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Win with Advanced Business Analytics: Creating Business Value from Your Data

Win with Advanced Business Analytics: Creating Business Value from Your Data

Jean-Paul Isson, Jesse Harriott

ISBN: 978-1-118-41708-9 September 2012 416 Pages




Plain English guidance for strategic business analytics and big data implementation

In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice.

  • Provides the essential concept and framework to implement business analytics
  • Written clearly for a nontechnical audience
  • Filled with case studies across a variety of industries
  • Uniquely focuses on integrating multiple types of big data intelligence into your business

Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

Preface xv

Acknowledgments xvii

Chapter 1 The Challenge of Business Analytics 1

The Challenge from Outside 5

The Challenge from Within 9

Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15

Business Challenges Pillar 18

Data Foundation Pillar 20

Analytics Implementation Pillar 22

Insight Pillar 26

Execution and Measurement Pillar 29

Distributed Knowledge Pillar 31

Innovation Pillar 32

Conclusion 33

Chapter 3 Aligning Key Business Challenges across the Enterprise 35

Mission Statement 36

Business Challenge 38

Identifying Business Challenges as a Consultative Process 39

Identify and Prioritize Business Challenges 41

Analytics Solutions for Business Challenges 45

Chapter 4 Big and Little Data: Different Types of Intelligence 51

Big Data 57

Little Data 61

Laying the Data Foundation: Data Quality 62

Data Sources and Locations 65

Data Definition and Governance 69

Data Dictionary and Data Key Users 72

Sanity Check and Data Visualization 72

Customer Data Integration and Data Management 73

Data Privacy 74

Chapter 5 Who Cares about Data?

How to Uncover Insights 77

The IMPACT Cycle 79

Curiosity Can Kill the Cat 82

Master the Data 86

A Fact in Search of Meaning 87

Actions Speak Louder Than Data 88

“Eat Like a Bird, Poop Like an Elephant” 89

Track Your Outcomes 91

The IMPACT Cycle in Action: The Monster Employment Index 92

Chapter 6 Data Visualization: Presenting Information

Clearly: The CONVINCE Framework 95

Convey Meaning 97

Objectivity: Be True to Your Data 99

Necessity: Don’t Boil the Ocean 101

Visual Honesty: Size Matters 103

Imagine the Audience 104

Nimble: No Death by 1,000 Graphs 107

Context 107

Encourage Interaction 109

Conclusion 109

Chapter 7 Analytics Implementation: What Works and What Does Not 113

Analytics Implementation Model 117

Vision and Mandate 118

Strategy 119

Organizational Collaboration 121

Human Capital 122

Metrics and Measurement 123

Integrated Processes 124

Customer Experience 125

Technology and Tools 125

Change Management 126

Chapter 8 Voice-of-the-Customer Analytics and Insights 131

By Abhilasha Mehta, PhD

Customer Feedback Is Invaluable 132

The Makings of an Effective Voice-of-the-Customer Program 137

Strategy and Elements of the VOC System 152

Common VOC Program Pitfalls 162

Chapter 9 Leveraging Digital Analytics Effectively 165

By Judah Phillips

Strategic and Tactical Use of Digital Analytics 173

Understanding Digital Analytics Concepts 174

Digital Analytics Team: People Are Most Important for Analytical Success 184

Digital Analytics Tools 187

Advanced Digital Analytics 191

Digital Analytics and Voice of the Customer 192

Analytics of Site and Landing Page Optimization 194

Call to Action: Unify Traditional and Digital Analytics 195

Chapter 10 Effective Predictive Analytics: What Works and What Does Not 199

What Is Predictive Analytics? 201

Unlocking Stage 203

Prediction Stage 206

Optimization Stage 210

Diverse Applications for Diverse Business Problems 213

Financial Service Industries as Pioneers 214

Chapter 11 Predictive Analytics Applied to Human Resources 223
By Jac Fitz-enz, PhD

Staff Roles 225

Assessment: Beyond People 226

Planning Shift 229

Competency versus Capability 229

Production 230

HR Process Management 231

HR Analysis and Predictability 232

Elevate HR with Analytics 233

Value Hierarchy 235

HR Reporting 237

HR Success through Analytics 238

Chapter 12 Social Media Analytics 247
By Judah Phillips

Social Media Is Multidimensional 249

Understanding Social Media Analytics: Useful Concepts 251

Is Social Media about Brand or Direct Response? 254

Social Media “Brand” and “Direct Response” Analytics 255

Social Media Tools 259

Social Media Analytical Techniques 262

Social Media Analytics and Privacy 265

Chapter 13 The Competitive Intelligence Mandate 271

Competitive Intelligence Defined 273

Principles for CI Success 275

Chapter 14 Mobile Analytics 285
By Judah Phillips

Understanding Mobile Analytics Concepts 290

How Is Mobile Analytics Different from Site Analytics? 291

Importance of Measuring Mobile Analytics 295

Mobile Analytics Tools 296

Business Optimization with Mobile Analytics 298

Chapter 15 Effective Analytics Communication

Strategies 301

Communication: The Gap between Analysts and Executives 303

An Effective Analytics Communication Strategy 305

Analytics Communication Tips 314

Communicating through Mobile Business Intelligence 316

Chapter 16 Business Performance Tracking: Execution and Measurement 321

Analytics’ Fundamental Questions 324

Analytics Execution 325

Business Performance Tracking 332

Analytics and Marketing 336

Chapter 17 Analytics and Innovation 343

What Is Innovation? 344

What Is the Promise of Advanced Analytics? 347

What Makes Up Innovation in Analytics? 348

Intersection between Analytics and Innovation 352

Chapter 18 Unstructured Data Analytics: The Next Frontier 359

What Is Unstructured Data Analytics? 360

The Unstructured Data Analytics Industry 363

Uses of Unstructured Data Analytics 364

How Unstructured Data Analytics Works 365

Why Unstructured Data Is the Next Analytical Frontier 366

Unstructured Analytics Success Stories 372

Chapter 19 The Future of Analytics 377

Data Become Less Valuable 379

Predictive Becomes the New Standard 380

Social Information Processing and Distributed Computing 381

Advances in Machine Learning 382

Traditional Data Models Evolve 383

Analytics Becomes More Accessible to the Nonanalyst 384

Data Science Becomes a Specialized Department 385

Human-Centered Computing 386

Analytics to Solve Social Problems 387

Location-Based Data Explosion 388

Data Privacy Backlash 388

About the Authors 391

Index 393