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Big Data: Understanding How Data Powers Big Business

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Overview

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 ...

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Overview

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.

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Product Details

  • ISBN-13: 9781118739570
  • Publisher: Wiley
  • Publication date: 9/30/2013
  • Edition number: 1
  • Pages: 240
  • Sales rank: 457,150
  • Product dimensions: 7.30 (w) x 9.20 (h) x 0.50 (d)

Meet the Author

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.

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Table of Contents

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 Ramifi cations 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 Ramifi cations 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 Profi tability 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 Ramifi cations 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

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Customer Reviews

Average Rating 2.5
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Sort by: Showing all of 3 Customer Reviews
  • Anonymous

    Posted April 3, 2014

    The book is written like a big presentation and is tremendously

    The book is written like a big presentation and is tremendously repetitive; in fact, one chapter is a cut and paste from previous chapters. This is a great resource for the absolute novice who does not know how to use Wikipedia. Which this book references extensively.

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  • Posted December 10, 2013

    This book is insightfully well written as it anticipates the rea

    This book is insightfully well written as it anticipates the reader's questions while providing comprehensive explanations. This book will be useful to both an experienced Data Analytics professional as well as the novice who is trying to wrap their heads around the concept of Big Data. This book demonstrates an advanced writing expertise evidenced in its layout and structure. For example, as I questioned concepts or ideas brought up in the book, I was pleasantly surprised to find these answered within the next few pages. As a veteran technologist in a Fortune 500 company, I recognize Bill's competency in this field and appreciate his pragmatic approach to the subject of Big Data.

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  • Anonymous

    Posted December 10, 2013

    Not Available for Nook for the Web

    Book content: 4 stars
    E-Book: 1 star. Not web enabled

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