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<p>Foreword ix</p> <p>Introduction xiii</p> <p><b>Chapter 1 Reimagining the Enterprise 1</b></p> <p>Disruption and Decision Making 3</p> <p>Self-Disruption at Cisco: On Purpose and at Scale 4</p> <p>Self-Disrupt in Sustainable Ways 5</p> <p>Analytic Pain Points and a Self-Service Revolution 6</p> <p>Access and Control 8</p> <p>A Necessary Evolution 11</p> <p>Putting It All Together 12</p> <p>Chapter 2 Leveraging an Expanding Universe of Data 15</p> <p>A Universe of Data: Expanding Exponentially with New Sources 17</p> <p>Game-Changing Capabilities 20</p> <p>Well-Intentioned Anarchy 22</p> <p>Data Marts and Their Discontents 23</p> <p>A Solution? “LinkedIn for Analytics” 25</p> <p>Getting Back to eBay: Fulfilling the Analytics Mandate 27</p> <p>Chapter 3 The Agile Data Platform 31</p> <p>Retaining Agility at Scale 32</p> <p>Rethinking Waterfall Methodologies 34</p> <p>Agile Analytics 35</p> <p>Spreading Agility Company-Wide with the Virtual Data Mart 36</p> <p>A Virtual Data Mart (by Any Other Name) in Action 38</p> <p>Time Boxing 39</p> <p>Fewer Requirements, More Prototypes 40</p> <p>Analytics on Analytics 41</p> <p>Making It Real with the Layered Data Architecture 42</p> <p>Driving Change in the Auto Industry 45</p> <p>Remembering the Big Picture 46</p> <p>Chapter 4 The Behavioral Data Platform 49</p> <p>Personalized—If Not Personal—Interaction 52</p> <p>New Measures for Success, Built on Behavioral Data 53</p> <p>Leveraging Behavioral Data for Real-World Business Challenges 55</p> <p>Behavioral Data Is Everywhere 58</p> <p>Agile Systems for Behavioral Data 59</p> <p>Back Inside the Layered Data Architecture 62</p> <p>Reaping Value and Insight 64</p> <p>Proactive Data Standards and Designing for the Unknown 65</p> <p>Chapter 5 The Collaborative Ideation Platform 67</p> <p>Avoiding “Anti-Social” Analytics 68</p> <p>The Problem of Metadata at Scale 70</p> <p>Collaboration and Context at Scale 72</p> <p>Merchandising Analytic Insights 73</p> <p>Staying on the Path to Value through Analytics on Analytics 75</p> <p>Adoption Takes Time 77</p> <p>Operationalizing Insights 78</p> <p>Chapter 6 The Analytical Application Platform 81</p> <p>Turning Analytic Insight into Action Across the Organization 83</p> <p>Lessons from the Cloud 85</p> <p>Creating an App Economy for the Enterprise 86</p> <p>DevOps to Make It Real 88</p> <p>Less ETL . . . 91</p> <p>. . . More “Data Listening” 92</p> <p>Setup for Sentience 94</p> <p>Chapter 7 The Autonomous Decisioning Platform 97</p> <p>Fast-Changing Capabilities 99</p> <p>Self-Driving Cars … and Companies 100</p> <p>“System of Systems” Building Blocks for Sentience 101</p> <p>Algorithms: A Must-Have for Autonomous Decisioning 102</p> <p>Strategically Applying Algorithmic Intelligence in the Enterprise 105</p> <p>Algorithmic “Magic” 106</p> <p>Analytics on Algorithms to Improve Decision Making 107</p> <p>Combining Algorithms on the Home Stretch to Sentience 109</p> <p>Agility as the Ultimate Litmus Test 111</p> <p><b>Chapter 8 Implementing Your Course to Sentience 113</b></p> <p>Ask the Right Questions, Warts and All 115</p> <p>Agile Strategic Planning Is Not an Oxymoron 116</p> <p>Adopt a Start-Up Mind-Set and Don’t Boil the Ocean 118</p> <p>Pick the Right Internal Partners to Demonstrate Value 119</p> <p>Embrace Agile Project Management Strategies 120</p> <p>Embrace Concurrency, Ensure Scalability 120</p> <p>Design in Governance That’s Seamless and Repeatable 121</p> <p>Optimize a Workforce to Act Fast, Fail Fast, and Scale Fast 122</p> <p>“It’s the Culture, Stupid” 123</p> <p>Conclusion 125</p> <p>Acknowledgments 137</p> <p>About the Authors 139</p> <p>Index 141</p> <p> </p>

<p><b>OLIVER RATZESBERGER</b> is executive vice president and chief product officer of Teradata Corporation. Prior to Teradata, he spent seven years at eBay where he led their data warehouse and big data platform programs. He also has deep experience with open source startups. Ratzesberger joined Teradata in early 2013 as leader of the Teradata Research & Development software teams. As head of Teradata R&D, he now oversees a global organization including more than 1,900 technologists around the world.</p> <p><b>MOHAN SAWHNEY</b> is the McCormick Foundation Chair of Technology, Clinical Professor of Marketing and the Director, Center for Research in Technology & Innovation at the Kellogg School of Management at Northwestern University. Professor Sawhney is a globally recognized scholar, teacher, consultant and speaker in innovation, strategic marketing and new media. His research and teaching currently focuses on modern marketing, organic growth and business innovation. Professor Sawhney advises and speaks to Global 2000 firms and governments worldwide and has written six management books as well as dozens of influential articles in leading academic journals and managerial publications.</p>

<p><b>A NEW MODEL FOR ANALYTIC CAPABILITY, MATURITY, AND AGILITY AT SCALE</b> <p><i>The Sentient Enterprise</i> summarizes the achievement of "sentience" that an analytics journey should lead companies toward. It's not meant to represent an actual destination. <p><i>The Sentient Enterprise</i> is more of a North Star that every large business should aspire to be, as it struggles to make decisions at the speed of data. However, Mohan and Oliver did determine that a Sentient Enterprise must have five foundational qualities. <p>It must be: <ul> <li><b>Proactive</b> and able to sense micro-trends signaling the next opportunity or alerting the next crisis</li> <li><b>Frictionless</b> because it can act as a single organism without any impedance or bottlenecks</li> <li><b>Autonomous</b> to listen to data and make decisions in real time without much human intervention</li> <li><b>Scalable</b> to any size company and able to leverage unlimited data for making decisions</li> <li><b>Evolving</b> through intelligence that is native and emergent</li> </ul> <p>In order to get there, any enterprise has to go on a journey. <i>The Sentient Enterprise</i> journey is the way your company will get there. This methodology is disrupting entire industries and changing the very nature – the very identity – of large companies across nearly all sectors of our economy. It is a journey every big company should take if they seek to understand how data can help them become even better in order to survive and compete in today's data-driven marketplace. <p>It will change the way everyone in business makes decisions – from small, tactical ones to mission-critical ones – by continuously decomposing problems into manageable components. <p>But it is a journey only a few brave ones have started because it is as long-term and complex as it is realistic and valuable. But make no mistake about it – it's an ongoing journey. <p>There is no finish line. <p>This is not for the meek or faint of heart. <p>It's for the early adopters among you who want to get ahead of the competition or for the followers among you to ensure your survival. <p>And for the unconcerned? Hmm… well… odds are you will lose out to your competitors who are already building architectures and strategies to lead the way. <p>Mohan and Oliver believe you can be the pioneers of your organizations. You want to take your company in the direction it needs to go to survive, lead, and disrupt in this changing, data-intensive world. <p>This book will inspire you by getting you to think about how this model can bring you competitive advantage and not just ideas about data innovation. Success is possible if you embrace the changes and methods needed to fully leverage big data analytics for your businesses.

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