Plain English guidance for strategic business analytics and big data implementationIn 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 businessCompanies 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 xvAcknowledgments xviiChapter 1 The Challenge of Business Analytics 1The Challenge from Outside 5The Challenge from Within 9Chapter 2 Pillars of Business Analytics Success: The BASP Framework 15Business Challenges Pillar 18Data Foundation Pillar 20Analytics Implementation Pillar 22Insight Pillar 26Execution and Measurement Pillar 29Distributed Knowledge Pillar 31Innovation Pillar 32Conclusion 33Chapter 3 Aligning Key Business Challenges across the Enterprise 35Mission Statement 36Business Challenge 38Identifying Business Challenges as a Consultative Process 39Identify and Prioritize Business Challenges 41Analytics Solutions for Business Challenges 45Chapter 4 Big and Little Data: Different Types of Intelligence 51Big Data 57Little Data 61Laying the Data Foundation: Data Quality 62Data Sources and Locations 65Data Definition and Governance 69Data Dictionary and Data Key Users 72Sanity Check and Data Visualization 72Customer Data Integration and Data Management 73Data Privacy 74Chapter 5 Who Cares about Data?How to Uncover Insights 77The IMPACT Cycle 79Curiosity Can Kill the Cat 82Master the Data 86A Fact in Search of Meaning 87Actions Speak Louder Than Data 88"Eat Like a Bird, Poop Like an Elephant" 89Track Your Outcomes 91The IMPACT Cycle in Action: The Monster Employment Index 92Chapter 6 Data Visualization: Presenting InformationClearly: The CONVINCE Framework 95Convey Meaning 97Objectivity: Be True to Your Data 99Necessity: Don't Boil the Ocean 101Visual Honesty: Size Matters 103Imagine the Audience 104Nimble: No Death by 1,000 Graphs 107Context 107Encourage Interaction 109Conclusion 109Chapter 7 Analytics Implementation: What Works and What Does Not 113Analytics Implementation Model 117Vision and Mandate 118Strategy 119Organizational Collaboration 121Human Capital 122Metrics and Measurement 123Integrated Processes 124Customer Experience 125Technology and Tools 125Change Management 126Chapter 8 Voice-of-the-Customer Analytics and Insights 131By Abhilasha Mehta, PhDCustomer Feedback Is Invaluable 132The Makings of an Effective Voice-of-the-Customer Program 137Strategy and Elements of the VOC System 152Common VOC Program Pitfalls 162Chapter 9 Leveraging Digital Analytics Effectively 165By Judah PhillipsStrategic and Tactical Use of Digital Analytics 173Understanding Digital Analytics Concepts 174Digital Analytics Team: People Are Most Important for Analytical Success 184Digital Analytics Tools 187Advanced Digital Analytics 191Digital Analytics and Voice of the Customer 192Analytics of Site and Landing Page Optimization 194Call to Action: Unify Traditional and Digital Analytics 195Chapter 10 Effective Predictive Analytics: What Works and What Does Not 199What Is Predictive Analytics? 201Unlocking Stage 203Prediction Stage 206Optimization Stage 210Diverse Applications for Diverse Business Problems 213Financial Service Industries as Pioneers 214Chapter 11 Predictive Analytics Applied to Human Resources 223By Jac Fitz-enz, PhDStaff Roles 225Assessment: Beyond People 226Planning Shift 229Competency versus Capability 229Production 230HR Process Management 231HR Analysis and Predictability 232Elevate HR with Analytics 233Value Hierarchy 235HR Reporting 237HR Success through Analytics 238Chapter 12 Social Media Analytics 247By Judah PhillipsSocial Media Is Multidimensional 249Understanding Social Media Analytics: Useful Concepts 251Is Social Media about Brand or Direct Response? 254Social Media "Brand" and "Direct Response" Analytics 255Social Media Tools 259Social Media Analytical Techniques 262Social Media Analytics and Privacy 265Chapter 13 The Competitive Intelligence Mandate 271Competitive Intelligence Defined 273Principles for CI Success 275Chapter 14 Mobile Analytics 285By Judah PhillipsUnderstanding Mobile Analytics Concepts 290How Is Mobile Analytics Different from Site Analytics? 291Importance of Measuring Mobile Analytics 295Mobile Analytics Tools 296Business Optimization with Mobile Analytics 298Chapter 15 Effective Analytics CommunicationStrategies 301Communication: The Gap between Analysts and Executives 303An Effective Analytics Communication Strategy 305Analytics Communication Tips 314Communicating through Mobile Business Intelligence 316Chapter 16 Business Performance Tracking: Execution and Measurement 321Analytics' Fundamental Questions 324Analytics Execution 325Business Performance Tracking 332Analytics and Marketing 336Chapter 17 Analytics and Innovation 343What Is Innovation? 344What Is the Promise of Advanced Analytics? 347What Makes Up Innovation in Analytics? 348Intersection between Analytics and Innovation 352Chapter 18 Unstructured Data Analytics: The Next Frontier 359What Is Unstructured Data Analytics? 360The Unstructured Data Analytics Industry 363Uses of Unstructured Data Analytics 364How Unstructured Data Analytics Works 365Why Unstructured Data Is the Next Analytical Frontier 366Unstructured Analytics Success Stories 372Chapter 19 The Future of Analytics 377Data Become Less Valuable 379Predictive Becomes the New Standard 380Social Information Processing and Distributed Computing 381Advances in Machine Learning 382Traditional Data Models Evolve 383Analytics Becomes More Accessible to the Nonanalyst 384Data Science Becomes a Specialized Department 385Human-Centered Computing 386Analytics to Solve Social Problems 387Location-Based Data Explosion 388Data Privacy Backlash 388About the Authors 391Index 393