Applied Business Analytics
Integrating Business Process, Big Data, and Advanced Analytics
Published on 12. January 2015
Book
Hardback
320 pages
978-0-13-348150-1 (ISBN)
Description
Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics... how to become one of those deciders... and how to identify, foster, support, empower, and reward others to join you.
Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes:
How analytical and conventional decision making differ - and the challenging implications
How to determine who your analytics deciders are, and ought to be
Proven best practices for actually applying analytics to decision-making
How to optimize your use of analytics as an analyst, manager, executive, or C-level officer
Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.
Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes:
How analytical and conventional decision making differ - and the challenging implications
How to determine who your analytics deciders are, and ought to be
Proven best practices for actually applying analytics to decision-making
How to optimize your use of analytics as an analyst, manager, executive, or C-level officer
Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.
More details
Other editions
Additional editions
Nathaniel Lin
Applied Business Analytics
Integrating Business Process, Big Data, and Advanced Analytics
E-Book
12/2014
1st Edition
Pearson FT Press
€68.89
Available for download
Person
Dr. Nathaniel Lin is a recognized leader in marketing and business analytics across various industries worldwide. He has over 20 years of frontline experience applying actionable advanced analytics strategies to the world's largest companies in technology, finance, automotive, telecommunications, retail, and across many other businesses, including IBM, Fidelity Investments, OgilvyOne, and Aspen Marketing Analytics.
Nathaniel is currently the Chief Customer Insights Officer of Attract China. He is leading the efforts to develop leading edge Big Data Analytics technology and knowledge assets to deliver unparalleled values to Chinese travelers and U.S. clients. Nathaniel is widely recognized as an expert, teacher, author, and hands-on leader and senior executive in the application of data and advanced analytics in a wide variety of businesses. He is also the Founder and President of Analytics Consult, LLC (www.analyticsconsult.com). He leverages his rich and unique expertise in business analytics to help companies optimize their customer, marketing, and sales strategies. Together with his team, Nathaniel serves as a trusted strategic advisor to senior management teams. He is frequently invited as the keynote speaker in analytics events and advised over 150 CEOs in the U.S. and aboard on analytics and Big Data issues. He was invited by WWW2010 as one of the four expert panelists (together with the heads of Google Analytics, eBay Analytics, and Web Analytics Association) on the Future of Predictive Analytics.
As a recognized analytics expert, Nathaniel has partnered with Professor Tom Davenport to benchmark analytics competencies of major corporations across different industries. He also demonstrates his passion in cultivating future analytics leaders by teaching Strategic CRM and Advanced Business Analytics for MBA students at the Georgia Tech College of Management, Boston College Carroll School of Management, and Quant III Advanced Business Analytics at Bentley University.
Nathaniel holds a PhD in Engineering from Birmingham University (UK) and an MBA from MIT Sloan School of Management.
Nathaniel is currently the Chief Customer Insights Officer of Attract China. He is leading the efforts to develop leading edge Big Data Analytics technology and knowledge assets to deliver unparalleled values to Chinese travelers and U.S. clients. Nathaniel is widely recognized as an expert, teacher, author, and hands-on leader and senior executive in the application of data and advanced analytics in a wide variety of businesses. He is also the Founder and President of Analytics Consult, LLC (www.analyticsconsult.com). He leverages his rich and unique expertise in business analytics to help companies optimize their customer, marketing, and sales strategies. Together with his team, Nathaniel serves as a trusted strategic advisor to senior management teams. He is frequently invited as the keynote speaker in analytics events and advised over 150 CEOs in the U.S. and aboard on analytics and Big Data issues. He was invited by WWW2010 as one of the four expert panelists (together with the heads of Google Analytics, eBay Analytics, and Web Analytics Association) on the Future of Predictive Analytics.
As a recognized analytics expert, Nathaniel has partnered with Professor Tom Davenport to benchmark analytics competencies of major corporations across different industries. He also demonstrates his passion in cultivating future analytics leaders by teaching Strategic CRM and Advanced Business Analytics for MBA students at the Georgia Tech College of Management, Boston College Carroll School of Management, and Quant III Advanced Business Analytics at Bentley University.
Nathaniel holds a PhD in Engineering from Birmingham University (UK) and an MBA from MIT Sloan School of Management.
Content
Foreword xv
Acknowledgments xviii
About the Author xix
Preface xxi
Why Another Book on Analytics? xxi
How This Book Is Organized xxii
After Reading and Working Through This Book xxvi
Chapter 1: Introduction 1
Raw Data, the New Oil 1
Data Big and Small Is Not New 2
Definition of Analytics 3
Top 10 Business Questions for Analytics 5
Financial Management 6
Customer Management 8
HR Management 11
Internal Operations 11
Vital Lessons Learned 12
Use Analytics 13
Reasons Why Analytics Are Not Used 13
Linking Analytics to Business 14
Business Analytics Value Chain 14
Integrated Approach 17
Hands-on Exercises 17
Reasons for Using KNIME Workflows 17
Conclusion 18
Chapter 2: Know Your Ingredients-Data Big and Small 21
Garbage in, Garbage out 21
Data or Big Data 22
Definition of Big Data 22
Data Types 23
Company Data 24
Individual Customer Data 31
Sensor Data 34
Syndicated Data 35
Data Formats 38
Structured, Poorly Structured, and Unstructured Data 39
Conclusion 42
Chapter 3: Data Management-Integration, Data Quality, and Governance 43
Data Integration 44
Data Quality 45
Data Security and Data Privacy 46
Data Security 47
Data Privacy 48
Data Governance 53
Data Preparation 56
Data Manipulation 58
Types of Data 58
Categorize Numerical Variables 59
Dummy Variables 60
Missing Values 60
Data Normalization 61
Data Partitions 62
Exploratory Data Analysis 64
Multidimensional Cube 65
Slicing 65
Dicing 65
Drilling Down/Up 66
Pivoting 66
Visualization of Data Patterns and Trends 66
Popularity of BI Visualization 66
Selecting a BI Visualization Tool 67
Beyond BI Visualizations 70
Conclusion 70
Chapter 4: Handle the Tools: Analytics Methodology and Tools 73
Getting Familiar with the Tools 73
Master Chefs Who Can't Cook 74
Types of Analytics 75
Descriptive and Diagnostic Tools: BI Visualization and Reporting 75
Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery 77
A Unified View of BI Analysis, Advanced Analytics, and Visualization 77
Two Ways of Knowledge Discovery 79
Types of Advanced Analytics and Applications 81
Analytics Modeling Tools by Functions 81
Modeling Likelihood 82
Modeling Groupings 86
Supervised Learning 87
Value Prediction 97
Other Models 102
Conclusion 111
Chapter 5: Analytics Decision-Making Process and the Analytics Deciders 115
Time to Take Off the Mittens 115
Overview of the Business Analytics Process (BAP) 116
Analytics Rapid Prototyping 120
Analytics Sandbox for Instant Business Insights 122
Analytics IT Sandbox Database 125
People and the Decision Blinders 125
Risks of Crossing the Chasms 126
The Medici Effect 127
Analytics Deciders 129
How to Find Analytics Deciders 130
Becoming an Analytics Decider 132
Conclusion 139
Chapter 6: Business Processes and Analytics (by Alejandro Simkievich) 141
Overview of Process Families 142
Enterprise Resource Planning 143
Customer Relationship Management 145
Product Lifecycle Management 147
Shortcomings of Operational Systems 147
Embedding Advanced Analytics into Operational Systems 150
Example 1: Forecast 152
Example 2: Improving Salesforce Decisions 154
Example 3: Engineers Get Instant Feedback on Their Design Choices 155
Conclusion 155
Chapter 7: Identifying Business Opportunities by Recognizing Patterns 157
Patterns of Group Behavior 157
Importance of Pattern Recognition in Business 158
Group Patterns by Clustering and Decision Trees 161
Three Ways of Grouping 162
Recognize Purchase Patterns: Association Analysis 167
Association Rules 167
Business Case 169
Patterns over Time: Time Series Predictions 173
Time Series Models 174
Conclusion 179
Chapter 8: Knowing the Unknowable 181
Unknowable Events 181
Unknowable in Business 182
Poor or Inadequate Data 185
Data with Limited Views 185
Business Case 186
Predicting Individual Customer Behaviors in Real-Time 192
Lever Settings and Causality in Business 197
Start with a High Baseline 199
Causality with Control Groups 199
Conclusion 201
Chapter 9: Demonstration of Business Analytics Workflows: Analytics Enterprise 203
A Case for Illustration 204
Top Questions for Analytics Applications 209
Financial Management 210
Human Resources 212
Internal Operations 213
Conclusion 218
Chapter 10: Demonstration of Business Analytics Workflows-Analytics CRM 219
Questions About Customers 220
Know the Customers 220
Actionable Customer Insights 222
Social and Mobile CRM Issues 226
CRM Knowledge Management 227
Conclusion 228
Chapter 11: Analytics Competencies and Ecosystem 231
Analytics Maturity Levels 233
Analytics Organizational Structure 234
The Centralized Model 236
The Consulting Model 237
The Decentralized Model 238
The Center of Excellence Model 239
Reporting Structures 241
Roles and Responsibilities 242
Analytics Roles 242
Business Strategy and Leadership Roles 243
Data and Quantitative Roles 247
Analytics Ecosystem 250
The In-House IT Function 250
External Analytics Advisory and Consulting
Resources 251
Analytics Talent Management 256
Conclusion 260
Chapter 12: Conclusions and Now What? 263
Analytics Is Not a Fad 263
Acquire Rich and Effective Data 264
Start with EDA and BI Analysis 265
Gain Firsthand Analytics Experience 265
Become an Analytics Decider and Recruit Others 266
Empower Enterprise Business Processes with Analytics 266
Recognize Patterns with Analytics 267
Know the Unknowable 268
Imbue Business Processes with Analytics 269
Acquire Competencies and Establish Ecosystem 270
Epilogue 271
Appendix A: KNIME Basics 273
Data Preparation 274
Types of Variable Values 274
Dummy Variables 275
Missing Values 275
Data Partitions 277
Exploratory Data Analysis (EDA) 279
Multi-Dimensional Cube 279
Slicing 281
Dicing 281
Drilling Down or Up 281
Pivoting 281
Index 285
Acknowledgments xviii
About the Author xix
Preface xxi
Why Another Book on Analytics? xxi
How This Book Is Organized xxii
After Reading and Working Through This Book xxvi
Chapter 1: Introduction 1
Raw Data, the New Oil 1
Data Big and Small Is Not New 2
Definition of Analytics 3
Top 10 Business Questions for Analytics 5
Financial Management 6
Customer Management 8
HR Management 11
Internal Operations 11
Vital Lessons Learned 12
Use Analytics 13
Reasons Why Analytics Are Not Used 13
Linking Analytics to Business 14
Business Analytics Value Chain 14
Integrated Approach 17
Hands-on Exercises 17
Reasons for Using KNIME Workflows 17
Conclusion 18
Chapter 2: Know Your Ingredients-Data Big and Small 21
Garbage in, Garbage out 21
Data or Big Data 22
Definition of Big Data 22
Data Types 23
Company Data 24
Individual Customer Data 31
Sensor Data 34
Syndicated Data 35
Data Formats 38
Structured, Poorly Structured, and Unstructured Data 39
Conclusion 42
Chapter 3: Data Management-Integration, Data Quality, and Governance 43
Data Integration 44
Data Quality 45
Data Security and Data Privacy 46
Data Security 47
Data Privacy 48
Data Governance 53
Data Preparation 56
Data Manipulation 58
Types of Data 58
Categorize Numerical Variables 59
Dummy Variables 60
Missing Values 60
Data Normalization 61
Data Partitions 62
Exploratory Data Analysis 64
Multidimensional Cube 65
Slicing 65
Dicing 65
Drilling Down/Up 66
Pivoting 66
Visualization of Data Patterns and Trends 66
Popularity of BI Visualization 66
Selecting a BI Visualization Tool 67
Beyond BI Visualizations 70
Conclusion 70
Chapter 4: Handle the Tools: Analytics Methodology and Tools 73
Getting Familiar with the Tools 73
Master Chefs Who Can't Cook 74
Types of Analytics 75
Descriptive and Diagnostic Tools: BI Visualization and Reporting 75
Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery 77
A Unified View of BI Analysis, Advanced Analytics, and Visualization 77
Two Ways of Knowledge Discovery 79
Types of Advanced Analytics and Applications 81
Analytics Modeling Tools by Functions 81
Modeling Likelihood 82
Modeling Groupings 86
Supervised Learning 87
Value Prediction 97
Other Models 102
Conclusion 111
Chapter 5: Analytics Decision-Making Process and the Analytics Deciders 115
Time to Take Off the Mittens 115
Overview of the Business Analytics Process (BAP) 116
Analytics Rapid Prototyping 120
Analytics Sandbox for Instant Business Insights 122
Analytics IT Sandbox Database 125
People and the Decision Blinders 125
Risks of Crossing the Chasms 126
The Medici Effect 127
Analytics Deciders 129
How to Find Analytics Deciders 130
Becoming an Analytics Decider 132
Conclusion 139
Chapter 6: Business Processes and Analytics (by Alejandro Simkievich) 141
Overview of Process Families 142
Enterprise Resource Planning 143
Customer Relationship Management 145
Product Lifecycle Management 147
Shortcomings of Operational Systems 147
Embedding Advanced Analytics into Operational Systems 150
Example 1: Forecast 152
Example 2: Improving Salesforce Decisions 154
Example 3: Engineers Get Instant Feedback on Their Design Choices 155
Conclusion 155
Chapter 7: Identifying Business Opportunities by Recognizing Patterns 157
Patterns of Group Behavior 157
Importance of Pattern Recognition in Business 158
Group Patterns by Clustering and Decision Trees 161
Three Ways of Grouping 162
Recognize Purchase Patterns: Association Analysis 167
Association Rules 167
Business Case 169
Patterns over Time: Time Series Predictions 173
Time Series Models 174
Conclusion 179
Chapter 8: Knowing the Unknowable 181
Unknowable Events 181
Unknowable in Business 182
Poor or Inadequate Data 185
Data with Limited Views 185
Business Case 186
Predicting Individual Customer Behaviors in Real-Time 192
Lever Settings and Causality in Business 197
Start with a High Baseline 199
Causality with Control Groups 199
Conclusion 201
Chapter 9: Demonstration of Business Analytics Workflows: Analytics Enterprise 203
A Case for Illustration 204
Top Questions for Analytics Applications 209
Financial Management 210
Human Resources 212
Internal Operations 213
Conclusion 218
Chapter 10: Demonstration of Business Analytics Workflows-Analytics CRM 219
Questions About Customers 220
Know the Customers 220
Actionable Customer Insights 222
Social and Mobile CRM Issues 226
CRM Knowledge Management 227
Conclusion 228
Chapter 11: Analytics Competencies and Ecosystem 231
Analytics Maturity Levels 233
Analytics Organizational Structure 234
The Centralized Model 236
The Consulting Model 237
The Decentralized Model 238
The Center of Excellence Model 239
Reporting Structures 241
Roles and Responsibilities 242
Analytics Roles 242
Business Strategy and Leadership Roles 243
Data and Quantitative Roles 247
Analytics Ecosystem 250
The In-House IT Function 250
External Analytics Advisory and Consulting
Resources 251
Analytics Talent Management 256
Conclusion 260
Chapter 12: Conclusions and Now What? 263
Analytics Is Not a Fad 263
Acquire Rich and Effective Data 264
Start with EDA and BI Analysis 265
Gain Firsthand Analytics Experience 265
Become an Analytics Decider and Recruit Others 266
Empower Enterprise Business Processes with Analytics 266
Recognize Patterns with Analytics 267
Know the Unknowable 268
Imbue Business Processes with Analytics 269
Acquire Competencies and Establish Ecosystem 270
Epilogue 271
Appendix A: KNIME Basics 273
Data Preparation 274
Types of Variable Values 274
Dummy Variables 275
Missing Values 275
Data Partitions 277
Exploratory Data Analysis (EDA) 279
Multi-Dimensional Cube 279
Slicing 281
Dicing 281
Drilling Down or Up 281
Pivoting 281
Index 285