Think Bigger: Developing a Successful Big Data Strategy for Your Business

Think Bigger: Developing a Successful Big Data Strategy for Your Business

by Mark Van Rijmenam


View All Available Formats & Editions
Choose Expedited Shipping at checkout for delivery by Thursday, June 17


Every day, an increasing amount of our movements, transactions, and choices are becoming digitized and stored up into what has become known as “big data”—revolutionizing the way we do business today. And it’s all there for your company to strategically utilize for giant profits! But where to begin? Think Bigger provides a roadmap for organizations looking to develop a profitable big data strategy. Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, this must-have resource for any business not wanting to fall far behind the competition covers the most important big data trends affecting organizations, as well as crucial types of analyses. Big data is changing the way businesses—and even governments—are operated and managed. And now, you too can revolutionize your business by learning how to properly employ the vast amount of digitalized information that is already available to you.

Product Details

ISBN-13: 9780814434154
Publisher: AMACOM
Publication date: 04/01/2014
Pages: 288
Product dimensions: 6.40(w) x 9.10(h) x 1.20(d)
Age Range: 18 Years

About the Author

Mark van Rijmenam is the founder and CEO of He is an expert on AI, Blockchain and Big Data, a highly sought-after speaker and author of the book Think Bigger. He is also co-author of the book Blockchain: Transforming Your Business and Our World, on how blockchain can be used for social good. He is named a global top 10 Big Data influencer and one of the most influential Blockchain people. He is pursuing a PhD at the University of Technology, Sydney on how organizations should deal with Big Data, Blockchain and AI and he is a faculty member of the Blockchain Research Institute.

Read an Excerpt


Of all the data in recorded human history, 90 percent has been created in the last two years. However, the need to use and interpret such Big Data has been around for much longer. In fact a the earliest examples of using data to track and control businesses date back 7,000 years, when Mesopotamians used rudimentary accounting to record the growth of crops and herds. Accounting principles continued to improve, and in 1663, John Graunt recorded and examined all information about mortality rolls in London. He wanted to gain an understanding of and build a warning system for the ongoing bubonic plague. In the first recorded example of statistical data analysis a he gathered his findings in the book Natural and Political Observations

Made upon the Bills of Mortality, which provides great insights into the causes of death in the seventeenth century. Because of his work,

Graunt can be considered the father of statistics.

The nineteenth century witnessed the start of the information age.

Modern data was first gathered in 1887, when Herman Hollerith invented a computing machine that could read holes punched into paper cards to organize census data.


In 1937, during Franklin D. Roosevelt administration, the United States created the first major data project to keep track of contributions by more than three million employers and 26 million employees under the new Social Security Act. IBM was awarded the contract to develop a punch card-reading machine for this immense bookkeeping task.

The British developed the first data-processing machine in 1943

to decipher Nazi codes during World War II. The device, named Colossus a searched for patterns in intercepted messages at a rate of 5,000

characters per second. This reduced the time required to perform the task from weeks to merely hours. It was a huge step forward.

In 1952 the U.S. National Security Agency (NSA) was created and, within 10 years, it had contracts with more than 12,000 cryptologists.

They were confronted with information overload during the Cold War, as they started collecting and processing intelligence signals automatically.

In 1965, the U.S. Government decided to build the first data center to store its more than 742 million tax returns and 175 million sets of fingerprints. Employees transferred all those records onto magnetic computer tape that was stored in a single location. The project was later dropped out of fear of “Big Brother,” but it represented the beginning of the electronic data storage era.

Then, in 1989, British computer scientist Tim Berners-Lee developed what eventually became the World Wide Web. He wanted to facilitate the sharing of information through a “hypertext” system.

Little could he know at that moment the impact his invention would have on everyone.

Beginning in the 1990s, data was created at an amazing rate as more and more devices were connected to the Internet. In 1995, the first supercomputer was built; it performed as much work in a second than a calculator operated by a single person could do in 30,000



In 2005, Roger Mougalas of O’Reilly Media coined the term “Big

Data,” a year after the company created the term Web 2.0. He used the term to refer to a large set of data that is almost impossible to manage and process using traditional business intelligence tools.

In that same year, Yahoo! created Hadoop on top of Google’s MapReduce. Its goal was to index the entire World Wide Web;

nowadays, many organizations around the world use the open-source

Hadoop to crunch massive data sets.

As more and more social network sites appeared and Web 2.0 took flight, more and more data was created daily. Innovative startups slowly mined this vast amount of data and governments also began Big Data projects. In 2009, the Indian government decided to take an iris scan a fingerprint, and photograph of all of its 1.2 billion inhabitants. All this data is stored in the largest biometric database in the world.

By 2010, when Eric Schmidt, the Executive Chairman of Google a spoke at the Techonomy forum in Lake Tahoe, California, he put the information revolution in perspective by stating that “every two days now we create as much information as we did from the dawn of civilization up until 2003. . . . That’s something like five exabytes of data. . . .”

In 2011 the well-received McKinsey report on “Big Data: The Next

Frontier for Innovation, Competition, and Productivity,” concluded that by 2018, the United States would face a shortage of 140,000 to

190,000 data scientists, as well as 1.5 million data managers. The job Big Data Scientist is therefore often coined the sexiest job of the twenty-first century.

In the past few years, there has been a massive increase in the number of Big Data startup companies. All are trying to help organizations manage and understand this explosion of Big Data. As more companies are slowly adopting Big Data, just as with the Internet in

1993, the Big Data revolution is still ahead of us, so a lot will change in the coming years.

In fact, the amount of data is growing at such an explosive rate that we have gone past the decimal system. Today, U.S. agencies, such as NSA and the FBI, are talking about yottabytes when calculating the size of their files. In the (near) future, we will be talking about brontobytes regarding sensor data. Therefore, new terms have been created to describe the amount of data that is expected to be created in coming years.

Big Data will completely change organizations and societies around the world. It is expected that the amount of data currently available will double every two years worldwide. So, let’s take a closer look at what Big Data exactly is.

Table of Contents


1 The History of Big Data 1

The Twentieth Century 1

The Twenty-First Century 2

2 What Is Big Data? 5

Explanation of the 7Vs 5

Eight Realities of Big Data You Should Already Know 12

The Impact of Big Data on Society 19

Takeaways 21

3 Big Data Trends 23

On-the-Go Big Data 25

Big Real-Time Data 31

The Internet of Things 36

The Quantified-Self 43

Big Social Data 46

Public Big Data 50

Gamification 53

Takeaways 57

4 Big Data Technologies 58

Hadoop HDFS and MapReduce 59

Open-Source Tools 60

viii Contents

Big Data Tools and Types of Analysis 62

Takeaways 70

5 Big Data Privacy, Ethics, and Security 71

Big Data Privacy 73

Big Data Ethics 80

Big Data Security 84

Takeaways 91

6 Big Data in Your Organization 93

Key Characteristics of Information-Centric

Organizations 96

Some Generic Big Data Uses 98

Big Data and Return on Investment (ROI) 104

Big Data on the Balance Sheet 106

Internal Big Data Opportunities 107

Big Data Roadmap 123

Big Data Competencies Required 127

Small and Medium Enterprises (SMEs) Can Achieve

Remarkable Results with Big Data 141

Governance for the Accuracy of Big Data 143

Takeaways 147

7 Big Data by Industry 149

Agriculture Industry 149

Automotive Industry 153

Consumer Goods 157

Education Industry 161

Energy Industry 165

Financial Services Industry 169

Gaming Industry 174

Healthcare Industry 178

Legal Profession 183

Manufacturing Industry 185

Not-for-Profit Sector 189

Media and Entertainment Industry 194

Oil and Gas Industry 198

Contents ix

Public Sector 201

Retail Industry 207

Telecom Industry 211

Transportation Industry 216

Travel and Leisure Industry 220

Takeaways 223

8 The Future of Big Data 225

Glossary 233

Notes 241

Index 265

About the Author 278

Customer Reviews