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E-raamat: Big Data Revolution: What farmers, doctors and insurance agents teach us about discovering big data patterns

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  • Ilmumisaeg: 05-Jan-2015
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118943724
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 05-Jan-2015
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118943724
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A guide to the effective use of big data to improve business performance covers such topics as examining data patterns to recognize future patterns as they emerge and developing governance and security strategy for data use.

Exploit the power and potential of Big Data to revolutionize business outcomes

Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist.

Companies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology.

  • Examine the major emerging patterns in Big Data
  • Consider the debate surrounding the ethical use of data
  • Recognize patterns and improve personal and organizational performance
  • Make more informed decisions with quantifiable results

In an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.

Prologue 1(6)
Berkeley, 1930s
1(1)
Pattern Recognition
2(3)
Nelson Peltz
3(2)
Committing to One Percent
5(1)
The Big Data Revolution
6(1)
Introduction 7(8)
Storytelling
7(1)
Objective
7(1)
Outline
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)
Storytelling (Continued)
13(2)
Part I: The Revolution Starts Now: 9 Industries Transforming With Data 15(116)
Chapter 1 Transforming Farms with Data
17(14)
California, 2013
17(1)
Brief History of Farming
18(1)
The Data Era
19(5)
Potato Farming
20(1)
Precision Farming
21(1)
Capturing Farm Data
22(2)
Deere & Company Versus Monsanto
24(2)
Integrated Farming Systems
25(1)
Data Prevails
26(1)
The Climate Corporation
26(1)
Growsafe Systems
27(1)
Farm of the Future
27(2)
California, 2013 (Continued)
29(2)
Chapter 2 Why Doctors Will Have Math Degrees
31(14)
United States, 2014
31(1)
The History of Medical Education
32(2)
Scientific Method
32(1)
Rise of Specialists
33(1)
We Have a Problem
34(2)
Ben Goldacre
35(1)
Vinod Khosla
35(1)
The Data Era
36(6)
Collecting Data
36(2)
Telemedicine
38(2)
Innovating with Data
40(2)
Implications of a Data-Driven Medical World
42(1)
The Future of Medical School
42(2)
A Typical Medical School
42(1)
A Medical School for the Data Era
43(1)
United States, 2030
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)
Compound Interest
50(1)
Probability
50(1)
Mortality Data
50(1)
Modern-Day Insurance
51(1)
Eight Weeks to Eight Days
51(1)
Online Policies
52(1)
The Data Era
52(6)
Dynamic Risk Management
52(2)
Catastrophe Risk
54(1)
Open Access Modeling
55(1)
Opportunities
56(2)
Middle of Somewhere, 2012 (Continued)
58(1)
Chapter 4 Personalizing Retail and Fashion
59(10)
Karolina
59(1)
A Brief History of Retail
60(3)
Retail Eras
60(1)
Aristide Boucicaut
61(1)
The Shift
62(1)
The Data Era
63(4)
Stitch Fix
63(2)
Keaton Row
65(1)
Zara
66(1)
Karolina (Continued)
67(2)
Chapter 5 Transforming Customer Relationships with Data
69(10)
Buying a House
69(1)
Brief History of Customer Service
70(5)
Customer Service Over Time
70(2)
Boeing
72(2)
Financial Services
74(1)
The Data Era
75(2)
An Automobile Manufacturer
76(1)
Zendesk
76(1)
Buying a House (Continued)
77(2)
Chapter 6 Intelligent Machines
79(10)
Denmark
79(1)
Intelligent Machines
80(2)
Machine Data
81(1)
The Data Era
82(5)
General Electric
82(2)
Drones
84(2)
Tesla
86(1)
Networks of Data
87(1)
Denmark (Continued)
88(1)
Chapter 7 Government and Society
89(18)
Egypt, 2011
89(1)
Social Media
90(1)
Intelligence
90(6)
Snowden Effect
91(1)
Privacy Risk Versus Reward
91(2)
Observation or Surveillance
93(1)
Development Targets
93(2)
Open Data
95(1)
Hackathons
95(1)
Open Access
95(1)
Ensuring Personal Protection
96(1)
Private Clouds
97(1)
Sanitizing Data
97(1)
Evidence-Based Policy
97(1)
Public-Private Partnerships
98(3)
Impact Bonds
101(4)
Social Impact Bond
102(1)
Development Impact Bonds
103(1)
The Role of Big Data
104(1)
Egypt, 2011 (Continued)
105(2)
Chapter 8 Corporate Sustainability
107(12)
City of London
107(2)
Global Megaforces
109(2)
Population
109(1)
Carbon Footprint
110(1)
Water Scarcity
110(1)
Environmental Risk
111(1)
BP and Exxon Mobile
111(1)
Early Warning Systems
112(2)
Social Media
113(1)
Risk and Resilience
114(1)
Measuring Sustainability
115(1)
Long-Term Decision Making
116(1)
Stranded Assets
117(1)
City of London (Continued)
118(1)
Chapter 9 Weather and Energy
119(12)
India, 2012
119(1)
The Weather
120(1)
Forecasting the Weather
120(4)
When are Weather Forecasts Wrong?
121(1)
Chaos
122(1)
Ensemble Forecasts
122(1)
Communication
123(1)
Renewable Energy
124(2)
Solar, Hydro, and Wind Power
124(1)
Volatile or Intermittent Supply
125(1)
Energy Consumption
126(3)
Smart Meters
127(1)
Intelligent Demand-Side Management
128(1)
India, 2012 (Continued)
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)
Bayes' Theorem
135(1)
Tsukiji Market
135(2)
Pattern Recognition
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)
Meatpacking District
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)
Data as the Product
145(6)
Dun & Bradstreet
146(2)
CoStar
148(1)
IHS
149(2)
Meatpacking District (Continued)
151(2)
Chapter 12 Patterns in Big Data
153(18)
The Data Factor
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)
New Product Offerings
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)
Data Security
165(1)
New Partnerships Founded on Data
165(1)
Shortening the Innovation Lifecycle
166(1)
Defining New Channels to Market
166(1)
New Economic Models
167(1)
Forecasting and Predicting Future Events
168(1)
Changing Incentives
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)
Bain Study
175(1)
Seizing the Opportunity
176(1)
Chapter 14 Porsche
177(4)
Rome
177(1)
Ferdinand Porsche
178(1)
The Birth of Porsche
178(1)
The Porsche Sports Car
179(1)
Porsche Today
180(1)
Rome (Continued)
180(1)
Chapter 15 Puma
181(4)
Herzogenaurach
181(1)
Advertising Wars
182(1)
Jochen Zeitz
182(1)
Environmental Profit and Loss
183(1)
Herzogenaurach (Continued)
184(1)
Chapter 16 A Methodology for Applying Big Data Patterns
185(20)
Introduction
185(1)
The Method
186(1)
Step 1: Understand Data Assets
187(4)
The Patterns
188(3)
Step 2: Explore Data
191(3)
Challenges
192(1)
Questions
192(1)
Hypotheses
193(1)
Data
193(1)
Models
193(1)
Statistical Significance
194(1)
Step 3: Design the Future
194(3)
The Patterns
195(2)
Step 4: Design a Data-Driven Business Model
197(2)
The Patterns
197(2)
Step 5: Transform Business Processes for the Data Era
199(2)
The Patterns
199(2)
Step 6: Design for Governance and Security
201(1)
The Patterns
201(1)
Step 7: Share Metrics and Incentives
202(3)
Chapter 17 Big Data Architecture
205(10)
Introduction
205(1)
Architect for the Future
206(1)
Lessons from Stuttgart
207(1)
Big Data Reference Architectures
207(1)
Leveraging Investments in Architecture
208(3)
Big Data Reference Architectures
211(4)
Business View
212(1)
Logical View
213(2)
Chapter 18 Business View Reference Architecture
215(8)
Introduction
215(1)
Men's Trunk: A Retailer in the Data Era
216(1)
The Business View Reference Architecture
217(5)
Answer Fabric
218(1)
Data Virtualization
219(1)
Data Engines
220(1)
Management
221(1)
Data Governance
221(1)
User Interface, Applications, and Business Processes
222(1)
Summary
222(1)
Chapter 19 Logical View Reference Architecture
223(10)
Introduction
223(1)
Men's Trunk: A Retailer in the Data Era (Continued)
224(2)
The Logical View Reference Architecture
226(1)
Data Ingest
227(1)
Analytics
227(3)
Discovery
228(1)
Landing
228(1)
Operational Warehouse
229(1)
Information Insight
230(1)
Operational Data
231(1)
Governance
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)
Step 2: Explore the Data
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)
Answer Fabric
240(1)
Data Virtualization
241(1)
Data Engines
241(1)
Management
242(1)
Data Governance
242(1)
User Interface, Applications, and Business Processes
243(1)
Men's Trunk: The Logical View Reference Architecture
244(4)
Approach
244(4)
Men's Trunk: A Retailer in the Data Era (Continued)
248(1)
Epilogue 249(6)
The Time is Now
249(1)
Taking Action
250(1)
Fear not Usual Competitors
251(1)
The Future
252(3)
Index 255
Rob Thomas is Vice President of Product Development for Big Data and Information Management in IBM Software Group. Previously, he had responsibility for global sales and mergers & acquisitions. Patrick McSharry is a Senior Research Fellow at the Smith School of Enterprise and the Environment, Faculty Member of the Oxford Man Institute of Quantitative Finance at Oxford University and Visiting Professor at the Department of Electrical and Computer Engineering, Carnegie Mellon University.