Mind+Machine. Vollenweider Marc

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      Marc Vollenweider

      mind+machine

      mind+machine

      A Decision Model for Optimizing and Implementing Analytics

      Marc Vollenweider

      Cover image: © iStock.com/sumkinn

      Cover design: Wiley

      Copyright © 2017 by Evaluserve Ltd. All rights reserved.

      Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

      Published simultaneously in Canada

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      ISBN: 9781119302919 (cloth); ISBN 9781119302926 (ebk); ISBN 9781119302971 (ebk)

To my wife Gabi and our children Michèle, Alexandra, Eric, and Mike

      PREFACE

      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.

      The editorial team at John Wiley & Sons approached me in November 2015 to ask if I would like to write a book on how our mind+machine approach could help with the management of information-heavy processes – a topic that is of increasing interest to companies worldwide. We got very positive feedback from clients, friends, and colleagues on the idea, and decided to go ahead.

      Mind+Machine is for generalist mainstream middle and top managers in business functions such as sales, marketing, procurement, R&D, supply chain, and corporate support functions, particularly in business-to-business (B2B) and B2C industries. We're writing for the hopeful beneficiaries and end users of analytics, and for people who might need to make decisions about analytics, now or in the future. The book is not a technical text primarily addressed to data scientists – although I firmly believe that even those specialists have something to learn about the primary problem in generating return on investment (ROI) from analytics.

      We won't be looking at super-advanced but rare analytics use cases – there are specialized textbooks for those. Instead, we're looking at the efficient frontier, offering practical help on dealing with the logistics of managing and improving decision-making support and getting positive ROI at the same time.

      After reading this book, you should know about key issues in the value chain of mind+machine in analytics, and be in a position to ask your data scientists, IT specialists, and vendors the right questions. You should understand the options and approaches available to you before you spend millions of dollars on a new proposal. You'll learn some useful things to demystify the world of analytics.

      We're also proposing a novel approach, the Use Case Methodology (UCM), to give you a set of tangible and tested tools to make your life easier.

      We've included 39 detailed case studies and plenty of real-life anecdotes to illustrate the applications of mind+machine. I'm sure you'll recognize some of your own experiences. And you'll see that you're far from alone in your quest to understand analytics.

      What makes me want to put these ideas about the problems and solutions to analytics issues out in the world is conversations like these two.

      The first words to me from a very senior line manager in a B2B corporation:

      “Marc, is this meeting going to be about big data? If so, I'll stop it right here. Vendors are telling me that I need to install a data lake and hire lots of increasingly rare and expensive statisticians and data scientists. My board is telling me that I need to do ‘something' in big data. It all sounds unjustifiably expensive and complex. I just want to make sure that my frontline people are going to get what they need in time. I keep hearing from other companies that after an initial burst of analytics activity, real life caught up with them, the line guys are still complaining about delays, and the CFO is asking a lot of questions about the spend on big data.”

      During a meeting with the COO of an asset manager to define the scope of a project:

      “We do thousands of pitches to pension funds and other institutional investors every year. We have over 25 different data sources with quantitative data and qualitative information, with lots of regional flavors. However, we still put the pitches together manually and get the sign-offs from the legal department by e-mail. There must be a smarter way of doing this.”

      Why is analytics becoming such a controversial and challenging world? Why are managers either daunted by overhyped new initiatives and processes that they don't understand or frustrated by the feeling that there should be a better way to do something, given all this talk about better, bigger, brighter analytics?

      Typical line managers want to get the right decision-making support to the right people at the right time in the right format. The proliferating number of analytics use cases and available data sets is not matched by an expansion in individuals' and companies' capacities to mentally and logistically absorb the information. Additionally, existing and new compliance requirements are piling up at a remarkable speed, especially in industries with a high regulatory focus, such as financial services and health care.

      Analytics itself is not truly the issue. In most cases, the problem is the logistics of getting things done in organizations: defining the workflow and getting it executed efficiently; making decisions on internal alignment, the complexities of getting IT projects done, and other organizational hurdles that hamper the progress. These complexities slow things down or make projects diverge from their original objectives, so that the actual beneficiaries of the analytics (e.g., the key account manager or the procurement manager in the field) don't get what they need in time.

      Many other issues plague the world of analytics: the proliferation of unintuitive jargon about data