Smarter Data Science. Cole Stryker

Science” to explain why we're underachieving on the promise of AI. Full of practical ideas for improving the practice of information architecture for modern analytical environments using AI or ML. Highly recommended.

      Linda Nadeau, Information Architect, Metaphor Consulting LLC

      In this book, the authors “unpack” the meaning of data as a natural resource for the modern corporation. Following on Neal's previous book that explored the role of data in enterprise transformation, the authors construct and lead the reader through a holistic approach to drive business value with data science. This book examines data, analytics, and the AI value chain across several industries describing specific use and business cases. This book is a must read for Chief Data Officers as well as accomplished or inspiring data scientists in any industry.

      Boris Vishnevsky, Principal, Complex Solutions and Cyber Security, Slalom; Adjunct Professor, TJU

As an architect working with clients on highly complex projects, all of my new projects involve vast amounts of data, distributed sources of data, cloud-based technologies, and data science. This book is invaluable for my real-world enterprise scale practice. The anticipated risks, complexities, and the rewards of infusing AI is laid out in a well-organized manner that is easy to comprehend taking the reader out of the scholastic endeavor of fact-based learning and into the real world of data science. I would highly recommend this book to anyone wanting to be meaningfully involved with data science.

      John Aviles, Federal CTO Technical Lead, IBM

      I hold over 150 patents and work as a data scientist on creating some of the most complex AI business projects, and this book has been of immense value to me as a field guide. The authors have established the need as to why IA must be part of a systematic maturing approach to AI. I regard this book as a “next generation AI guidebook” that your organization can't afford to be without.

      Gandhi Sivakumar, Chief Architect and Master Inventor, IBM (Australia)

      A seminal treatment for how enterprises must leverage AI. The authors provide a clear and understandable path forward for using AI across cloud, fog, and mist computing. A must read for any serious data scientist and data manager.

      Raul Shneir, Director, Israel National Cyber Directorate (Israel)

      As a professor at Wharton who teaches data science I often mention to my students about emerging new analytical tools such as AI that can provide valuable information to business decision makers. I also encourage them to keep abreast of such tools. Smarter Data Science will definitely make my recommended readings list. It articulates clearly how an organization can build a successful Information architecture, capitalizing on AI technologies benefits. The authors have captured many intricate themes that are relevant for my students to carry with them into the business world. Many of the ideas presented in this book will benefit those working directly in the field of data science or those that will be impacted by data science. The book also includes many critical thinking tools to ready the worker of tomorrow … and realistically, today.

      Dr. Josh Eliashberg, Sebastian S. Kresge Professor of Marketing, Professor of Operations, Information, and Decisions, The Wharton School

      This is an excellent guide for the data-driven organization that must build a robust information architecture to continuously deliver greater value through data science or be relegated to the past. The book will enable organizations to complete their transformative journey to sustainably leverage AI technologies that incorporate cloud-based AI tools and dueling neural networks. The guiding principles that are laid out in the book should result in the democratization of data, a data literate workforce, and a transparent AI revolution.

      Taarini Gupta, Behavioral Scientist/Data Scientist, Mind Genomics Advisors

Smarter Data Science

      Succeeding with Enterprise-Grade Data and AI Projects

      Neal Fishman with Cole Stryker

      Copyright © 2020 by John Wiley & Sons, Inc., Indianapolis, Indiana

      Published simultaneously in Canada

      ISBN: 978-1-119-69341-3

      ISBN: 978-1-119-69438-0 (ebk)

      ISBN: 978-1-119-69342-0 (ebk)

      Manufactured in the United States of America

      No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone]. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at www.wiley.com/go/permissions.

      Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites listed in this work may have changed or disappeared between when this work was written and when it is read.

      For general information on our other products and services please contact our Customer Care Department within the United States at [phone], outside the United States at [phone] or fax [phone].

      Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

       Library of Congress Control Number: 2020933636

      Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

       ATM4

About the Authors

      Neal Fishman is an IBM Distinguished Engineer and is the CTO for Data-Based Pathology within IBM's Global Business Services organization. Neal is also an Open Group Certified Distinguished IT Architect. Neal has extensive experience working with IBM's clients across six continents on complex data and AI initiatives.

      Neal