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Summary
Summary
The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management
Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding "how, when, and where" events have occurred, to understand why - and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today's definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.Author Notes
THOMAS H. DAVENPORT , (Cambridge, MA) co-founder and Director of Research of the International Institute for Analytics, a world renowned thought leader and executive advisor on analytics. His book, Analytics at Work: Smarter Decisions, Better Results , was named a must-read for 2010 by CIO Insight. Davenport holds a Ph.D. from Harvard University, taught at Harvard Business School, and led research centers at McKinsey and CSC. He is President's Distinguished Professor of Information Technology and Management at Babson College; senior advisor, Deloitte Analytics, Deloitte Touche Tohmatsu; and member, Board of Sponsors, MIT Center for Information Systems.
Reviews 1
Choice Review
Editor Davenport (Babson College; author of Competing on Analytics, with Jeanne Harris, CH, Jul'07, 44-6322) is cofounder and director of research at the International Institute for Analytics , and contributors to this work are all associated with IIA. In the introduction, Davenport observes that analytical approaches to decision making and management are on the rise due to four factors: the dramatic increase in the amounts of data to analyze from various business information systems; powerful and inexpensive computers and software that can analyze all this data; the movement of quantitatively trained managers into positions of responsibility within organizations; and the need to differentiate products and offers, optimize prices and inventories, and understand what drives various aspects of business performance. The book's main theme is that analytics has become an enterprise resource; many chapters relate to how analytics can and should be managed at an enterprise level. The book is organized in five parts. An overview of analytics and their return on investments is followed by discussions on the application of analytics, the technologies for analytics, and the human side of analytics; the book concludes with case studies. This well-written volume has a strong practitioner emphasis. Summing Up: Recommended. Upper-division undergraduate and graduate students; practitioners. E. J. Szewczak Canisius College
Table of Contents
| Foreword and Acknowledgments | p. xii |
| About the Authors | p. xiv |
| Introduction: The New World of Enterprise Analytics | p. 1 |
| Part I Overview of Analytics and Their Value | |
| Chapter 1 What Do We Talk About When We Talk About Analytics? | p. 9 |
| Why We Needed a New Term: Issues with Traditional Business Intelligence | p. 11 |
| Three Types of Analytics | p. 12 |
| Where Does Data Mining Fit In? | p. 14 |
| Business Analytics Versus Other Types | p. 15 |
| Web Analytics | p. 16 |
| Big-Data Analytics | p. 16 |
| Conclusion | p. 18 |
| Chapter 2 The Return on Investments in Analytics | p. 19 |
| Traditional ROI Analysis | p. 19 |
| The Teradata Method for Evaluating Analytics Investments | p. 24 |
| An Example of Calculating the Value | p. 27 |
| Analytics ROI at Freescale Semiconductor | p. 28 |
| Part II Application of Analytics | |
| Chapter 3 Leveraging Proprietary Data for Analytical Advantage | p. 37 |
| Issues with Managing Proprietary Data and Analytics | p. 39 |
| Lessons Learned from Payments Data | p. 45 |
| Endnote | p. 46 |
| Chapter 4 Analytics on Web Data: The Original Big Data | p. 47 |
| Web Data Overview | p. 48 |
| What Web Data Reveals | p. 54 |
| Web Data in Action | p. 60 |
| Wrap-Up | p. 68 |
| Chapter 5 The Analytics of Online Engagement | p. 71 |
| The Definition of Engagement | p. 71 |
| A Model to Measure Online Engagement | p. 74 |
| The Value of Engagement Scores | p. 76 |
| Engagement Analytics at PBS | p. 77 |
| Engagement Analytics at Philly.com | p. 79 |
| Chapter 6 The Path to "Next Best Offers" for Retail Customers | p. 83 |
| Analytics and the Path to Effective Next Best Offers | p. 84 |
| Offer Strategy Design | p. 85 |
| Know Your Customer | p. 87 |
| Know Your Offers | p. 87 |
| Know the Purchase Context | p. 88 |
| Analytics and Execution: Deciding on and Making die Offer | p. 90 |
| Learning from and Adapting NBOs | p. 93 |
| Part III Technologies for Analytics | |
| Chapter 7 Applying Analytics at Production Scale | p. 97 |
| Decisions Involve Actions | p. 98 |
| Time to Business Impact | p. 99 |
| Business Decisions in Operation | p. 100 |
| Compliance Issues | p. 100 |
| Data Considerations | p. 101 |
| Example of Analytics at Production Scale: YouSee | p. 101 |
| Lessons Learned from Other Successful Companies | p. 107 |
| Endnote | p. 109 |
| Chapter 8 Predictive Analytics in the Cloud | p. 111 |
| Business Solutions Focus | p. 112 |
| Five Key Opportunities | p. 113 |
| The State of the Market | p. 116 |
| Pros and Cons | p. 118 |
| Adopting Cloud-Based Predictive Analytics | p. 119 |
| Endnote | p. 121 |
| Chapter 9 Analytical Technology and the Business User | p. 123 |
| Separate but Unequal | p. 123 |
| Staged Data | p. 124 |
| Multipurpose | p. 124 |
| Generally Complex | p. 125 |
| Premises- and Product-Based | p. 125 |
| Industry-Generic | p. 125 |
| Exclusively Quantitative | p. 126 |
| Business Unit-Driven | p. 126 |
| Specialized Vendors | p. 127 |
| Problems with the Current Model | p. 127 |
| Changes Emerging in Analytical Technology | p. 128 |
| Creating the Analytical Apps of the Future | p. 130 |
| Summary | p. 134 |
| Chapter 10 Linking Decisions and Analytics for Organizational Performance | p. 135 |
| A Study of Decisions and Analytics | p. 136 |
| Linking Decisions and Analytics | p. 138 |
| A Process for Connecting Decisions and Information | p. 146 |
| Looking Ahead in Decision Management | p. 150 |
| Endnotes | p. 151 |
| Part IV The Human Side of Analytics | |
| Chapter 11 Organizing Analysts | p. 157 |
| Why Organization Matters | p. 157 |
| General Goals of Organizational Structure | p. 158 |
| Goals of a Particular Analytics Organization | p. 159 |
| Basic Models for Organizing Analysts | p. 160 |
| Coordination Approaches | p. 163 |
| What Model Fits Your Business? | p. 165 |
| How Bold Can You Be? | p. 168 |
| Triangulating on Your Model and Coordination Mechanisms | p. 169 |
| Analytical Leadership and the Chief Analytics Officer | p. 173 |
| To Where Should Analytical Functions Report? | p. 174 |
| Building an Analytical Ecosystem | p. 175 |
| Developing the Analytical Organization Over Time | p. 176 |
| The Bottom Line | p. 177 |
| Endnotes | p. 178 |
| Chapter 12 Engaging Analytical Talent | p. 179 |
| Four Breeds of Analytical Talent | p. 179 |
| Engaging Analysts | p. 180 |
| Arm Analysts with Critical Information About the Business | p. 182 |
| Define Roles and Expectations | p. 183 |
| Feed Analysts' Love of New Techniques, Tools, and Technologies | p. 184 |
| Employ More Centralized Analytical Organization Structures | p. 185 |
| Chapter 13 Governance for Analytics | p. 187 |
| Guiding Principles | p. 188 |
| Elements of Governance | p. 189 |
| You Know You're Succeeding When | p. 200 |
| Chapter 14 Building a Global Analytical Capability | p. 203 |
| Widespread Geographic Variation | p. 203 |
| Central Coordination, Centralized Organization | p. 205 |
| A Strong Center of Excellence | p. 206 |
| A Coordinated "Division of Labor" Approach | p. 207 |
| Other Global Analytics Trends | p. 210 |
| Endnotes | p. 212 |
| Part V Case Studies in the Use of Analytics | |
| Chapter 15 Partners HealthCare System | p. 215 |
| Centralized Data and Systems at Partners | p. 215 |
| Managing Clinical Informatics and Knowledge at Partners | p. 218 |
| High-Performance Medicine at Partners | p. 220 |
| New Analytical Challenges for Partners | p. 223 |
| Centralized Business Analytics at Partners | p. 225 |
| Hospital-Specific Analytical Activities: Massachusetts General Hospital | p. 226 |
| Hospital-Specific Analytical Activities: Brigham & Women's Hospital | p. 229 |
| Endnotes | p. 232 |
| Chapter 16 Analytics in the HR Function at Sears Holdings Corporation | p. 233 |
| What We Do | p. 233 |
| Who Make Good HR Analysts | p. 235 |
| Our Recipe for Maximum Value | p. 237 |
| Key Lessons Learned | p. 238 |
| Chapter 17 Commercial Analytics Culture and Relationships at Merck | p. 241 |
| Decision-Maker Partnerships | p. 242 |
| Reasons for the Group's Success | p. 243 |
| Embedding Analyses into Tools | p. 245 |
| Future Directions for Commercial Analytics and Decision Sciences | p. 246 |
| Chapter 18 Descriptive Analytics for the Supply Chain at Bernard Chaus, Inc | p. 249 |
| The Need for Supply Chain Visibility | p. 250 |
| Analytics Strengthened Alignment Between Chaus's IT and Business Units | p. 253 |
| Index | p. 255 |
