Applied Business Analytics Integrating Business Process Big Data & Advanced Analytics

BRIDGE THE GAP BETWEEN BUSINESS ANALYTICS AND BETTER DECISIONS

• Overcome structural and cultural obstacles to gaining value from analytics
• Optimize your use of analytics as an analyst, manager, or executive
• Discover proven best practices from real cases and examples

You possess massive data sets. You’re constantly crunching numbers. But are you really improving your decision making? As many businesses have discovered, there’s a huge gap between merely doing analytics and gaining real value from it.

Applied Business Analytics will help you bridge that gap.

Using real cases and hands-on examples, Nathaniel Lin helps you successfully integrate and profit from analytics at all levels of the business: from top-level strategy to low-level operational detail.

Lin illuminates the organizational and cultural issues that break “analytics value chains,” and helps you clear away these obstacles. You’ll learn why a special breed of “analytics deciders” is indispensable for any organization aiming to compete on analytics…how to become one of them…and how to identify, foster, support, empower, and reward others to join you.

If you’re a business professional, you don’t need to be “evangelized” about analytics. But you’ve seen enough to recognize the gap between promise and reality. As elsewhere, execution makes all the difference. Applied Business Analytics will help you execute on your analytics opportunity—even if you don’t have a Ph.D. in statistics (and don’t want one)!

Through real case studies and examples, Nathaniel Lin presents powerfully effective ways to embed analytics throughout current business processes, and use it to shape strategic new processes.

Lin starts with an up-to-date primer on the basics: data, tools, and processes. Next, he systematically addresses the crucial organizational and cultural challenges businesses face when they attempt to implement analytics.

Drawing on immense personal experience, he answers critical questions such as: How should analytics change decision-making? How should you organize, manage, and integrate it? How do you effectively promote analytics-driven decision-making? What kind of predictions can analytics credibly make? How do you detect patterns you can actually act on?

Whatever your role in leading, implementing, or utilizing analytics, this guide will help you figure out where you stand, decide where to go, and get there.

Bridge the gaps between analytics technology and better decisions

Develop unbroken “analytics value chains” that deliver maximum valu e

Go beyond implementing tools and hiring analysts

Build the right competencies, ecosystem, and culture

Understand what your analysts are telling you

…and make sure they’re delivering what you really need

Apply proven analytics workflows to solve real business problems

Walk through complete examples and adapt them to your own environment

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.

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.

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