Mind+Machine

Оглавление

Vollenweider Marc. Mind+Machine

PREFACE

ACKNOWLEDGMENTS

PART I. THE TOP 12 FALLACIES ABOUT MIND+MACHINE

FALLACY #1. BIG DATA SOLVES EVERYTHING

FALLACY #2. MORE DATA MEANS MORE INSIGHT

FALLACY #3. FIRST, WE NEED A DATA LAKE AND TOOLS

FALLACY #4. ANALYTICS IS JUST AN ANALYTICS CHALLENGE. PART I: THE LAST MILE

FALLACY #5. ANALYTICS IS JUST AN ANALYTICS CHALLENGE. PART II: THE ORGANIZATION

FALLACY #6. REORGANIZATIONS WON'T HURT ANALYTICS

FALLACY #7. KNOWLEDGE MANAGEMENT IS EASY – WE JUST NEED SOME WIKIS

FALLACY #8. INTELLIGENT MACHINES CAN SOLVE ANY ANALYTIC PROBLEM

FALLACY #9. EVERYTHING MUST BE DONE IN-HOUSE!

FALLACY #10. WE NEED MORE, LARGER, AND FANCIER REPORTS

FALLACY #11. ANALYTICS INVESTMENT MEANS GREAT ROI

FALLACY #12. ANALYTICS IS A RATIONAL PROCESS

PART I. CONCLUSION

PART II. 13 TRENDS CREATING MASSIVE OPPORTUNITIES FOR MIND+MACHINE

TREND #1. THE ASTEROID IMPACT OF CLOUD AND MOBILE

TREND #2. THE YIN AND YANG OF THE INTERNET OF THINGS

TREND #3. ONE-TO-ONE MARKETING

TREND #4. REGULATORY FLOODING OF THE RING OF KNOWLEDGE

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

The Teeth of the General Data Protection Regulation

Privacy Impacting the Ring of Knowledge

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

TREND #5. THE SEISMIC SHIFT TO PAY-AS-YOU-GO OR OUTPUT-BASED COMMERCIAL MODELS

TREND #6. THE HIDDEN TREASURES OF MULTIPLE-CLIENT UTILITIES

TREND #7. THE RACE FOR DATA ASSETS, ALTERNATIVE DATA, AND SMART DATA

TREND #8. MARKETPLACES AND THE SHARING ECONOMY FINALLY ARRIVING IN DATA AND ANALYTICS

TREND #9. KNOWLEDGE MANAGEMENT 2.0 – STILL AN ELUSIVE VISION?

TREND #10. WORKFLOW PLATFORMS AND PROCESS AUTOMATION FOR ANALYTICS USE CASES

TREND #11. 2015–2025: THE RISE OF THE MIND–MACHINE INTERFACE

TREND #12. AGILE, AGILE, AGILE

TREND #13 (MIND+MACHINE)2 = GLOBAL PARTNERING EQUALS MORE THAN 1+1

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

Era 2: Globalizing Outsourcing (2005–2015)

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

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

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

Pricing and Performance Benchmarks

The Future of Outsourcing in Knowledge-Intensive Processes

PART II. CONCLUSION

PART III. HOW TO IMPLEMENT THE MIND+MACHINE APPROACHE

PERSPECTIVE #1. FOCUS ON THE BUSINESS ISSUE AND THE CLIENT BENEFITS

PERSPECTIVE #2. MAP OUT THE RING OF KNOWLEDGE

PERSPECTIVE #3. CHOOSE DATA WISELY BASED ON THE ISSUE TREE

PERSPECTIVE #4. THE EFFICIENT FRONTIER WHERE MACHINES SUPPORT MINDS

PERSPECTIVE #5. THE RIGHT MIX OF MINDS MEANS A WORLD OF GOOD OPTIONS

PERSPECTIVE #6. THE RIGHT WORKFLOW: FLEXIBLE PLATFORMS EMBEDDED IN THE PROCESS

PERSPECTIVE #7. SERVING THE END USERS WELL: FIGURING OUT THE LAST MILE

PERSPECTIVE #8. THE RIGHT USER INTERACTION: THE ART OF USER EXPERIENCE

PERSPECTIVE #9. INTEGRATED KNOWLEDGE MANAGEMENT MEANS SPEED AND SAVINGS

PERSPECTIVE #10. THE COMMERCIAL MODEL: PAY-AS-YOU-GO OR PER-UNIT PRICING

PERSPECTIVE #11. INTELLECTUAL PROPERTY: KNOWLEDGE OBJECTS FOR MIND+MACHINE

PERSPECTIVE #12. CREATE AN AUDIT TRAIL AND MANAGE RISK

PERSPECTIVE #13. THE RIGHT PSYCHOLOGY: GETTING THE MINDS TO WORK TOGETHER

PERSPECTIVE #14. THE GOVERNANCE OF USE CASE PORTFOLIOS: CONTROL AND ROI

PERSPECTIVE #15. TRADING AND SHARING USE CASES, EVEN ACROSS COMPANY BOUNDARIES

PART III. CONCLUSION

ABOUT THE AUTHOR

INDEX

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Thank you for buying this book.

In 2015, after 15 years of operations in the field of research and analytics, we decided to adopt the notion of mind+machine at Evalueserve. We believe this marriage of the perceptive power of the human brain with the benefits of automation is essential because neither mind nor machine alone will be able to handle the complexities of analytics in the future.

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Based on hundreds of client interactions at Evalueserve and with my former colleagues in the strategy consulting world, it became increasingly clear that there is a strong unmet need in the general managerial population for a simplified framework to enable efficient and effective navigation of information-heavy decision-support processes. Simplicity should always win over complex and nontransparent processes – the analytics space is no exception.

I want to demystify analytics. I'll start with the fundamental observation that terms such as big data and artificial intelligence are getting so much attention in the media that the bricks-and-mortar topics of everyday analytics aren't getting the attention they deserve: topics such as problem definition, data gathering, cleansing, analysis, visualization, dissemination, and knowledge management. Applying big data to every analytics problem would be like taking one highly refined chef's tool – a finely balanced sushi knife, for example – and trying to use it for every task. While very useful big data use cases have emerged in several fields, they represent maybe 5 percent of all of the billions of analytics use cases.

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