Prologue |
|
1 | (6) |
|
|
1 | (1) |
|
|
2 | (3) |
|
|
3 | (2) |
|
Committing to One Percent |
|
|
5 | (1) |
|
|
6 | (1) |
Introduction |
|
7 | (8) |
|
|
7 | (1) |
|
|
7 | (1) |
|
|
8 | (5) |
|
Part I "The Revolution Starts Now: 9 Industries Transforming with Data" |
|
|
8 | (3) |
|
Part II "Learning from Patterns in Big Data" |
|
|
11 | (1) |
|
Part III "Leading the Revolution" |
|
|
11 | (2) |
|
|
13 | (2) |
Part I: The Revolution Starts Now: 9 Industries Transforming With Data |
|
15 | (116) |
|
Chapter 1 Transforming Farms with Data |
|
|
17 | (14) |
|
|
17 | (1) |
|
|
18 | (1) |
|
|
19 | (5) |
|
|
20 | (1) |
|
|
21 | (1) |
|
|
22 | (2) |
|
Deere & Company Versus Monsanto |
|
|
24 | (2) |
|
Integrated Farming Systems |
|
|
25 | (1) |
|
|
26 | (1) |
|
|
26 | (1) |
|
|
27 | (1) |
|
|
27 | (2) |
|
California, 2013 (Continued) |
|
|
29 | (2) |
|
Chapter 2 Why Doctors Will Have Math Degrees |
|
|
31 | (14) |
|
|
31 | (1) |
|
The History of Medical Education |
|
|
32 | (2) |
|
|
32 | (1) |
|
|
33 | (1) |
|
|
34 | (2) |
|
|
35 | (1) |
|
|
35 | (1) |
|
|
36 | (6) |
|
|
36 | (2) |
|
|
38 | (2) |
|
|
40 | (2) |
|
Implications of a Data-Driven Medical World |
|
|
42 | (1) |
|
The Future of Medical School |
|
|
42 | (2) |
|
|
42 | (1) |
|
A Medical School for the Data Era |
|
|
43 | (1) |
|
|
44 | (1) |
|
Chapter 3 Revolutionizing Insurance: Why Actuaries Will Become Data Scientists |
|
|
45 | (14) |
|
Middle of Somewhere, 2012 |
|
|
45 | (1) |
|
Short History of Property & Casualty Insurance and Underwriting |
|
|
46 | (3) |
|
Actuarial Science In Insurance |
|
|
47 | (2) |
|
Pensions, Insurance, Leases |
|
|
49 | (2) |
|
|
50 | (1) |
|
|
50 | (1) |
|
|
50 | (1) |
|
|
51 | (1) |
|
Eight Weeks to Eight Days |
|
|
51 | (1) |
|
|
52 | (1) |
|
|
52 | (6) |
|
|
52 | (2) |
|
|
54 | (1) |
|
|
55 | (1) |
|
|
56 | (2) |
|
Middle of Somewhere, 2012 (Continued) |
|
|
58 | (1) |
|
Chapter 4 Personalizing Retail and Fashion |
|
|
59 | (10) |
|
|
59 | (1) |
|
A Brief History of Retail |
|
|
60 | (3) |
|
|
60 | (1) |
|
|
61 | (1) |
|
|
62 | (1) |
|
|
63 | (4) |
|
|
63 | (2) |
|
|
65 | (1) |
|
|
66 | (1) |
|
|
67 | (2) |
|
Chapter 5 Transforming Customer Relationships with Data |
|
|
69 | (10) |
|
|
69 | (1) |
|
Brief History of Customer Service |
|
|
70 | (5) |
|
Customer Service Over Time |
|
|
70 | (2) |
|
|
72 | (2) |
|
|
74 | (1) |
|
|
75 | (2) |
|
An Automobile Manufacturer |
|
|
76 | (1) |
|
|
76 | (1) |
|
Buying a House (Continued) |
|
|
77 | (2) |
|
Chapter 6 Intelligent Machines |
|
|
79 | (10) |
|
|
79 | (1) |
|
|
80 | (2) |
|
|
81 | (1) |
|
|
82 | (5) |
|
|
82 | (2) |
|
|
84 | (2) |
|
|
86 | (1) |
|
|
87 | (1) |
|
|
88 | (1) |
|
Chapter 7 Government and Society |
|
|
89 | (18) |
|
|
89 | (1) |
|
|
90 | (1) |
|
|
90 | (6) |
|
|
91 | (1) |
|
Privacy Risk Versus Reward |
|
|
91 | (2) |
|
Observation or Surveillance |
|
|
93 | (1) |
|
|
93 | (2) |
|
|
95 | (1) |
|
|
95 | (1) |
|
|
95 | (1) |
|
Ensuring Personal Protection |
|
|
96 | (1) |
|
|
97 | (1) |
|
|
97 | (1) |
|
|
97 | (1) |
|
Public-Private Partnerships |
|
|
98 | (3) |
|
|
101 | (4) |
|
|
102 | (1) |
|
|
103 | (1) |
|
|
104 | (1) |
|
|
105 | (2) |
|
Chapter 8 Corporate Sustainability |
|
|
107 | (12) |
|
|
107 | (2) |
|
|
109 | (2) |
|
|
109 | (1) |
|
|
110 | (1) |
|
|
110 | (1) |
|
|
111 | (1) |
|
|
111 | (1) |
|
|
112 | (2) |
|
|
113 | (1) |
|
|
114 | (1) |
|
|
115 | (1) |
|
Long-Term Decision Making |
|
|
116 | (1) |
|
|
117 | (1) |
|
City of London (Continued) |
|
|
118 | (1) |
|
Chapter 9 Weather and Energy |
|
|
119 | (12) |
|
|
119 | (1) |
|
|
120 | (1) |
|
|
120 | (4) |
|
When are Weather Forecasts Wrong? |
|
|
121 | (1) |
|
|
122 | (1) |
|
|
122 | (1) |
|
|
123 | (1) |
|
|
124 | (2) |
|
Solar, Hydro, and Wind Power |
|
|
124 | (1) |
|
Volatile or Intermittent Supply |
|
|
125 | (1) |
|
|
126 | (3) |
|
|
127 | (1) |
|
Intelligent Demand-Side Management |
|
|
128 | (1) |
|
|
129 | (2) |
Part II: Learning From Patterns In Big Data |
|
131 | (40) |
|
Chapter 10 Pattern Recognition |
|
|
133 | (8) |
|
Elements of Success Rhyme |
|
|
133 | (1) |
|
Pattern Recognition: A Gift or Trap? |
|
|
134 | (1) |
|
What Fish Teach Us About Pattern Recognition |
|
|
135 | (2) |
|
|
135 | (1) |
|
|
135 | (2) |
|
|
137 | (3) |
|
Rochester Institute of Technology |
|
|
137 | (1) |
|
A Method for Recognizing Patterns |
|
|
137 | (3) |
|
Elements of Success Rhyme (Continued) |
|
|
140 | (1) |
|
Chapter 11 Why Patterns in Big Data Have Emerged |
|
|
141 | (12) |
|
|
141 | (1) |
|
Business Models in the Data Era |
|
|
142 | (1) |
|
Data as a Competitive Advantage |
|
|
143 | (2) |
|
Data Improves Existing Products or Services |
|
|
145 | (1) |
|
|
145 | (6) |
|
|
146 | (2) |
|
|
148 | (1) |
|
|
149 | (2) |
|
Meatpacking District (Continued) |
|
|
151 | (2) |
|
Chapter 12 Patterns in Big Data |
|
|
153 | (18) |
|
|
154 | (1) |
|
Summary of Big Data Patterns |
|
|
155 | (15) |
|
Redefining a Skilled Worker |
|
|
155 | (1) |
|
Creating and Utilizing New Sources of Data |
|
|
156 | (1) |
|
Building New Data Applications |
|
|
157 | (1) |
|
Transforming and Creating New Business Processes |
|
|
157 | (1) |
|
Data Collection for Competitive Advantage |
|
|
158 | (1) |
|
Exposing Opinion-Based Biases |
|
|
159 | (1) |
|
Real-Time Monitoring and Decision Making |
|
|
159 | (1) |
|
Social Networks Leveraging and Creating Data |
|
|
160 | (1) |
|
Deconstructing the Value Chain |
|
|
161 | (1) |
|
|
161 | (1) |
|
Building for Customers Instead of Markets |
|
|
162 | (1) |
|
Tradeoff Between Privacy and Insight |
|
|
163 | (1) |
|
Changing the Definition of a Product |
|
|
163 | (1) |
|
Inverting the Search Paradigm for Data Discovery |
|
|
164 | (1) |
|
|
165 | (1) |
|
New Partnerships Founded on Data |
|
|
165 | (1) |
|
Shortening the Innovation Lifecycle |
|
|
166 | (1) |
|
Defining New Channels to Market |
|
|
166 | (1) |
|
|
167 | (1) |
|
Forecasting and Predicting Future Events |
|
|
168 | (1) |
|
|
168 | (1) |
|
New Partnerships (Public/Private) |
|
|
169 | (1) |
|
Real-Time Monitoring and Decision Making (Early Warning Systems) |
|
|
169 | (1) |
|
A Framework for Big Data Patterns |
|
|
170 | (1) |
Part III: Leading The Revolution |
|
171 | (78) |
|
Chapter 13 The Data Opportunity |
|
|
173 | (4) |
|
What Oil Teaches Us About Data |
|
|
173 | (2) |
|
|
175 | (1) |
|
|
176 | (1) |
|
|
177 | (4) |
|
|
177 | (1) |
|
|
178 | (1) |
|
|
178 | (1) |
|
|
179 | (1) |
|
|
180 | (1) |
|
|
180 | (1) |
|
|
181 | (4) |
|
|
181 | (1) |
|
|
182 | (1) |
|
|
182 | (1) |
|
Environmental Profit and Loss |
|
|
183 | (1) |
|
Herzogenaurach (Continued) |
|
|
184 | (1) |
|
Chapter 16 A Methodology for Applying Big Data Patterns |
|
|
185 | (20) |
|
|
185 | (1) |
|
|
186 | (1) |
|
Step 1: Understand Data Assets |
|
|
187 | (4) |
|
|
188 | (3) |
|
|
191 | (3) |
|
|
192 | (1) |
|
|
192 | (1) |
|
|
193 | (1) |
|
|
193 | (1) |
|
|
193 | (1) |
|
|
194 | (1) |
|
Step 3: Design the Future |
|
|
194 | (3) |
|
|
195 | (2) |
|
Step 4: Design a Data-Driven Business Model |
|
|
197 | (2) |
|
|
197 | (2) |
|
Step 5: Transform Business Processes for the Data Era |
|
|
199 | (2) |
|
|
199 | (2) |
|
Step 6: Design for Governance and Security |
|
|
201 | (1) |
|
|
201 | (1) |
|
Step 7: Share Metrics and Incentives |
|
|
202 | (3) |
|
Chapter 17 Big Data Architecture |
|
|
205 | (10) |
|
|
205 | (1) |
|
|
206 | (1) |
|
|
207 | (1) |
|
Big Data Reference Architectures |
|
|
207 | (1) |
|
Leveraging Investments in Architecture |
|
|
208 | (3) |
|
Big Data Reference Architectures |
|
|
211 | (4) |
|
|
212 | (1) |
|
|
213 | (2) |
|
Chapter 18 Business View Reference Architecture |
|
|
215 | (8) |
|
|
215 | (1) |
|
Men's Trunk: A Retailer in the Data Era |
|
|
216 | (1) |
|
The Business View Reference Architecture |
|
|
217 | (5) |
|
|
218 | (1) |
|
|
219 | (1) |
|
|
220 | (1) |
|
|
221 | (1) |
|
|
221 | (1) |
|
User Interface, Applications, and Business Processes |
|
|
222 | (1) |
|
|
222 | (1) |
|
Chapter 19 Logical View Reference Architecture |
|
|
223 | (10) |
|
|
223 | (1) |
|
Men's Trunk: A Retailer in the Data Era (Continued) |
|
|
224 | (2) |
|
The Logical View Reference Architecture |
|
|
226 | (1) |
|
|
227 | (1) |
|
|
227 | (3) |
|
|
228 | (1) |
|
|
228 | (1) |
|
|
229 | (1) |
|
|
230 | (1) |
|
|
231 | (1) |
|
|
231 | (1) |
|
Men's Trunk: A Retailer in the Data Era (Continued) |
|
|
232 | (1) |
|
Chapter 20 The Architecture of the Future |
|
|
233 | (16) |
|
Men's Trunk: A Retailer in the Data Era (Continued) |
|
|
233 | (2) |
|
Men's Trunk: Applying the Methodology |
|
|
235 | (4) |
|
Step 1: Understand Data Assets |
|
|
235 | (1) |
|
|
236 | (1) |
|
Step 3: Design the Future |
|
|
237 | (1) |
|
Step 4: Design a Data-Driven Business Model |
|
|
237 | (1) |
|
Step 5: Transform Business Processes for the Data Era |
|
|
237 | (1) |
|
Step 6: Design for Governance and Security |
|
|
237 | (1) |
|
Step 7: Share Metrics and Incentives |
|
|
238 | (1) |
|
Men's Trunk: The Business View Reference Architecture |
|
|
239 | (5) |
|
|
240 | (1) |
|
|
241 | (1) |
|
|
241 | (1) |
|
|
242 | (1) |
|
|
242 | (1) |
|
User Interface, Applications, and Business Processes |
|
|
243 | (1) |
|
Men's Trunk: The Logical View Reference Architecture |
|
|
244 | (4) |
|
|
244 | (4) |
|
Men's Trunk: A Retailer in the Data Era (Continued) |
|
|
248 | (1) |
Epilogue |
|
249 | (6) |
|
|
249 | (1) |
|
|
250 | (1) |
|
Fear not Usual Competitors |
|
|
251 | (1) |
|
|
252 | (3) |
Index |
|
255 | |