Big Data Huawei booth at Connect 2016 information technology conference and exhibition in Shanghai, 2 September 2016. (Credit: drserg/ via (via: )
Big data: A contested mythical narrative in a developing country

Similar to in other parts of the world, big data entered China as a technical myth promising a brighter future for human society irrespective of geographical, national, and cultural differences. Viktor Mayer-Schönberger—big data futurist, professor at the Oxford Internet Institute, and author of the bestselling book, Big Data: A Revolution That Will Transform How We Live, Work, and Think (2014)—became a legend on Chinese university campuses. His lectures attracted much younger audiences compared to conventional person-centric courses that drew a holistic and historical map to reveal how information and communication technologies are embedded in a broader social process. Moreover, social scientific scholarship, and even that in the humanities, is in the midst of a remarkable transformation led by a single mode of thinking: datafication and computation. As Chris Anderson, editor of Wire magazine, provocatively declared, “With enough data, the numbers speak for themselves.” In this sense, the automation of data systems is surely prospective. Humans’ key concern should shift from Marxian alienation in a techno-driven capitalist society to a singularity between humans and technology; Canadian political economist Vincent Mosco (2017) coined this new era the Next Internet or Post-Internet. However, as Mazzocchi (2015) mused, “Could Big Data be the end of theory in science?” (p. 1250). Alternatively, to borrow a Leninist conceptualisation, could big data or datafication guarantee humankind full emancipation from previously unknown natural or unnatural powers?

China remains central to this discussion given that the country stands at the frontier of the globally emerging big data market, including the datafication of traditional industries and government policy making. Given its rapidly expanding and data-driven economic strength, a techno-nationalist metaphor is spreading in the Chinese Silicon Valley, Zhongguancun, in northwestern Beijing. Some have claimed, “Here is the centre of the universe.” Contrary to this futurist and nationalist mythology, I argue in this article that critical scholarship should shed light on the deconstruction of big data as a new version of the Darwinist technical myth and the political economy of big data industries in reform-era China. A large gap clearly exists between what big data propagandists tell the public through multiple media platforms and who actually benefits from the use of big data as a core productive power in digital capitalism, including as an effective tool to impose certain power over Chinese society. In this sense, given the asymmetry in China’s social development, the country has presented itself as an ideal case to explore the multiplicity of big data in a transitional society.

In the past four decades, a rising China, spurred by fast economic growth, has been keen to incorporate new technologies to increase productivity and guarantee developmental sustainability. Innovations in science and technology (Kejiao Xingguo) have accordingly been founded as a fundamental policy for national development after the Great Cultural Revolution that almost destroyed the economy and downplayed the status of intellectuals under politically laden circumstances, many of whom were scientists. In recent years, as an outcome of the active involvement of the government and industries, big data has become a highly fashionable concept, among others, in defining the future of the Chinese economy and people’s digital lives.

Although the mythical nature of the big data concept is an open narrative, Mosco (2004) contended that the lay public has no professional knowledge to understand how this emerging technology has combined old economies and centralised power among a handful of players. What is most often propagated is a simple digital sublime that endorses the great technical potential of big data to solve all social problems and benefit everyone. Therefore, at least three interrelated components of the big data myth warrant discussion: technical capacity, problem resolution, and universal service.

This is only one side of the story; myth operates on a metaphorical basis. “A metaphorical concept” like big data “can keep us from focusing on other aspects of the concept that are inconsistent with that metaphor” (Lakoff & Johnson, 1980, p. 10). In other words, understanding big data requires assuming a holistic perspective, particularly with regard to the technology’s relative immaturity and potentially negative effects on human society. Recognising the hidden metaphorical reduction of a social process to a single technical process, Boyd and Crawford (2012) critically proposed that “big data is, in many ways, a poor term” (p. 663) that obscures the social historical contexts in which it emerged and its counteractive effects on society.

As I depart here from the discussion of big data’s political economic configuration, I would like to point out three major contestations that underpin the mythical narratives of big data in China and may engender a series of cognitive disruptions in the future.

First is the contestation between technocrats as mythical producer and the lay public as mythical consumer. While technocrats continue to impose a computational mode of thinking, the public has gradually realised that overproduction of data presents a substantial problem in their lives. Swimming in the ocean of data carries a likely risk of drowning in that same ocean. Aside from commercial and political surveillance, finding useful information and building meaningful connections are becoming increasingly difficult. Living in an echo chamber or filter bubble is therefore the new normal in daily life in the digital era. As a reflective consequence, after celebrating the dawn of a big data epoch, the sense that data cannot make life better—and may in fact make it worse—has permeated society and challenged the dominance of the big data myth.

Second is the contestation between data capital and data labour in terms of how the concept of big data is communicated and perceived. While internet companies continuously invest in the building of data infrastructure (e.g., data centres) and computational capacity (e.g., algorism), data-related labour has begun to encounter more precarious working conditions that have already led to a dystopia of the big data concept. Taking Didi (an equivalent of Uber in China) as an example, contracted drivers are completely dependent on the Didi platform, particularly the algorism, to reach passengers and make a living. In a random interview with a Didi driver in Beijing, I was told that the company is leveraging every possibility to increase efficiency and the consumer experience although most drivers suffer from long work hours and high-stress competition on the road. As a result, big data is no longer a beautiful and distant concept for those in the labour force; rather, it is a contested discursive space in which capital and labour intersect, although the collective labour movement has not yet appeared in China.

Last but not least, a contestation exists between data sovereignty and the free flow of data. In a collective learning activity involving the politburo members of the Communist Party’s central committee, General Secretary Xi Jinping outlined the tenets of China’s national big data strategy as follows: not only to build a strong digital economy, construct a modern governance system, and serve the people, but also to defend national data security. In doing so, China’s autonomy over its data infrastructure can be championed. A fierce debate persists about the ownership of big data in China, which was particularly triggered by the recent Facebook data leaks in a post-Snowden era. Therefore, big data, as an integral part of state power, will never lose its sovereign colour.

Politics, the economy, and the four facts of big data in China

In the rest of this article, I will identify four facts that define the political economy of big data in China. Big data has been said to carry multiple political and economic implications that differ from a single perspective on technological rationality. Different stakeholders have begun to enter into this new arena for respective purposes.

First, big data is an important tool for fictitious capital. In the stock market, traditional companies and newly listed internet companies are both using big data concepts to brand themselves to investors who are immersed in a series of mythical data narratives. By the end of 2017, at least 37 big data-related stocks reportedly existed in the Chinese market (Financial Circle, 2017). As Li Yang, Assistant Professor of Marketing at the Cheung Kong Graduate School of Business, pessimistically noted, “This industry is full of bubbles, and there’s hot money flowing around. People are trying to get money and get funded and move to the next round and then IPO” (Clover, 2016). Accordingly, big data is being appropriated by fictitious capital for self-reproduction and expansion.

Second, big data facilitates economic upgrades—but preferably for Baidu, Alibaba, and Tencent, otherwise known as the BAT. Since 2009, China’s “Internet+” policy has connected traditional manufacturing industries with the internet for survival on one hand and sustainable development on the other. Together with its massive computational capacity for accurately matching production with consumption, the internet also promotes the transformation of traditional industries to assimilate into the rising digital economy. However, one should admit that this transformation process is not simply about upgrading traditional industries; it also features new leadership in the market composed of leading internet companies with transformative power. The three BAT monopolies represent such companies, with each BAT member dominating its respective market with regard to web searches, online transactions, and social media while exerting a holistic impact on the economic system. It is thus fair to say that in the current economic environment, “Internet+” has been reduced to “BAT+”. The bargaining power for newcomers in a BAT-created economic ecology is undoubtedly low; therefore, in a big data-driven economy, platform capitalism has already been formulated and will be consolidated in the near future.

Third, big data enables massive surveillance over an enhanced state–market coalition. In their seminal work, Boyd and Crawford (2012) indicated that “there is a deep government and industrial drive toward gathering and extracting maximal value from data, be it information that will lead to more targeted advertising, product design, traffic planning, or criminal policing” (p. 675). In China, the situation is similar but with some contextual characteristics. The country recently witnessed a trend towards a centralised power structure over big data collection, extraction, and application; that is, a handful of players are manipulating the emerging industry. A typical case depicting the centralisation of big data power is China’s Credit Reference Centre. This organisation is operated by the People’s Bank of China, the major national bank endowed with the power to carry out monetary policy and regulate financial institutions in mainland China. As mentioned in an analysis on the Chinese website Financial Times, “Eight licenses have been issued to companies ranging from Tencent and Alibaba, two of the biggest internet conglomerates, to Ping An Insurance, one of China’s largest insurers” (Clover, 2016). The article further mapped out a monopolistic power structure around the technical potential of big data in China, which could be classified into three interrelated aspects. First, the authoritarian government, combined with its patriarchal pattern of governance, facilitates data collection from Chinese citizens and can thus identify good or bad citizens. Second, leading internet companies offer technical support to render data-driven governance possible, such that the government is becoming increasingly dependent on internet companies and associated cutting-edge technologies, thereby strengthening a government–company coalition. Third, those companies, whose core business is based on big data mining, become major beneficiaries. Besides their own data extraction mechanisms, though already ethically problematic, their desire to privatise government-owned, public sets of big data poses a major challenge to the nature of big data itself. As Qiu (2015) criticised, the “enclosures of [the] big data common” (p. 1092) is ongoing.

Fourth, a big data divide is manifesting in the labour market. Boyd and Crawford (2012) quoted Manovich’s three classes of labour to exemplify the inequality between the so-called Big Data Rich and Big Data Poor: data creator, data collector, and data analyst. Data analysts hold the power to determine “the rules about how Big Data will be used, and who gets to participate” (p. 657). However, a more profound classification is emerging in China. Since 2014, Guizhou, an under-developed province in southwestern China, has been designated by the central government as an experimental zone to test China’s national strategy of big data development. Guizhou offers three advantages over other provinces. First, its subtropical humid monsoon climate provides an ideal natural environment to cool the data centre without high energy costs. Second, Guizhou’s provincial government prioritises big data most heavily in the developmental agenda. Third, and most relevant to the big data divide in China, the labour cost is comparatively lower than in other regions. Amidst the continued development of the big data industry, Guizhou’s contribution to the national market is a primary data-cleaning labour force with a lower professional background. After the cleaning process, valuable datasets will be transmitted to Zhongguancun, China’s Silicon Valley in Beijing, where data analysts will assume control of big data mining and use. In other words, the big data labour market is not flat at all.

Concluding remarks

It is difficult to see the entire ocean when swimming in it. Big data is today’s ocean, and society is struggling to determine in which direction to go to find land. As the second largest economy in the world, China quickly jumped aboard the high-speed train of economic and social development powered by a big data engine. However, it is important to slow down and even disembark from the train at certain points to determine whether the train track is well laid and the route is serving everybody alongside. At this critical juncture, I argue that, to understand what big data means to China and how different powerful players wish to privatise it, we must dismantle the mythical narratives of big data as a manufactured, irreversible technical trend and place the political economic structure of big data under scrutiny. Local political and cultural logic should also be considered, such as the patriarchal tradition of governance and sovereignty appeal.



Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.

Clover, C. (2016, January 19). China: When big data meets big brother. Financial Times. Retrieved from

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Lakeoff, G., & Johnson, M. (1980). Metaphors we live by. London: The University of Chicago Press.

Mazzocchi, F. (2015). Could big data be the end of theory in science? A few remarks on the epistemology of data-driven science, EMBO Reports, 16(10), 1250–1255.

Mosco, V. (2005). The digital sublime: Myth, power, and cyberspace. Cambridge, MA: The MIT Press.

Mosco, V. (2017). Becoming digital: Toward a post-internet society. Bingley, UK: Emerald Publishing Limited.

Qiu, J. L. (2015). Reflections on big data: ‘Just because it is accessible does not make it ethical’, Media, Culture & Society, 37(7), 1089–1094.

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Ji Deqiang

Associate Professor; The National Centre for Radio and Television Studies, Communication University of China, Beijing, China, CN

Associate professor of communications at the National Centre for Communication Innovation Studies at the Communication University of China. He is the vice chair of the International Communication Section of International Association for Media and Communication Research (IAMCR), and the editor-in-chief of, a leading online platform for Chinese media and communication studies. His research interests include the political economy of communication, anti-corruption communication, and international communication.