“There are five broad ways in which using big data can create value. First, big data can unlock significant value by making information transparent and usable at much higher frequency. Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency now casting to adjust their business levers just in time. Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, sophisticated analytics can substantially improve decision-making. Finally, big data can be used to improve the development of the next generation of products and services.”
2. The Case of Facebook
2.1. Facebook: The Creation of a Giant
- The first includes all those purchases aimed to improving features of the Facebook website: among these we find FriendFeed (a real-time feed aggregator whose “Like” button and “News Feed” functions have become Facebook’s hallmarks since 2009), Octazen Solutions (whose contact importer service was incorporated into “Facebook’s Friend Finder” in 2010), and DivvyShot (whose photo-sharing technologies were integrated into “Facebook Photos” in the same year).
- The second subset encompasses acquisitions needed to enter the smartphone industry: SnapTu (a mobile application platform) and Beluga (an instant group messaging app and web service also) represent the foundations of Facebook Mobile and Facebook Messenger (a messaging app both for mobile and desktop computers, separated from Facebook’s platform). In this field, we also find the most expensive company investments: in 2012 Instagram (a photo-sharing social network still working under its own brand although some of its features have been integrated into Facebook) was bought for $1 billion, while the acquisition of WhatsApp (a free mobile messaging app) was valued at $19 billion.
- The third segment concerns the implementation of Facebook’s advertising revenue model. In 2013 the company absorbed and re-designed the ad-serving and ad-campaign performance measurement platform Atlas Solutions, previously owned by Microsoft. This acquisition permitted to match Atlas’ own tracking techniques with Facebook’s huge repository of anonymized first-party data, insights from offline purchase data providers and people-based advertising (Facebook has partnered with third party data vendors, that is, the data brokers Axciom, Epilson, Experian, Datalogix, Oracle, and Quantium to reach people on the basis of what they buy and do offline ). Instead of the outdated cookies-based model, which has become unreliable since the advent of mobile and the consequent change in users’ purchasing behaviour, which shifted to cross-device habits, this investment appeared to some observers  as an attempt to build an ad network outside of Facebook, challenging Google’s domain in online display advertising. The decision, announced in 2016, to move Atlas from Facebook’s ad tech group to its measurement division due to bad quality and fraud issues , resulted in the synchronous closure of both Facebook Exchange  (a desktop ad-exchange service allowing third party companies to buy advertising spots on the social network) and LiveRail  (a video ad-exchange acquired in 2014 for half a billion dollars). This decision mirrored Facebook’s intentions to build a closed and centrally controlled “off Facebook” digital advertising ecosystem, a “walled garden” that keeps data sheltered from other parties’ access, called the Facebook Audience Network . The latter, working in synergy with Facebook Ads Manager, represents company essential revenue source of the company.
- The fourth and final segment concerns diversification, namely acquisitions in sectors other than social advertising. Nonetheless, these purchases can be considered still strongly related to its core business. In 2014, Facebook acquired the virtual reality tech company Oculus VR, the fitness/health tracking app company ProtoGeo, and the UK solar-powered drone maker Ascenta. This latter talent acquisition—combined with a team composed of members of the NASA Jet Propulsion Laboratory, NASA Ames Research Centre, and the National Optical Astronomy Observatory—has been functional to the development of a larger project in the framework of a specifically created Research and Development group called Connectivity Lab: Internet.org , in partnership with some telecom industry giants. The project is a mobile application with the goal of bringing affordable internet access to that still-prevalent portion of the world that has not yet experienced the ‘benefits of connectivity,’ of using vehicles such as, precisely, high altitude solar powered unmanned aircrafts (that is, drones), lasers and satellites. The platform was rechristened with the less pretentious name of “Free Basics” in September 2015, after digital rights groups from 31 countries signed an open letter  to Zuckerberg, saying that Internet.org, by providing access to a tiny and selected set of websites and services rather than to the full Internet, ‘violates the principles of net neutrality, threatening freedom of expression, equality of opportunity, security, privacy and innovation’.
2.2. Facebook’s Business Model
- Facebook’s platform offers a costless vast array of technology features to its subscribed users.
- Users provide Facebook (who takes note of every single bit) with information of all kinds, from more standard to less intuitive types of data .
- Through Facebook Ads Manager, advertisers purchase advertising slots from Facebook, based on a real-time auction mechanism.
- Ads are displayed on publishers’/developers’ websites/apps who, being registered to FAN (net) through FAN (tool), are paid for hosting Facebook-powered ads.
- Ads are displayed also on Facebook platform itself (Facebook news feed for desktop and/or mobile, Facebook right-hand column, Instagram, and Messenger).
- Advertising spaces can be purchased/offered either directly through Facebook Ads Manager/FAN (tool) or indirectly through an advertising agency.
- Advertiser/publisher/developer expenses/revenues do not depend on the actual purchase of the advertised product or service, but on the number of user clicks on the ads, namely on the mere “attention” that they generate (by chance, mistake or real interest) for ads.
3. The Transformation Process and the Sources of Valorisation in the Platform Economy
- Advertising platforms like Google and Facebook, extracting information from their users to resell their profiles in the form of advertising spaces.
- Cloud platforms such as Amazon Web Services, which create hardware and software for digital-dependent markets and lease them to businesses of all kinds, creating monopolies on knowledge.
- Industrial platforms like General Electric or Siemens, building hardware and software at lower production costs, manufacturing and transforming goods into services (Industry 4.0).
- Product platforms like Spotify, which generates profits by relying on other platforms that transform a commodity like music in a service, and earn through share of subscription paid to subscribers to the aforementioned service.
- Work platforms such as Uber, Airbnb, Deliveroo or Foodora, which organize the workforce through an algorithm and connect customers and businesses by drawing profit through the reduction of labour costs.
- Logistic platforms like Amazon, that govern trade and the displacement of goods.
“Despite the confusion of the input, the Google system works better. Its translations are more accurate than those offered by other systems. And it is much, much richer. In mid-2012, its dataset covered over 60 languages. He was even able to accept voice input in 14 languages to make translations more fluid. And since it treats the language simply as a chaotic mass of data to which the calculation of probabilities can be applied, it can even translate between two languages like Hindi and Catalan”. (p. 132)
“The set of business processes to collect data and analyse strategic information, the technology used to implement these processes and the information obtained as a result of these processes”. (p. 54)
- company data collection;
- their cleaning, validation and integration;
- the subsequent data processing, aggregation and analysis; and
- the fundamental use of this amount of information in strategic and enhancement processes.
- The first is related to the large amount of data, because the more we have, the more we can learn. The spread of machine learning is closely linked to the appearance of big data.
- The second aspect shows instead how a mountain of available data can reduce the complexity that characterizes these processes. With machine learning, the process undergoes a strong acceleration. “The Industrial Revolution has automated manual labour, and the Information Revolution has done the same with the intellectual. Machine learning, on the other hand, automates automation itself: if it were not there, programmers would become bottlenecks that curb progress”  (p. 14).
“Amazon cannot adequately encode the tastes of all its customers in a program, and Facebook is not able to write a program that chooses the best updates to show to each of its users. Walmart, the distribution giant, sells millions of products and must make billions of decisions a day: if its programmers tried to write a dedicated program, they would never finish. The solution adopted by such companies, instead, is to unleash the learning algorithms on the mountains of data that have accumulated and let them guess what customers want”. (p. 17)
4. Digital Labour or Digital Work?
- Digital labour has been used to describe the labour-force of independent contractors who work on their own account and at their own risk for low wages and without social security, as in the case of many platform-based business models like Uber, Foodora or other work and logistic platforms.
- Digital labour means also the human activity used by other platform-based business models like Facebook or Google that rely on a new composition of capital capable of capturing personal information and transforming it into big data.
“The basic argument in this debate is that the dominant capital accumulation model of contemporary corporate Internet platforms is based on the exploitation of users’ unpaid labour, who engage in the creation of content and the use of blogs, social networking sites, wikis, microblogs, content sharing sites for fun and in these activities create value that is at the heart of profit generation”. (p. 237)
“Our online identity, so eagerly performed, has a curious afterlife in faraway data centers where subjectivities and data are turned into monetary value. Without being recognized as labour, our location, expressions, and time spent on the network can be turned into economic value. The tracking and monetization of users is frequently justified with the significant operating costs of platform operators. It is unclear, however, what exactly is recorded, how its value is measured, to whom it is sold, and for what purpose.”. (p. 69)
“The process, described as the ‘becoming-rent of profit’ (Marazzi, 2010 ; Vercellone, 2010 ) here becomes apparent: Facebook does not reap a profit merely from organizing the paid labour of its relatively few employees (as labour process theory would suggest), but extracts a rent from the commons produced by the free labour of its users”. (p. 2)
5. Preliminary Conclusions
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|Type of Data||Source of Data||Sector||Function|
|Big volume||Online||Financial Services||Marketing|
|Continuous flows||Sensors||Manufacturing||Human Resources|
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