From Big Data to Big Profits_ Success with Data and Analytics

313 Pages·2015·2.59 MB·English

by  Russell Walker

from big data to big profits
Success with Data and Analytics

From Big Data to Big Profits
SUCCESS WITH DATA AND ANALYTICS
Russell Walker
1
1
Oxford University Press is a department of the University of Oxford.
It furthers the University’s objective of excellence in research, scholarship,
and education by publishing worldwide. Oxford is a registered trade mark
of Oxford University Press in the UK and in certain other countries
Published in the United States of America by
Oxford University Press
198 Madison Avenue, New York, NY 10016,
United States of America
© Oxford University Press 2015
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or
transmitted, in any form or by any means, without the prior permission in writing of Oxford University
Press, or as expressly permitted by law, by license, or under terms agreed with the appropriate
reproduction rights organization. Inquiries concerning reproduction outside the scope of the above should
be sent to the Rights Department, Oxford University Press, at the address above.
You must not circulate this work in any other form
and you must impose this same condition on any acquirer
Cataloging-in-Publication data is on file with the Library of Congress
9780199378326
9 8 7 6 5 4 3 2 1
Printed in the United States of America on acid-free paper
This book is dedicated to my family—Anna, Raymond, and Natalie

Contents
Foreword xiii
Preface xvii
Acknowledgments xix
Introduction xxi
Definitions of Concepts and Terms Used Widely in the Book xxv
Book Overview xxvi
part one | examining big data and its value to firms
1. What is “Big Data”? 3
Scale: How Big Is Big? How Big Will It Become? 6
Data Creation: A Measure of How Fast Data Is Generated 7
Data Storage: A Measure of Scale and the Data We Keep 9
Data Processing: A Measure of How Much Data We Use 10
Data Consumption: A Measure of Our Demand for Data 11
Implications of Scale in Big Data 15
Exploratory Data Analysis: Considering All of the Data 16
Data Organization and Metadata 19
Variety: Using More than Numerical Data 20
Velocity: Leveraging Data within Its Window of Opportunity 21
Viral Distribution of Data: Social Networks Take Front Stage 23
Availability of Data Alters Decisions for the Better 26
Where Is Big Data Being Created? 27
Customer Data: External Data 28
Operations: Internal Data 30
Knowledge Sets: Internal Data 32
Mass Markets: External Data 33
vii
viii Contents
2. Benefits of Scale and Velocity in Big Data: The Movement to Now! 35
Overcoming Complexity through Scale in Big Data 35
Yelp and TripAdvisor: Case Studies in the Creation of Value through
Big Data 36
Scale 37
Organization and Metadata 37
Data Variety 38
Data Velocity 38
Data Availability 38
Value of Information: Risk Reduction 38
Data Velocity Is the New Normal 40
Automated Data Creation: A Necessary Byproduct of Scale
and Velocity 42
Human Interactions with the Internet of Things: Wearable Devices 45
Mastering Velocity and Scale: Creating Advantages with Big Data 47
Increasing Data Velocity 47
Increasing Data Scale 48
Merging High Velocity and High Scale in Data 50
Merging High Velocity and High Scale at Amazon 51
Merging High Velocity and High Scale in Advertising 54
Getting to High Return on Big Data 55
Success in a High Velocity and High Precision Data Environment 58
3. Big Data Expands with Passive Data Capture 61
Active Data Capture 61
Example of Passive Data Capture at Work 62
Passive Data Capture 63
Mobile Platforms Expand Passive Data Capture 65
What Variables Can Be Passively Captured with Smartphones Today? 66
Mobile Apps Perform Passive Data Capture Too 67
Passive Data Capture Will Change the Driving Experience 68
Passive Data Capture Adds Value to Agriculture 68
Valuable Features of Passive Data Capture 69
Passive Data Capture Is in the Home of the Future 70
Passive Data Capture Is Transforming Health Care 71
Trade-offs Are Inevitable When Passive Data Capture Is Collected and
Leveraged 72
Passive Data Capture Raises Privacy Concerns 73
Contents ix
4. Novel Measures in Market Activity: Direct vs. Indirect Measurement 75
Direct Measurement by Active Data Capture 76
From Micro to Macro 77
Indirect Measurement by Passive Data Capture 79
Measurement of Assets by Leveraging Big Data and Data Inverting 81
What’s a Billboard Worth—Exactly? 81
Inverting Data 82
Media Measurement by Third Parties 83
Measurement of Health Care Providers 84
Considerations in the Use of Direct and Indirect Asset Measurements 85
5. Precision in Data: New Possibilities for Mass Customization and Location-Based
Services 86
New Sensors and Mobile Phone Systems Enable Precision in Location-Based
Data Capture 86
Social Networks Enable Measuring the Previously Immeasurable 87
Precision in Measuring Human Performance Is Here Now 88
Precision Agriculture Is Changing Decision-Making in Powerful Ways 89
Precision Medicine and Genomics Enable Personalized Care 90
High Precision in Customer Data Leads to Mass Customization 91
Digital Platforms Enable Increased Precision in Data Capture 92
Precision in Data Is Critical to Unraveling Complexity 93
6. Data Fusion: Combining Data to Produce Economic Value 95
Data Availability in the Real Estate Industry 96
Zillow: A Real Estate Innovator 97
History of Zillow: Data Opens Opportunities 98
Zillow Focuses on Data Fusion and Data Productization 100
Zillow’s Data Product Innovations 101
Make Me Move 102
Mortgage Marketplace 102
Zillow Digs 103
Zillow Data 103
Mobile 104
Success with Data Breeds Competition and Innovation 105
Data Comes in All Forms 107
Lessons from Zillow 108
Mint.com Transforms Personal Finance 110