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Mind+Machine: A Decision Model for Optimizing and Implementing Analytics
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 · 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.
320 pages, Hardcover
Published November 7, 2016
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Displaying 1 - 3 of 3 reviews
October 9, 2017
Having read other books of a very similar primary title, on the face of it, the contents were not what I expected, as with previous similarly entitled books, the subject matter related to discussing human cognition and artificial intelligence (AI). This book is about Big Data and Analytics, as indicated by the sub-title.
mind+machine actually refers to a guide for conducting analytics using the mind+machine TM approach. The book is in three parts:
The Top 12 Fallacies about Mind+Machine;
13 Trends Creating Massive Opportunities for Mind+Machine;
How to Implement the Mind+Machine Approach.
The hardback version of the book was far from glossy or in colour and its structure and layout could have been greatly improved to enhance the reader’s experience and the book’s appeal. (The e-book version was not read and for reasons of printing costs, it could be in colour, although the structure is likely to be the same).
Although there are some excellent real-world use case examples and very interesting points given, overall I found it difficult to establish the book’s goals. If the book’s purpose was mainly to guide, then any guide should be clear and simple, and regrettably I didn’t find this to be the case.
The author and book have much to recommend them. The author obviously has a wealth of knowledge and experience, and importantly, the background needed to possibly write a simpler, clearer, less academic paperback guide version of mind+machine.
The book is indexed, but has no separate reference section however, some references are provided under ‘Notes’.
The book is perhaps currently aimed more towards the expert or informed data analytics practitioner than those interested in AI.
Review by Deryn Graham FBCS, Senior Lecturer (Retired)
Originally posted: http://www.bcs.org/content/conWebDoc/...
mind+machine actually refers to a guide for conducting analytics using the mind+machine TM approach. The book is in three parts:
The Top 12 Fallacies about Mind+Machine;
13 Trends Creating Massive Opportunities for Mind+Machine;
How to Implement the Mind+Machine Approach.
The hardback version of the book was far from glossy or in colour and its structure and layout could have been greatly improved to enhance the reader’s experience and the book’s appeal. (The e-book version was not read and for reasons of printing costs, it could be in colour, although the structure is likely to be the same).
Although there are some excellent real-world use case examples and very interesting points given, overall I found it difficult to establish the book’s goals. If the book’s purpose was mainly to guide, then any guide should be clear and simple, and regrettably I didn’t find this to be the case.
The author and book have much to recommend them. The author obviously has a wealth of knowledge and experience, and importantly, the background needed to possibly write a simpler, clearer, less academic paperback guide version of mind+machine.
The book is indexed, but has no separate reference section however, some references are provided under ‘Notes’.
The book is perhaps currently aimed more towards the expert or informed data analytics practitioner than those interested in AI.
Review by Deryn Graham FBCS, Senior Lecturer (Retired)
Originally posted: http://www.bcs.org/content/conWebDoc/...
March 26, 2021
This is a dense book. Not easy to read at once.
I took much time to absorb all the book contents.
But I can certainly say that it is a very good reference on how to implement analytics on corporations.
Being part of Evalueserve, I can see most of the propositions applied to the company culture. As Marc states in the book, it's challenging, even for us to apply to our organizational culture.
I recommend reading once. Or check some of the most interesting chapters.
I took much time to absorb all the book contents.
But I can certainly say that it is a very good reference on how to implement analytics on corporations.
Being part of Evalueserve, I can see most of the propositions applied to the company culture. As Marc states in the book, it's challenging, even for us to apply to our organizational culture.
I recommend reading once. Or check some of the most interesting chapters.
April 24, 2017
a Little dense and too much self promotion but otherwise a very good book
Displaying 1 - 3 of 3 reviews