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This is an addition to the data capitalism article.

In the field of information economics, data capitalism signifies a new manifestation of information capitalism in which data is viewed as a commodity and valued as a source of monetization and market power. Media historians and scholars also refer to this mode of capitalism as big data capitalism and often liken it to Shoshana Zuboff’s theorization of surveillance capitalism.

Historically, data capitalism can be linked to traditional methods of personal data collection like census lists and blacklists which were used in the 19th century to document consumer credit risk.

The dotcom bubble of the mid 1990s to early 2000s is underscored by some media scholars and historians as a seminal moment in the development of Internet commerce and our contemporary understanding of data capitalism. The shift from solely selling goods on the Internet to also leveraging user data for advertising and other commercial purposes, has inspired a generation of technology companies with business models driven by data capitalism.

Critical responses with respect to the implications of data capitalism are both positive and negative. Proponents often cite data capitalism’s benefits for building networked communities and personalizing the online user experience. In contrast, critics draw on the negative outcomes that have arisen in relation to data exploitation, privacy violations and unreasonable surveillance of consumers.

Description
Data capitalism operates on the idea that the commodification of data is facilitated by the ideologies and principles of capitalism. Looking to dominant search technology companies like Google Astrid Mager, a researcher with the Austrian Academy of Sciences, has called this a “new spirit of capitalism…that develops in a connexonist world". The data (singular form: datum) referred to in this case are generally any digitally collectable raw figures, quantities or observations that can be subsequently filtered, analyzed, processed or organized by a computer.  The more popularized or buzzword term of big data also fits into the scope of data capitalism, pointing more specifically to the collation of datasets with high volume, velocity and/or variety. Although the terms data and big data are often used interchangeably, it is important to highlight the difference in the complexity of big datasets which requires the use of more involved methods of data analysis.

With an eye to the developing complexity of personal data collection, Sarah Myers West (a post-doctoral researcher with the AI Now Institute at New York University) further elucidates the definition of data capitalism. She describes it as “a system in which the commoditization of our data enables an asymmetric redistribution of power that is weighted toward the actors who have access and ability to make sense of information”. Myers West’s definition is consistent with the traditional characterization of capitalism as an economic system in which the scales of power tilt more favourably towards actors who possess and own the means of production and distribution. As Slovene philosopher Slavoj Žižek explains in his article entitled The Revolt of the Salaried Bourgeoisie, capitalism is a competitive process that favours the first players to privatize and collect “rent” on what Karl Marx called the general intellect. Other frequently discussed features of capitalism which have also become dominant fixtures of the data capitalist model include but are not limited to;
 * the rights to private ownership of property;
 * increased competition among firms;
 * encouragement of the profit motive, a theory which prioritizes the maximization of profits and financial returns;
 * expansion of consumer sovereignty and choice;
 * ever-increasing rates of capital accumulation;
 * amplification of societal class distinctions and divides.

Brief History
Instances of mass data collection on humans and their behaviour span many centuries. For example, the Bible’s Old and New Testaments reference a number of occasions in which census lists were used to collect personal data for population rates or for comparing the military size, organization and strength of different nations. Moving into the 17th century (and on) we begin to see for instance, the use of colonial censuses by Western imperialists who sought to organize “their colonial subjects into intelligible, quantifiable categories”. This was mostly done to widen their nets of social domination and control in foreign territories. However, it is only in the 19th century that media scholars and historians spot a culture of consumer surveillance that places “political and monetary value to the collection of personal data”; a culture of consumer surveillance that has contributed to our contemporary understanding of data capitalism.

Commercial credit reporting by individual businesses and agencies emerged in the mid-to-late 19th century as a way to track consumers’ credit risks and purchasing behaviours. During this time, documenting or cataloguing the (potential) credit risk of consumers was initially made possible by two methods of data collection; blacklisting or what Josh Lauer has called the affirmative-negative system in his book Creditworthy: A History of Consumer Surveillance and Financial Identity in America. To briefly expound on these methods, blacklisting necessitated collecting the names of community members who were either slow in paying their debts or did not pay them at all. The affirmative-negative system, popularized by Lewis Tappan in the 1840s, was a little more comprehensive in its collection of personal data. It allowed creditors to keep updated records of “the financial habits of all individuals within the geography of cities, counties, states and ultimately the nation”. Today, practices of data mining and harvesting for the development of various digital dossiers on consumers appear to harken back to a similar system. Similar methods of data collection have not only expanded in commercial credit reporting but have also benefitted from technological innovation to widen their scope in areas of employment, policing, housing and health.

In her article Data Capitalism: Redefining the Logics of Surveillance and Privacy, Myers West uses the 19th century legacy of utilizing personal data to calculate consumer credit risk as the backdrop for understanding data capitalism. According to her analysis, there are two historical developments from the mid-1990s to mid-2000s which contribute to the current manifestation of data capitalism and demonstrate how Internet commerce began to burgeon as a viable business enterprise. These two seminal moments are;
 * 1) The dotcom bubble and its subsequent crash in the early 2000s;
 * 2) The shift from solely selling goods online to also leveraging user data for commercial purposes.

The Dotcom Bubble and its Implications for Data Capitalism
During the dotcom boom, the overarching goal of Internet commerce was to capitalize on the growing connectivity of Internet users by selling goods and services online. By the mid to late 1990s motivations to increase profits using online commerce had led to some risky investments in various Internet and technology companies. Many of these companies and their investors had overestimated the profitability of their dotcom business models. Unfortunately, the trouble did not stop there. The consumer migration to online could not quickly compensate for how fast technology companies were growing – and this ultimately led to the stock market crash or the bursting of the dotcom bubble in the fall of 2001.

Following the crash, technology entrepreneurs began to think about cost-effective ways of leveraging other functionalities of Web 2.0; deciding to design business models which would give them a competitive advantage over the Internet’s interactivity. This reconceptualization led to a shift from merely selling products and services online but also recognizing the value in using technology to harvest and profit from digital traces left by consumers’ online. Myers West asserts that at this point in the history of e-commerce having “Control over the databases that store users’ data would lead to control over the market”. It is not hard to deduce that many technology companies probably desired to acquire a large of share of that benefit of capitalism. Myers West supposition is also in alignment with Tim O’Reilly’s statement that “In the Internet era, one can already see a number of cases where control over the databases has led to market control and outsized financial returns”. One could argue that this is the case for many of the Big Five technology companies which include Alphabet Inc. (Google), Facebook and Amazon; companies which have managed to generate significant returns on investment by capitalizing on their algorithmic dominance and standing in the data capitalism era. According to Myers West, this means that these companies have unparalleled access to tools within the Internet marketplace which allow them to infuse “data with new kinds of informational power”.

Connections to Zuboff’s Surveillance Capitalism
Myers West states that her conceptualization of data capitalism shares many aspects of Zuboff’s concept of surveillance capitalism. Zuboff describes surveillance capitalism as a business model and new mode of information capitalism that “aims to predict and modify human behaviour as a means to produce revenue and market control”. The theory of data capitalism mirrors a similar position which posits that the predicting and analyzing of human behaviour online is conducted for the purpose of gaining high financial returns and market dominance. A slight difference between the two concepts may exist around surveillance capitalism’s focus on digital surveillance as a technological and social process, while data capitalism appears to gravitate more towards data harvesting and the commercial uses conducted following collection. However, even with this delineation, the reasoning behind why media scholars like Myers West separate the two concepts is still unclear and remains to be further investigated.

Features of Data Capitalism
The general features of data capitalism listed below are comparable to the Zuboff’s four key uses for surveillance capitalism outlined in her article Big Other: surveillance capitalism and the prospects of an information civilization. Zuboff's uses include “data extraction and analysis”, “new contractual forms due to better monitoring”, “personalization and customization” and “continuous experiments”. Structures of data capitalism are similarly built around;
 * Data (or big data) acquisition;
 * “Algorithmic inscrutability” which allows technology companies to guard the algorithms they develop and their systems for data collection and analysis;
 * Technological and algorithmic dominance which allow companies to produce more customized advertising, products and tools;
 * Digital traces left by consumers which are then collected for profit and/or other commercial uses;
 * User data as a privatized commodity which bends to the needs of the capitalist economic system and drives data monetization;
 * Flexible workers who can assume multiple functions;
 * An asymmetric power dynamic which economically supports companies that possess the means to extract and analyze the data collected (i.e. the big data rich);
 * Business models that are designed to capitalize “on the sale of audiences –or more accurately, on the sale of individual behavioral profiles tied to user data”.

Technological innovation renders data capitalism and Zuboff’s surveillance capitalism as ever-evolving phenomena. As a result, the above list of characteristics and features is not exhaustive.

Big Tech
The Big Five technology giants or the FAAMG which include Facebook, Amazon, Apple, Microsoft and Google (under its parent company Alphabet), are typically lauded as some of the most influential market players in the realm of data capitalism. The status of these five corporations is often attributed to their high-performing technology stocks and their “combined market capitalization of over $4.4 trillion”. These five technology companies have embedded one or more of the above-mentioned elements of data capitalism within their business models. Their market dominance is also defined by their ability to conduct successful mergers and acquisitions which have allowed them to secure other technology businesses as smaller subsidiaries. These business decisions often link technology giants with quite large consumer bases and new opportunities for the collection of diverse user data. For example, the $26.2 billion acquisition of LinkedIn by Microsoft is said to have given the technology company “immediate access to more than 575 million members”. This exposed Microsoft to a database of users that could be reached with personalized products and services in areas education, research and employment.

Google
Google is often used as a model for assessing the constitution and materialization of data capitalism. Myers West argues that this is because the company’s business structure is primarily supported by “both the production and use of data”. The company (now under Alphabet) has managed to establish itself as one of the largest web search engines (i.e. Google Search) and leading advertising platforms. Google’s establishment in 1998 points to its ability to weather the storm of the dotcom bubble and still generate profits in the post-bubble era. For example, the Google AdWords program (now known as Google Ads) was established in October of 2000 as a way to sell audiences (and their personal data) to advertisers. The program represented Google’s innovation in “figuring out how to finance its online business by translating the data [it] collected from across the web into content-targeted advertising”. Following the success of Google Ads, Google then rolled out Google AdSense in 2003; a platform which places adverts “on sites across the wider Web through a network of millions of third-party sites that display its ads”. The program helps match consumers with targeted advertisements based on characteristics related to a site’s content or the audience’s interests. Because of Google’s acquisition of YouTube in 2006, the popular video-sharing site has also become a hot-spot for the use of Google AdSense among creators eligible for and accepted into the YouTube Partner Program (YPP). Creators who are a part of YPP are able to earn revenue from “display, overlay and video” advertisements managed through a Google AdSense account. Google has since expanded the scope of its operations through the rollout and creation of various applications like Chrome, Maps and Gmail or tools like Google Analytics and Google Translate.

A 2018 study titled Google Data Collection by Professor Douglas C. Schmidt and his team at Vanderbilt University, describes how Google actively or passively collects user data. Active data collection is more manifest and involves users “directly and consciously communicating information to Google”. This can be done by engaging directly with the Google search engine or Gmail for instance. Contrarily, passive methods are covert in their collection of data. Schmidt notes that passive data harvesting can at times occur unbeknownst to an end user. This understanding of Google’s manifest and latent methods of data extraction may present some concerns around data capitalism; concerns with respect to privacy, informed consent and data ethics.

Positive
As Mark Zuckerberg, the CEO of Facebook, sat before Congress in the wake of the Facebook-Cambridge Analytica data scandal, he was asked whether he aligned with capitalism or socialism. Zuckerberg responded emphatically stating “Congressman, I would definitely consider myself a capitalist”. The technology entrepreneur’s response exemplifies Jerry Frieden and Ronald Rogowski’s statement that “Capitalism’s principal supporters have been those who have benefitted most from its development, or hope to do so”. Where data capitalism is concerned this looks to be the case, with its proponents emerging predominantly from the realm of technology entrepreneurship. Supporters of data capitalism often point to the following benefits;
 * Increases in social engagement for Internet users;
 * Increases in opportunities for businesses to provide customers with more effective and personalized online advertising, tools, services and products;
 * More politically open and accessible online communities and networks;
 * Technological experimentation and investment in the innovation of the applications and products utilized by consumers;
 * Global development of data centers which can create new job opportunities and can help revitalize low-income economies;
 * Expansion of consumer power and sovereignty as the needs of consumers' are placed at the nucleus of all production.

Negative
On the negative end of the spectrum, some media scholars have accused promoters of data capitalism of dabbling in an unrealistic technological utopianism. From Myers West’s perspective, technological utopianism over-inflates the societal and technological benefits of data capitalism, while it simultaneously conceals the detrimental effects for end users and consumers. She adds that claims of transparency made by big data collectors are also weak as “the processes and mechanisms through which companies…enact their business objectives are closely guarded as their most prized trade secrets”. More generally, critics have highlighted negative consequences of data capitalism related to;
 * Data exploitation and misuse;
 * The development of asymmetries of power and knowledge, which confer ownership of user data collected on powerful companies and not the consumers who generate it;
 * Threats to democracy and consumer privacy as a result of the increasing pervasiveness and sophistication of the data surveillance infrastructure;
 * The tightening of linkages between data capitalists and state governments. Edward Snowden’s exposure of the United States’ National Security Agency’s (NSA) PRISM program revealed the “expansion of state power into intimate domains of everyday life". This expansion was made possible by the NSA’s relationships with companies like Facebook, Apple, Google and Microsoft to name a few;
 * The amplified potentiality of algorithmic discrimination and bias in areas of health, policing or credit reporting;
 * The mistreatment of tech workers in the form of low-wages, unpaid labor, substandard working conditions or instances of sexual abuse. This has been demonstrated for instance, by the Google walkouts and protests of tech workers in Silicon Valley;
 * The increased possibilities of mass data breaches, particularly for political influence;
 * The expansion of data centers in regions which are often unable to sustainably operate these data hubs due to limited water and energy resources.