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| Foreword and Acknowledgments | |
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| About the Authors | |
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| Introduction: The New World of Enterprise Analytics | |
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| Overview of Analytics and Their Value | |
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| What Do We Talk About When We Talk About Analytics? | |
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| Why We Needed a New Term: Issues with Traditional Business Intelligence | |
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| Three Types of Analytics | |
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| Where Does Data Mining Fit In? | |
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| Business Analytics Versus Other Types | |
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| Web Analytics | |
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| Big-Data Analytics | |
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| Conclusion | |
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| The Return on Investments in Analytics | |
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| Traditional ROI Analysis | |
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| The Teradata Method for Evaluating Analytics Investments | |
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| An Example of Calculating the Value | |
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| Analytics ROI at Freescale Semiconductor | |
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| Application of Analytics | |
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| Leveraging Proprietary Data for Analytical Advantage | |
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| Issues with Managing Proprietary Data and Analytics | |
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| Lessons Learned from Payments Data | |
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| Endnote | |
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| Analytics on Web Data: The Original Big Data | |
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| Web Data Overview | |
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| What Web Data Reveals | |
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| Web Data in Action | |
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| Wrap-Up | |
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| The Analytics of Online Engagement | |
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| The Definition of Engagement | |
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| A Model to Measure Online Engagement | |
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| The Value of Engagement Scores | |
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| Engagement Analytics at PBS | |
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| Engagement Analytics at Philly.com | |
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| The Path to "Next Best Offers" for Retail Customers | |
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| Analytics and the Path to Effective Next Best Offers | |
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| Offer Strategy Design | |
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| Know Your Customer | |
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| Know Your Offers | |
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| Know the Purchase Context | |
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| Analytics and Execution: Deciding on and Making die Offer | |
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| Learning from and Adapting NBOs | |
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| Technologies for Analytics | |
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| Applying Analytics at Production Scale | |
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| Decisions Involve Actions | |
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| Time to Business Impact | |
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| Business Decisions in Operation | |
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| Compliance Issues | |
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| Data Considerations | |
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| Example of Analytics at Production Scale: YouSee | |
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| Lessons Learned from Other Successful Companies | |
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| Endnote | |
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| Predictive Analytics in the Cloud | |
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| Business Solutions Focus | |
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| Five Key Opportunities | |
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| The State of the Market | |
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| Pros and Cons | |
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| Adopting Cloud-Based Predictive Analytics | |
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| Endnote | |
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| Analytical Technology and the Business User | |
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| Separate but Unequal | |
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| Staged Data | |
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| Multipurpose | |
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| Generally Complex | |
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| Premises- and Product-Based | |
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| Industry-Generic | |
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| Exclusively Quantitative | |
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| Business Unit-Driven | |
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| Specialized Vendors | |
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| Problems with the Current Model | |
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| Changes Emerging in Analytical Technology | |
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| Creating the Analytical Apps of the Future | |
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| Summary | |
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| Linking Decisions and Analytics for Organizational Performance | |
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| A Study of Decisions and Analytics | |
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| Linking Decisions and Analytics | |
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| A Process for Connecting Decisions and Information | |
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| Looking Ahead in Decision Management | |
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| Endnotes | |
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| The Human Side of Analytics | |
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| Organizing Analysts | |
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| Why Organization Matters | |
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| General Goals of Organizational Structure | |
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| Goals of a Particular Analytics Organization | |
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| Basic Models for Organizing Analysts | |
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| Coordination Approaches | |
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| What Model Fits Your Business? | |
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| How Bold Can You Be? | |
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| Triangulating on Your Model and Coordination Mechanisms | |
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| Analytical Leadership and the Chief Analytics Officer | |
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| To Where Should Analytical Functions Report? | |
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| Building an Analytical Ecosystem | |
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| Developing the Analytical Organization Over Time | |
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| The Bottom Line | |
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| Endnotes | |
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| Engaging Analytical Talent | |
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| Four Breeds of Analytical Talent | |
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| Engaging Analysts | |
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| Arm Analysts with Critical Information About the Business | |
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| Define Roles and Expectations | |
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| Feed Analysts' Love of New Techniques, Tools, and Technologies | |
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| Employ More Centralized Analytical Organization Structures | |
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| Governance for Analytics | |
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| Guiding Principles | |
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| Elements of Governance | |
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| You Know You're Succeeding When | |
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| Building a Global Analytical Capability | |
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| Widespread Geographic Variation | |
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| Central Coordination, Centralized Organization | |
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| A Strong Center of Excellence | |
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| A Coordinated "Division of Labor" Approach | |
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| Other Global Analytics Trends | |
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| Endnotes | |
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| Case Studies in the Use of Analytics | |
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| Partners HealthCare System | |
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| Centralized Data and Systems at Partners | |
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| Managing Clinical Informatics and Knowledge at Partners | |
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| High-Performance Medicine at Partners | |
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| New Analytical Challenges for Partners | |
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| Centralized Business Analytics at Partners | |
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| Hospital-Specific Analytical Activities: Massachusetts General Hospital | |
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| Hospital-Specific Analytical Activities: Brigham & Women's Hospital | |
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| Endnotes | |
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| Analytics in the HR Function at Sears Holdings Corporation | |
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| What We Do | |
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| Who Make Good HR Analysts | |
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| Our Recipe for Maximum Value | |
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| Key Lessons Learned | |
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| Commercial Analytics Culture and Relationships at Merck | |
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| Decision-Maker Partnerships | |
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| Reasons for the Group's Success | |
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| Embedding Analyses into Tools | |
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| Future Directions for Commercial Analytics and Decision Sciences | |
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| Descriptive Analytics for the Supply Chain at Bernard Chaus, Inc | |
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| The Need for Supply Chain Visibility | |
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| Analytics Strengthened Alignment Between Chaus's IT and Business Units | |
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| Index | |