From big data to big profits: success with data and analytics
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Oxford Univ. Press
2015
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXVI, 283 S. graph. Darst. |
| ISBN: | 9780199378326 |
Internformat
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Datensatz im Suchindex
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| adam_text | Titel: From big data to big profits
Autor: Walker, Russell
Jahr: 2015
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
i. 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 zo
Velocity: Leveraging Data within Its Window of Opportunity zi
Viral Distribution of Data: Social Networks Take Front Stage Z3
Availability of Data Alters Decisions for the Better z6
Where Is Big Data Being Created? Z7
Customer Data: External Data z8
Operations: Internal Data 30
Knowledge Sets: Internal Data 3Z
Mass Markets: External Data 33
vii
viii i Contents ....................................................................................................................
Benefits of Scale and Velocity in BigData: 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 6z
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 71
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—Exacdy ? 81
Inverting Data 8z
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 9a
Precision in Data Is Critical to Unraveling Complexity 93
6. Data Fusion: Combining Data to Produce Economic Value 9 s
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 ioz
Mortgage Marketplace ioz
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 no
x ! Contents
Fusing of Data at Mint.com Creates Novel Data Views for Users and Vendors in
Lessons from Mint.com 112.
PART TWO I SUCCESS IN LEVERAGING BIG DATA
7. Strategies for Monetizing Big Data 117
Keep the Data Proprietary 119
Monetization Strategy: Leverage Data for Internal Operations 119
Monetization Strategy: Enter New Business 121
Monetization Strategy: License Data Exclusively 124
Data Strategy: Trade Data to Business Partners for Shared Benefits 127
Monetization Strategy: Trade Data with Downstream Business Partners 127
Data Strategy: Sell the Data Product (to a Host of Possible Clients) 131
Monetization Strategy: Sell Data Products to Asset Owners 133
Monetization Strategy: Sell Data Products to Other Interested Parties 136
Monetization Strategy: Sell Premium Data Product Access 139
Data Strategy: Make the Data Available (and Even Free) to Many Users 141
Monetization Strategy: Leverage User Base for Advertisement
Opportunities 142
Advertisement Strategy for Broad Awareness (Low Precision and Low
Velocity Data) 146
Advertisement Strategy for Time-Sensitive Decisions (High Velocity
in Data) 146
Advertising Strategy for Products or Services Aligned with Customer
(High Precision in Data) 148
Advertising Strategy for Products or Services Aligned with Customer
AND are Time-Sensitive (High Precision, High Velocity in Data) 150
Novel Data Creation in Advertisement on Digital Platforms 152
Origins of the Marketplace 133
Overview of Data Strategies and Monetization Strategies 158
Multi-sided Business Models Form to Monetize Data from Digital Platforms 160
Linkedln.com Creates Big Data 160
Lessons from Linkedln on Multi-sided Business Models 163
8. Monetizing Big Data through Productization and Data Inverting 166
The Origins of Netflix as a Disruptive Innovator 167
Blockbuster: A History 168
Netflix Cultivates Big Data on Customer Preferences 169
Netflix Forms a Data Exchange with Customers 170
Big Data and Analytics Enable Netflix s Success 172
Contents j xi
Data Supporting the Digital Platform Enables Customer Loyalty 172.
Analytics Enable Long-Tail Capture and Aggregation of Demand 173
Data on Movies Changes Relationship with Movie Houses 174
Employee Management Reflects Data Importance 177
The Future of Netflix: Data Wars Have Begun 178
Lessons from Netflix 182
9- Impact of Analytics and Big Data on Corporate Culture and Recruitment 184
The Rise of the Data Scientist 185
A Portrait of a Data Scientist 188
Graduate Programs in Data Science are Available 192
Benefits of Functionally Assigned Analytical Teams 194
Challenges of Functionally Assigned Analytical Teams 195
Benefits of Centralized Analytical Teams 196
A New Organizational Model: Chief Data Scientist 197
Maximizing the Impact of Data Scientists 200
10. Stimulating Innovation through Big Data 202
Leveraging and Re-leveraging Data Dynamically 202
New Data Fuels Innovation 205
Digital Platforms Enable Innovation 207
New Data and Digital Platforms Can Change Markets 207
Innovation in Health Care 209
Innovation through Data Requires a Data Laboratory for Data Creation 210
Nest and Building New Digital Platforms for Innovation 213
Digital Platforms and the Internet of Things Fuel Innovation 215
Stimulating Innovation with Big Data Challenges 219
Experimenting with Data at the Enterprise 221
11. Disrupting Business Models with New Data from Location-Based Services 222
Big Data Possibilities from Cellular Networks 223
Passive vs. Active Data Capture in Location-Based Services 225
Leveraging Location for Data Monetization 227
Location-Based Services 228
Trends in Location-Based Services 228
Foursquare: Using Customer Location Data to Guide You Where to Go 230
Opportunities Created by Leveraging Location-Based Data 231
Foursquare Example: Gaining Precision in User Location Data 234
San Francisco vs. New York 237
xii I Contents
Lessons from Foursquare 238
Strategy Implications of Using Location-Based Data 241
12. Protecting Data Assets 242
Privacy Concerns 242
Tracking and Monitoring 243
Who Owns the Data? Data Ownership Raises Many Questions 244
Data Ownership Differs for Actively Shared and Passively Captured Data 243
Privacy in Aggregate Data Views 247
Operational Risk in Dealing with Big Data 248
Best Practices for Firms Dealing with Sensitive Personal Data 250
i}. Future Trends in Big Data 252
Increases in Automation for Data Capture, Creation, and Use 252
Cloud Computing Makes Big Data Possible for Most Firms 254
Flexible Analytical Tools Make Big Data Processing Possible to More Firms 254
Mobile Platforms Drive Location-Based Data and Services to New Levels 254
Analytical Talent Will Be in Short Supply Due to High Demand 255
Aggregation of Digital Platforms Will Become More Common 255
Digital Platforms Will Reduce Market Inefficiencies through New Data 236
Autonomy, not Just Automation, Will Become More Mainstream 237
14. Getting Started—SIGMA Framework for Implementing a BigData Strategy:
From BigData to Big Profits 258
Sources of Data 239
Innovation 259
Growth Mindset 260
Market Opportunities 261
Analytics 262
Big Data to Big Profits Diagnostic: Scoring an Enterprise with the SIGMA
Framework for Big Data Readiness 263
Getting Started on the Path from Big Data to Big Profits 266
SELECTED BIBLIOGRAPHY 269
INDEX 271
|
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| spelling | Walker, Russell 1972- Verfasser (DE-588)1035553589 aut From big data to big profits success with data and analytics Russell Walker New York, NY Oxford Univ. Press 2015 XXVI, 283 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Unternehmen (DE-588)4061963-1 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Big Data (DE-588)4802620-7 s Data Mining (DE-588)4428654-5 s Datenanalyse (DE-588)4123037-1 s Unternehmen (DE-588)4061963-1 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028219561&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Walker, Russell 1972- From big data to big profits success with data and analytics Unternehmen (DE-588)4061963-1 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd |
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| title | From big data to big profits success with data and analytics |
| title_auth | From big data to big profits success with data and analytics |
| title_exact_search | From big data to big profits success with data and analytics |
| title_full | From big data to big profits success with data and analytics Russell Walker |
| title_fullStr | From big data to big profits success with data and analytics Russell Walker |
| title_full_unstemmed | From big data to big profits success with data and analytics Russell Walker |
| title_short | From big data to big profits |
| title_sort | from big data to big profits success with data and analytics |
| title_sub | success with data and analytics |
| topic | Unternehmen (DE-588)4061963-1 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Unternehmen Big Data Datenanalyse Data Mining |
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