In This Issue
Winter Bridge: A Global View of Big Data
December 15, 2014 Volume 44 Issue 4

Editor's Note: A Global View of Big Data

Monday, December 15, 2014

Author: Yong Shi

Big Data evolved from two major developments in the 20th century. The creation of the computer in the 1940s gradually provided tools for people to collect data, and Big Data became a popular term to represent the collection, processing, and analysis of large quantities of data (Tuitt 2012). Those quantities have grown exponentially in the past 7 decades. An IDC report estimated that the world would generate 1.8 zettabytes of data (1.8 × 1021 bytes) by 2011 (Gantz and Reinsel 2011). By 2020, this figure will grow 50 times or more. The Big Data era has arrived!

The term Big Data captures the data collection and analysis that have evolved from database management in the 1960s to data warehousing in the 1970s, knowledge discovery in databases (KDD) in the 1980s, enterprise resource planning and data mining in the 1990s, and customer relationship management and business analytics in the 2000s (Chen et al. 2012). The term Big Data unifies all of these concepts so that most people know what it means.

It is not possible to ignore the presence and impact of Big Data in daily life. Among other things, such data provide a very important opportunity to the business world for increasing productivity (Shaw 2014). But Big Data present significant challenges for the people who analyze them because of their complex and mixed structure and lack of adequate technology.

For this issue I invited 14 authors and coauthors from 9 countries and regions—Australia, Brazil, China, Japan, Hong Kong, Romania, Spain, the United Kingdom, and the United States—to contribute the perspectives of their country or region on Big Data development. The issue’s articles cover the countries, political units, or geographical regions of China, Japan, the European Union, the Commonwealth, Latin America, and the United States. The authors either chose a topic that reflects the development of Big Data in general in their country or described Big Data–related applications.

I set the stage in the first article by outlining the trends of Big Data development from a science and engineering point of view (Shi 2014). Technologies are lacking for efficient analysis of semi- and unstructured data. The science of Big Data, called data science, remains almost unknown. This paper calls for data scientists and engineers to work toward breakthroughs on three challenges: (1) transformation of semi- and unstructured data to structured data; (2) advances in systematic modeling to explore the explore the complexity and uncertainty of Big Data; and (3) understanding of the relationship of data heterogeneity, knowledge heterogeneity, and decision heterogeneity. The paper also calls for countries around the world to open governmental data sources so that people can access needed data to change and improve their life.

In the second paper Tien (2014) provides a helpful overview of Big Data and then reviews the state of Big Data development in the United States. He explains the components of Big Data (acquisition, access, analytics, and application), contrasts traditional and Big Data methods, identifies potential applications of Big Data to the Grand Challenges of the US National Academy of Engineering, and presents “remarks” on issues specific to each component that warrant attention. Big Data, he concludes, “have to be regarded as a permanent disruptive innovation.”

Next, Li and colleagues (2014) look at the impact of Big Data in the Chinese financial industry, which has rapidly implemented data analysis and data mining in recent years. The industry has about $24 trillion and more than 10 petabytes of data. In addition, there has been a boom in Internet companies, such as Alibaba Group, which have stepped into the financial market. The use of Big Data technologies in China not only supports financial system innovation but also improves financial risk management.

Tsumoto (2014) highlights a number of achievements of Big Data in Japan. He discusses the application of Big Data in economic development, the consumer market, traffic flow control, and hospital management, and cites educational programs to develop the needed skills, knowledge, and practice. He also describes new government funding programs for Big Data research in areas such as agriculture and genomics.

Turning to the European Union, Filip and Herrera-Viedma (2014) report current strategies there to meet the Big Data movement. Given the estimation that the Big Data sector is likely to grow at a rate of 40 percent per year, the European Union is not quite ready yet for Big Data and should prepare to meet the challenge in two ways. For industrial applications of Big Data, since there are fewer Big Data companies in the European Union than in the United States, EU businesses need to transition to data-driven styles for better productivity. From the policymaking perspective, the European Union needs to clearly identify its Big Data priorities, objectives, and plans as well as obstacles and the regulations needed to protect privacy.

In the next article, He and colleagues (2014) provide an assessment of current trends and plans for future Big Data development in representative Commonwealth countries—Australia, Canada, India, South Africa, and the United Kingdom—on five continents. They focus on four aspects relevant to Big Data capability: government strategy; algorithms, tools, and infrastructure to store and analyze data; Big Data research and development, education, and human capital; and industrial practice. In 2012 the Commonwealth’s nominal gross domestic product (GDP) was $9.8 trillion—15 percent of world GDP—so Big Data developments in these countries will affect the world economy.

Gomes (2014) surveys the progress of Big Data applications in Latin America, looking at the region’s two largest economies, Brazil and Mexico, as well as Argentina, Chile, Colombia, and Peru. There could be $1.9 billion invested in the Big Data sector by 2018 in these Latin America countries. Some countries in the region are already riding the wave of Big Data. For example, the Brazilian market for Big Data (hardware, software, and service) is projected to grow from $285 million in 2013 to $1 billion in 2017, and commercial banks and e-business will benefit substantially from Big Data applications.

The purpose of this issue is to provide readers with a snapshot of what is going on in Big Data around the world. It is not—cannot be—comprehensive, but it is informative of current trends and emerging developments. Big Data are rapidly changing the world, and action is necessary to catch the wave and make the most of Big Data for a better future.


I thank Bridge Editor in Chief Ron Latanision and Managing Editor Cameron H. Fletcher for their kind invitation to prepare this issue. In addition, the authors and I greatly appreciate Ms. Fletcher’s excellent work in editing and polishing all the papers in this issue. I am also grateful for the constructive comments of Daniel Berg (NAE), University of Miami.


Chen H, Chiang RHL, Storey V. 2012. Business intelligence and analytics: From big data to big import. MIS Quarterly 36(4):1165–1188.

Filip FG, Herrera-Viedma E. 2014. Big data in the European Union. Bridge 44(4):33–37.

Gantz J, Reinsel D. 2011. Extracting Value from Chaos. Framingham, MA: International Data Corporation (IDC). Available at value-from-chaos-ar.pdf.

Gomes LFAM. 2014. Snapshot of big data trends in Latin America. Bridge 44(4):46–49.

He J, Liu XH, Huang GY, Blumenstein M, Leung C. 2014. Current and future development of big data in Commonwealth countries. Bridge 44(4):38–45.

Li JP, Zhang YJ, Wu DS, Zhang W. 2014. Impacts of big data in the Chinese financial industry. Bridge 44(4):20–26.

Shaw J. 2014. Why “big data” is a big deal. Harvard Business Review, March-April. Available at deal.

Shi Y. 2014. Big data: History, current status, and challenges going forward. Bridge 44(4):6–11.

Tien J. 2014. Overview of big data: A US perspective. Bridge 44(4):12–19.

Tsumoto S. 2014. Big data education and research in Japan. Bridge 44(4):27–32.

Tuitt D. 2012. A history of big data. HCL Technologies Blogs. Available at history-big-data.

About the Author:Yong Shi is director of the Key Research Laboratory on Big Data Mining and Knowledge Management and executive deputy director of the Research Center on Fictitious Economy and Data Science, both at the Chinese Academy of Sciences.