industry experts Jeff Deal and Gerhard Pilcher explain in clear, understandable English…

  • What data mining and predictive analytics are
  • Why they are such powerful management tools
  • How and when to use them for greatest positive

impact across a broad spectrum of industries

Complete with solid advice and instructive case histories, it demonstrates how to harness the power of data mining and predictive analytics, while avoiding costly mistakes.

Use it to gain a quick overview of the subject and as a handy resource to be referred to again and again.

If you’re preparing to lead or participate in a data analytics initiative, this is the one book you must read!

Receiving early, strong praise from business government leaders who are using these powerful management tools to achieve dramatic goals for projects and their organizations.

Product Details

ISBN-13: 9780996712101
Publisher: Data Science Publishing
Publication date: 09/19/2016
Pages: 184
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Jeff Deal is the Vice President of Operations for Elder Research, Inc. Working out of the firm's corporate headquarters in Charlottesville, Virginia, he oversees operational, contractual, and financial matters. Drawing on his more than twenty-five years of management experience in business and government, he regularly helps organizational clients clarify and attain their data analytics goals.
A frequent speaker on the subject of organizational challenges to meeting data analytics goals, Mr. Deal is the program chair for the annual Predictive Analytics
World - Healthcare conference, which annually attracts leading analytics professionals in the healthcare industry from around the country. He holds a Master of Health Administration degree from Virginia Commonwealth University in Richmond, Virginia, and a Bachelor of Arts degree from the College of William and Mary in Williamsburg, Virginia, where he was a member of the wrestling team. Jeff and his wife, Jennifer, have four children. In his spare time he enjoys hiking, reading, and an increasing amount of recreational travel with his wife, now that their kids have all moved out of the house.

Gerhard Pilcher, Chief Executive Officer for Elder Research, is responsible for the firm's northern Virginia office. He has more than thirty years of industry and consulting experience with commercial businesses and government institutions in the United States and abroad. His specialties include fraud detection, financial risk management, and healthcare outcomes.
Mr. Pilcher earned a Master of Science degree in analytics from the Institute for Advanced Analytics at North Carolina State University in Raleigh, North Carolina. He is an adjunct faculty member in the math and statistics masters degree program at Georgetown University, and a regular instructor at the SAS Business Knowledge Series course, "Data Mining: Principles and Best Practices." Gerhard currently serves on the advisory boards of the Institute for Advanced Analytics and the Masters in Science in Business Analytics program at George Washington University.
Gerhard and his wife, Denise, have two children. In his spare time, he especially enjoys outdoor activities, including mountaineering and trail running.

Table of Contents

CONTENTS
Foreword xiii
Introduction xvii
How to Use This Book xix
Introduction and Overview
1. Empowering the Decision Makers 1
• Hunting for Needles in Haystacks 1
• Breaking the Mind Barrier 2
• A World of Applications 4
2. Clearing Up the Confusion 7
• Ten Levels of Analytics 8
• Four Categories of Modeling Knowledge 14
• Supervised vs. Unsupervised Learning 16
• Levels and Advanced Data Types 18
The Analytic Organization
3. Leading a Data Analytics Initiative 21
• Starting Small 23
• Examples of Poor vs. Good Focus 24
• Cultivating the Culture 25
• Managing a Data Analytics Initiative 26
• The Experiences of a Mobile Phone Service Provider 27
• Leadership is Key 30
• A Parade of Champions at a Federal Agency 31
• A Lack of Leadership at a Financial Firm 31
• The Effect of Different Leadership Styles at a Government Agency 32
• Bold Leadership Required 32
4. Staffing a Data Analytics Project 35
• Individual or Team? 36
• Assembling the Team 37
• What is a Data Scientist? 39
• More than Academic Credentials 41
• The Most Important Quality 42
• Mike Thurber's Story 43
• Building Teams through "Gap Analysis" 43
5. Acquiring the Right Tools 47
• A Variety of Techniques and Disciplines 48
• Interface Level of Tools 51
• Sources of Tools 53
• A Word about Open-Source Tools 54
• Tool Trends 55
6. Hiring Data Analytics Consultants 59
• Discerning Fact from Hype 60
• Evaluating Industry Experience 61
• Evaluating Analytics Experience 63
• Finding the Right Consultant 63
The Modeling Process
7. Understanding the Data Mining Process 67
• The CRISP-DM Process 67
• Resist the Temptation to Take Shortcuts 69
8. Understanding the Business 73
• Clarifying Your Objective 74
• Defining the Terminology 75
• Framing the Questions 76
• An Unexpected Finding 77
9. Understanding and Preparing the Data 81
• Understanding the Data 81
• Cleaning the Data 82
• "Perfect" Data 83
• Collecting and Preparing the Data 84
• Fostering Cooperation 85
• Governing the Data 86
10. Building the Model 89
• Inside the "Black Box" 89
• Building an Illustrative Model 90
• Non-Linear Models 93
• Choosing a Model 95
• Response Surfaces of Predictive Models 97
• The Trade-off between Accuracy and Interpretability 98
• Choosing and Testing Modeling Algorithms 98
xi
• Dealing with Variance 99
• Model Ensembles 100
11. Validating the Model 103
• Technical Validation 103
• Checking for Mistakes 106
• Checking for Generalization 112
• Using Experts to Qualify Model Results 112
• Target Shuffling 114
• Business Validation 116
Putting the Model into Practice
12. Deploying the Model 119
• Planning and Budgeting for Deployment 119
• Business Processes Are Key 120
• Example: Finding Taxpayer Fraud 121
• Four Important Questions 122
13. Realizing the Transformation 125
• Realizing the Potential 125
• The Tipping Point 126
Appendix
• Are Orange Cars Really Least Likely To Be Lemons? 129
About the Authors 151
About Elder Research, Inc. 152
Index 153

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