Details

Preface xi <p>Acknowledgments xvii</p> <p>List of Use Cases xix</p> <p><b>Part I: The Top 12 Fallacies about Mind+Machine 1</b></p> <p>Fallacy #1: Big Data Solves Everything 3</p> <p>Fallacy #2: More Data Means More Insight 17</p> <p>Fallacy #3: First, We Need a Data Lake and Tools 26</p> <p>Fallacy #4: Analytics Is Just an Analytics Challenge: Part I: The Last Mile 31</p> <p>Fallacy #5: Analytics Is Just an Analytics Challenge: Part II: The Organization 36</p> <p>Fallacy #6: Reorganizations Won’t Hurt Analytics 40</p> <p>Fallacy #7: Knowledge Management Is Easy—We Just Need Some Wikis 45</p> <p>Fallacy #8: Intelligent Machines Can Solve Any Analytic Problem 49</p> <p>Fallacy #9: Everything Must Be Done In-House! 61</p> <p>Fallacy #10: We Need More, Larger, and Fancier Reports 66</p> <p>Fallacy #11: Analytics Investment Means Great ROI 72</p> <p>Fallacy #12: Analytics Is a Rational Process 78</p> <p>Part I: Conclusion 82</p> <p><b>Part II: 13 Trends Creating Massive Opportunities for Mind+Machine 85</b></p> <p>Trend #1: The Asteroid Impact of Cloud and Mobile 87</p> <p>Trend #2: The Yin and Yang of the Internet of Things 96</p> <p>Trend #3: One-to-One Marketing 105</p> <p>Trend #4: Regulatory Flooding of the Ring of Knowledge 111</p> <p>The European Union and Privacy Rules: The General Data Protection Regulation and the EU–US Privacy Shield 114</p> <p>The Teeth of the General Data Protection Regulation 115</p> <p>Privacy Impacting the Ring of Knowledge 120</p> <p>The Nine Questions You Need to Ask Your CIO Regarding Personal Data 121</p> <p>Trend #5: The Seismic Shift to Pay-as-You-Go or Output-Based Commercial Models 123</p> <p>Trend #6: The Hidden Treasures of Multiple-Client Utilities 133</p> <p>Trend #7: The Race for Data Assets, Alternative Data, and Smart Data 136</p> <p>Trend #8: Marketplaces and the Sharing Economy Finally Arriving in Data and Analytics 144</p> <p>Trend #9: Knowledge Management 2.0—Still an Elusive Vision? 147</p> <p>Trend #10: Workfl ow Platforms and Process Automation for Analytics Use Cases 156</p> <p>Trend #11: 2015–2025: The Rise of the Mind–Machine Interface 164</p> <p>Trend #12: Agile, Agile, Agile 172</p> <p>Trend #13: (Mind+Machine)2 = Global Partnering Equals More Than 1+1 177</p> <p>Era 1: Pure Geographic Cost Arbitrage (2000–2005) 178</p> <p>Era 2: Globalizing Outsourcing (2005–2015) 180</p> <p>Era 3: Process Reengineering (2007–2015) and Specialization 184</p> <p>Era 4: Hybrid On-Site, Near-Shore, and Far-Shore Outsourcing (2010–) 184</p> <p>Era 5: Mind+Machine in Outsourcing (2010–) 185</p> <p>Pricing and Performance Benchmarks 190</p> <p>The Future of Outsourcing in Knowledge-Intensive Processes 194</p> <p>Part II: Conclusion 196</p> <p><b>Part III: How to Implement the Mind+Machine Approach 197</b></p> <p>The Analytics Use Case Methodology: A Change in Mind-Set 198</p> <p>Perspective #1: Focus on the Business Issue and the Client Benefits 207</p> <p>Perspective #2: Map Out the Ring of Knowledge 214</p> <p>Perspective #3: Choose Data Wisely Based on the Issue Tree 218</p> <p>Perspective #4: The Effi cient Frontier Where Machines Support Minds 226</p> <p>Perspective #5: The Right Mix of Minds Means a World of Good Options 232</p> <p>Perspective #6: The Right Workfl ow: Flexible Platforms Embedded in the Process 241</p> <p>Perspective #7: Serving the End Users Well: Figuring Out the Last Mile 245</p> <p>Perspective #8: The Right User Interaction: The Art of User Experience 250</p> <p>Perspective #9: Integrated Knowledge Management Means Speed and Savings 257</p> <p>Perspective #10: The Commercial Model: Pay-as-You-Go or Per-Unit Pricing 264</p> <p>Perspective #11: Intellectual Property: Knowledge Objects for Mind+Machine 266</p> <p>Perspective #12: Create an Audit Trail and Manage Risk 269</p> <p>Perspective #13: The Right Psychology: Getting the Minds to Work Together 271</p> <p>Perspective #14: The Governance of Use Case Portfolios: Control and ROI 274</p> <p>Perspective #15: Trading and Sharing Use Cases, Even across Company Boundaries 279</p> <p>Part III: Conclusion 281</p> <p>Notes 283</p> <p>About the Author 287</p> <p>Index 289</p>

<p><b>MARC VOLLENWEIDER</b> is co-founder and CEO of Evalueserve, an industry- influencing global research, analytics, and data management solutions provider known for its mind+machine™ process that combines a global network of expert analysts and best-in-class technology. The McKinsey & Co. alum has extensive consulting experience in such industries as telecommunications, banking, and pharmaceuticals.

<p>Praise for <b>mind+machine</b> <p><i>"Mind+Machine </i>is a thought-provoking exploration of data analytics as they stand now. Marc introduces and develops several game-changing concepts, like the Efficient Frontier, Ring of Knowledge, and Use Case Methodology, showing how to apply them for the best possible return on investment. These ideas should be the cornerstone of every organization's approach to data analytics. Written for the generalist with plenty of real-world examples, it's a fascinating read that I couldn't put down."<br/> <b>—Stefan Arn,</b> Global Head of Technology for UBS Wealth Management and UBS Switzerland and Group IT Head for Strategic Regulatory Initiatives <p><i>"Mind+Machine</i> provides a much needed antidote for the business leader looking for actionable business insights in a world of information and technology overload. Based on his credentials as adviser to many of the world's top banks, corporations, and professional services firms, as well founder of two industry-disrupting businesses, Marc has a unique perspective on how to combine human expertise with technology to deliver high ROI business results. He simplifies a complex subject, and his approach is supported by over 40 accessible and insightful case studies. A compelling and accessible guide for decision makers and business leaders seeking sustainable competitive advantage in their markets."<br/> <b>—Mark Bidwell,</b> founder of The Innovation Ecosystem, chairman of BC Platforms and director at Terramera <p>"A very interesting read. It demystifies data analytics with lots of real-world examples on use-cases, best practices and insights from industry. It also offers refreshing business angles of examining the value of analytics in terms of ROI and 'so-what'. Though it is targeted at top managers and decision makers, I find it to be a good read for technical staff, researchers and data analysts. I will use it as part of my research into big data and social media analytics."<br/> <b>—Professor Tat-Seng Chua,</b> KITHCT Chair Professor at the School of Computing, National University of Singapore

US