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Mind+Machine: A Decision Model for Optimizing and Implementing Analytics

ISBN: 978-1-119-30291-9
320 pages
November 2016
Mind+Machine: A Decision Model for Optimizing and Implementing Analytics (1119302919) cover image

Description

Cut through information overload to make better decisions faster

Success relies on making the correct decisions at the appropriate time, which is only possible if the decision maker has the necessary insights in a suitable format. Mind+Machine is the guide to getting the right insights in the right format at the right time to the right person. Designed to show decision makers how to get the most out of every level of data analytics, this book explores the extraordinary potential to be found in a model where human ingenuity and skill are supported with cutting-edge tools, including automations.

The marriage of the perceptive power of the human brain with the benefits of automation is essential because mind or machine alone cannot handle the complexities of modern analytics. Only when the two come together with structure and purpose to solve a problem are goals achieved.

With various stakeholders in data analytics having their own take on what is important, it can be challenging for a business leader to create such a structure. This book provides a blueprint for decision makers, helping them ask the right questions, understand the answers, and ensure an approach to analytics that properly supports organizational growth.

Discover how to:

  • Harness the power of insightful minds and the speed of analytics technology
  • Understand the demands and claims of various analytics stakeholders
  • Focus on the right data and automate the right processes
·         Navigate decisions with confidence in a fast-paced world

The Mind+Machine model streamlines analytics workflows and refines the never-ending flood of incoming data into useful insights. Thus, Mind+Machine equips you to take on the big decisions and win.

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Table of Contents

Preface xi

Acknowledgments xvii

List of Use Cases xix

Part I: The Top 12 Fallacies about Mind+Machine 1

Fallacy #1: Big Data Solves Everything 3

Fallacy #2: More Data Means More Insight 17

Fallacy #3: First, We Need a Data Lake and Tools 26

Fallacy #4: Analytics Is Just an Analytics Challenge: Part I: The Last Mile 31

Fallacy #5: Analytics Is Just an Analytics Challenge: Part II: The Organization 36

Fallacy #6: Reorganizations Won’t Hurt Analytics 40

Fallacy #7: Knowledge Management Is Easy—We Just Need Some Wikis 45

Fallacy #8: Intelligent Machines Can Solve Any Analytic Problem 49

Fallacy #9: Everything Must Be Done In-House! 61

Fallacy #10: We Need More, Larger, and Fancier Reports 66

Fallacy #11: Analytics Investment Means Great ROI 72

Fallacy #12: Analytics Is a Rational Process 78

Part I: Conclusion 82

Part II: 13 Trends Creating Massive Opportunities for Mind+Machine 85

Trend #1: The Asteroid Impact of Cloud and Mobile 87

Trend #2: The Yin and Yang of the Internet of Things 96

Trend #3: One-to-One Marketing 105

Trend #4: Regulatory Flooding of the Ring of Knowledge 111

The European Union and Privacy Rules: The General Data Protection Regulation and the EU–US Privacy Shield 114

The Teeth of the General Data Protection Regulation 115

Privacy Impacting the Ring of Knowledge 120

The Nine Questions You Need to Ask Your CIO Regarding Personal Data 121

Trend #5: The Seismic Shift to Pay-as-You-Go or Output-Based Commercial Models 123

Trend #6: The Hidden Treasures of Multiple-Client Utilities 133

Trend #7: The Race for Data Assets, Alternative Data, and Smart Data 136

Trend #8: Marketplaces and the Sharing Economy Finally Arriving in Data and Analytics 144

Trend #9: Knowledge Management 2.0—Still an Elusive Vision? 147

Trend #10: Workfl ow Platforms and Process Automation for Analytics Use Cases 156

Trend #11: 2015–2025: The Rise of the Mind–Machine Interface 164

Trend #12: Agile, Agile, Agile 172

Trend #13: (Mind+Machine)2 = Global Partnering Equals More Than 1+1 177

Era 1: Pure Geographic Cost Arbitrage (2000–2005) 178

Era 2: Globalizing Outsourcing (2005–2015) 180

Era 3: Process Reengineering (2007–2015) and Specialization 184

Era 4: Hybrid On-Site, Near-Shore, and Far-Shore Outsourcing (2010–) 184

Era 5: Mind+Machine in Outsourcing (2010–) 185

Pricing and Performance Benchmarks 190

The Future of Outsourcing in Knowledge-Intensive Processes 194

Part II: Conclusion 196

Part III: How to Implement the Mind+Machine Approach 197

The Analytics Use Case Methodology: A Change in Mind-Set 198

Perspective #1: Focus on the Business Issue and the Client Benefits 207

Perspective #2: Map Out the Ring of Knowledge 214

Perspective #3: Choose Data Wisely Based on the Issue Tree 218

Perspective #4: The Effi cient Frontier Where Machines Support Minds 226

Perspective #5: The Right Mix of Minds Means a World of Good Options 232

Perspective #6: The Right Workfl ow: Flexible Platforms Embedded in the Process 241

Perspective #7: Serving the End Users Well: Figuring Out the Last Mile 245

Perspective #8: The Right User Interaction: The Art of User Experience 250

Perspective #9: Integrated Knowledge Management Means Speed and Savings 257

Perspective #10: The Commercial Model: Pay-as-You-Go or Per-Unit Pricing 264

Perspective #11: Intellectual Property: Knowledge Objects for Mind+Machine 266

Perspective #12: Create an Audit Trail and Manage Risk 269

Perspective #13: The Right Psychology: Getting the Minds to Work Together 271

Perspective #14: The Governance of Use Case Portfolios: Control and ROI 274

Perspective #15: Trading and Sharing Use Cases, Even across Company Boundaries 279

Part III: Conclusion 281

Notes 283

About the Author 287

Index 289

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Author Information

MARC VOLLENWEIDER 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.

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