Big Data
John Wiley & Sons Inc (Verlag)
978-1-118-73957-0 (ISBN)
Leverage big data to add value to your business
Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value.
Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data.
Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes
Explores different value creation processes and models
Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles
Provides methodology worksheets and exercises so readers can apply techniques
Includes real-world examples from a variety of organizations leveraging big data
Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice.
Bill Schmarzo is the Chief Technology Officer for EMC Global Services' Enterprise Information Management & Analytics service line. Nicknamed the Dean of Big Data, he is responsible for setting strategy for EMC's big data consulting business. He created the Business Benefits Analysis methodology and has served on the faculty of The Data Warehouse Institute.
Preface xix
Introduction xxi
1 The Big Data Business Opportunity 1
The Business Transformation Imperative 3
Walmart Case Study 3
The Big Data Business Model Maturity Index 5
Business Monitoring 7
Business Insights 7
Business Optimization 9
Data Monetization 10
Business Metamorphosis 12
Big Data Business Model Maturity Observations 16
Summary 18
2 Big Data History Lesson 19
Consumer Package Goods and Retail Industry Pre-1988 19
Lessons Learned and Applicability to Today’s Big Data Movement 23
Summary 24
3 Business Impact of Big Data 25
Big Data Impacts: The Questions Business Users Can Answer 26
Managing Using the Right Metrics 27
Data Monetization Opportunities 30
Digital Media Data Monetization Example 30
Digital Media Data Assets and Understanding Target Users 31
Data Monetization Transformations and Enrichments 32
Summary 34
4 Organizational Impact of Big Data 37
Data Analytics Lifecycle 40
Data Scientist Roles and Responsibilities 42
Discovery 43
Data Preparation 43
Model Planning 44
Model Building 44
Communicate Results 45
Operationalize 46
New Organizational Roles 46
User Experience Team 46
New Senior Management Roles 47
Liberating Organizational Creativity 49
Summary 51
5 Understanding Decision Theory 53
Business Intelligence Challenge 53
The Death of Why 55
Big Data User Interface Ramifications 56
The Human Challenge of Decision Making 58
Traps in Decision Making 58
What Can One Do? 62
Summary 63
6 Creating the Big Data Strategy 65
The Big Data Strategy Document 66
Customer Intimacy Example 67
Turning the Strategy Document into Action 69
Starbucks Big Data Strategy Document Example 70
San Francisco Giants Big Data Strategy Document Example 73
Summary 77
7 Understanding Your Value Creation Process 79
Understanding the Big Data Value Creation Drivers 81
Driver #1: Access to More Detailed Transactional Data 82
Driver #2: Access to Unstructured Data 82
Driver #3: Access to Low-latency (Real-Time) Data 83
Driver #4: Integration of Predictive Analytics 84
Big Data Envisioning Worksheet 85
Big Data Business Drivers: Predictive Maintenance Example 86
Big Data Business Drivers: Customer Satisfaction Example 87
Big Data Business Drivers: Customer Micro-segmentation Example 89
Michael Porter’s Valuation Creation Models 91
Michael Porter’s Five Forces Analysis 91
Michael Porter’s Value Chain Analysis 93
Value Creation Process: Merchandising Example 94
Summary 104
8 Big Data User Experience Ramifications 105
The Unintelligent User Experience 106
Understanding the Key Decisions to Build a Relevant User Experience 107
Using Big Data Analytics to Improve Customer Engagement 108
Uncovering and Leveraging Customer Insights 110
Rewiring Your Customer Lifecycle Management Processes 112
Using Customer Insights to Drive Business Profitability 113
Big Data Can Power a New Customer Experience 116
B2C Example: Powering the Retail Customer Experience 116
B2B Example: Powering Small- and Medium-Sized Merchant Effectiveness 119
Summary 122
9 Identifying Big Data Use Cases 125
The Big Data Envisioning Process 126
Step 1: Research Business Initiatives 127
Step 2: Acquire and Analyze Your Data 129
Step 3: Ideation Workshop: Brainstorm New Ideas 132
Step 4: Ideation Workshop: Prioritize Big Data Use Cases 138
Step 5: Document Next Steps 139
The Prioritization Process 140
The Prioritization Matrix Process 142
Prioritization Matrix Traps 143
Using User Experience Mockups to Fuel the Envisioning Process 145
Summary 149
10 Solution Engineering 151
The Solution Engineering Process 151
Step 1: Understand How the Organization Makes Money 153
Step 2: Identify Your Organization’s Key Business Initiatives 155
Step 3: Brainstorm Big Data Business Impact 156
Step 4: Break Down the Business Initiative into Use Cases 157
Step 5: Prove Out the Use Case 158
Step 6: Design and Implement the Big Data Solution. 159
Solution Engineering Tomorrow’s Business Solutions 161
Customer Behavioral Analytics Example 162
Predictive Maintenance Example 163
Marketing Effectiveness Example 164
Fraud Reduction Example 166
Network Optimization Example 166
Reading an Annual Report 167
Financial Services Firm Example 168
Retail Example 169
Brokerage Firm Example 171
Summary 172
11 Big Data Architectural Ramifications 173
Big Data: Time for a New Data Architecture 173
Introducing Big Data Technologies 175
Apache Hadoop 176
Hadoop MapReduce 177
Apache Hive 178
Apache HBase 178
Pig 178
New Analytic Tools 179
New Analytic Algorithms 180
Bringing Big Data into the Traditional Data Warehouse World 181
Data Enrichment: Think ELT, Not ETL 181
Data Federation: Query is the New ETL 183
Data Modeling: Schema on Read 184
Hadoop: Next Gen Data Staging and Prep Area 185
MPP Architectures: Accelerate Your Data Warehouse 187
In-database Analytics: Bring the Analytics to the Data 188
Cloud Computing: Providing Big Data Computational Power 190
Summary 191
12 Launching Your Big Data Journey 193
Explosive Data Growth Drives Business Opportunities 194
Traditional Technologies and Approaches Are Insufficient 195
The Big Data Business Model Maturity Index 197
Driving Business and IT Stakeholder Collaboration 198
Operationalizing Big Data Insights 199
Big Data Powers the Value Creation Process 200
Summary 202
13 Call to Action 203
Identify Your Organization’s Key Business Initiatives 203
Start with Business and IT Stakeholder Collaboration 204
Formalize Your Envisioning Process 204
Leverage Mockups to Fuel the Creative Process 205
Understand Your Technology and Architectural Options 205
Build off Your Existing Internal Business Processes 206
Uncover New Monetization Opportunities 206
Understand the Organizational Ramifications 207
Index 209
| Verlagsort | New York |
|---|---|
| Sprache | englisch |
| Maße | 188 x 234 mm |
| Gewicht | 408 g |
| Themenwelt | Informatik ► Weitere Themen ► Hardware |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-10 | 1-118-73957-4 / 1118739574 |
| ISBN-13 | 978-1-118-73957-0 / 9781118739570 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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