Web companies such as Google have radically redefined the concept of the consumer: instead of paying for online services, users generate data for the hosting companies which is later sold to advertisers for a significant sum. This section analyzes the monetization of data collected through these online services, in addition to the development of social innovation projects based on data analysis. Five American-based tech giants (Google, Amazon, Facebook, Apple, and Microsoft, collectively known as GAFAM) grew to dominate the industry for online service providers. As the world’s major players, these few companies have unprecedented access to the enormous quantity of data generated by their users on a daily basis. Recorded data on individual browsing behavior, such as posts and clicks provides new insights into private life and social behavior.

The data collected by websites and social networks serves as a boon for advertisers who want to better reach their target markets. Most companies, like Google, Facebook, or Twitter, all want to access the lucrative advertising market, which was worth $444 trillion in 2014. They specialize in providing contextual advertising, which sends different ads to users depending on geography, gender, favorite brands, job, or marital situation in order to target specific populations. Money generated from these ads make up the large majority of revenues for Facebook (92.5% in 2014), Google (93%), and Twitter (89.5%) (Brygier, 2014).

Companies calculate money generated from advertising as the Average Revenue per User (ARPU), often linked with a high Monthly Active User (MAU) base. Two important elements determine how much companies earn per user: geography and the ownership of a cell phone. Users in the U.S. and Canada provide the highest ARPUs, while Europeans on average only generate half of this income. The lowest levels of revenue per user come from India, where most of the people access the Internet through their cell phones (Brygier, 2014). Mobile phone users present particular challenges, since there is less space on the screen for ads, and cell phone use tends to be associated with a shorter attention span (Tewari, 2015).

Lured by increasing profits, these companies work to constantly increase the number of active users and the amount of money generated by each user, in part by developing new methods of advertising. For example, increased spending from advertisers raised Facebook’s ARPU by 20% from 2014 to 2015. Part of the network’s success stems from the dwindling supply of ad spaces since the death of print and broadcast media combined with the rise of adblockers. In addition, there are better deals for advertisers through social media networks, which have much more accurate information about their users (Hern, 2015).

Large tech companies with data based business models also tend to acquire successful start-ups in order to increase their user base. When Facebook bought Instagram and Whatsapp, it allowed the company to collect and sell users’ data across different platforms. The UK Information Commissioner’s Office blocked a proposal from Facebook in 2016 to use phone numbers from Whatsapp in order to better target ads on the main Facebook profile (Hern, 2016). This means that users would have had the perception of using different platforms, while actually submitting their personal information to a few select actors in the digital economy.

A variety of actors and services interact when gathering and facilitating the exchange of data, a process that gains intensity with the growth of emerging technologies. In addition to providing online connectivity, internet service providers (ISPs) routinely collect and sell their customers’ data. In the US, politicians recently voted down regulations meant to prevent internet service providers (ISPs) from selling users’ web-browsing histories and app usage histories to advertisers. Without these protections proposed by the Federal Communications Commission (FCC), ISPs such as Comcast, Verizon and AT&T are free to track browsing behavior and sell that data to advertisers without their users’ consent (Solon, 2017).

In addition, with external software linked to smartphones such as third-party libraries, app-developers can add functionality to their products, such as Facebook authentication, and make money by behavioral marketing. For example, the Google AdMob library might access a user’s location to target the user with ads, while the Flurry analytics library might gather user information for a marketing profile (Spice, 2017). This helps make advertising as targeted and relevant as possible to the user, thereby increasing its effectiveness.

Finally, the “domotique” industry , marked by the rise of digital personal assistants such as Google Home, Alexa, Siri, and Echo, is predicted to grow rapidly. Since these machines will be placed in the middle of the home, they will be able to gather information that has long been inaccessible to most companies (Boyer, 2017). This new market then generates new ways to facilitate users’ ability to consume. For example, a connected fridge could automatically order groceries online when it runs low and might show ads when particular ingredients are on sale.

In addition to collecting and selling data to advertisers about user content, several social and conventional media websites integrate advertising in their content through corporate sponsorship. In 2012, Facebook developed an additional revenue stream through the “sponsored” posts, which are ads disguised as native content. This was in response to brands naturally reaching only 15-16% of their fan base, and branders accepted that would need to pay in order to reach and engage with their consumers (Tewari, 2015).

Another example of corporate sponsorship lies in infotainment sites like Konbini and Melty, which are based on social media, videos, heavy advertising, and very low journalistic quality. Ads are now placed within the text itself, rather than alongside it, a technique known as “native advertising.” The algorithm for Google trends predicts readers’ interest based on previous data and decides the topics for new articles, which are often sponsored by large corporations such as Coca Cola or Nike. Native advertising is increasing in importance because it is not yet detectable by adblockers (Eustache, 2017).

Finally, the Google owned Snapchat messaging app also expanded its services to include ads in its Stories feature and sponsored lenses for businesses. Following the same model, Facebook’s Instagram also included 30 second videos with ads and the equivalent of “Stories” (McQueeney, 2015).

The healthcare industry is not only predicted to be a major source of economic growth, but also to play an important role in the data monetization industry. According to Adam Tanner, a fellow at Harvard’s institute for quantitative social science, it is important for healthcare users to realize that all private medical data is for sale, and that information disclosed to personal doctors can be sold to drugstore chains like Rite Aid and CVS (Thielman, 2017). With a 3 trillion dollar industry at stake just in the US, many insurance companies and governments are highly interested in learning more about the health of their users (Humer and Finkle, 2014). In fact, medical records are so valuable that they can be up to 10 times more valuable than stolen credit card details on the black market (Humer and Finkle, 2014). The major tech companies also sense the importance of the digital healthcare information market and are investing accordingly. Google’s DeepMind and Verility are now working tightly with UK’s biggest drug company, GSK, while Apple’s iWatch is working with Stanford University and American medicine agencies to test for heart arrhythmias (Withers, 2017). These public-private partnerships can have important impacts for the pricing of future healthcare for at-risk patients.

Usually aimed at profit, the collection and analysis of data can also be used for social good, from helping humanitarian crisis response, to installing Internet services in underserved communities, and improving the daily lives of people with disabilities. Private companies, such as social media networks or financial service providers, can collect data in places previously unavailable to researchers due to difficult conditions such as extreme poverty, a lack of infrastructure, or frequent natural disasters. Non-profit organizations and research institutions can then use these data indicators to fill information gaps and better inform policy making. The United Nations developed in the Global Pulse Initiative in order to promote this type of public-private data partnership and organize research projects all around the world (“Global Pulse About,” 2016).

For example, information from mobile money transactions in Uganda can help map out “insights about population movements, density, location, social patterns or finances” in the absence of official statistics (“Exploring the Potential of Mobile Money,” 2017). Development and government agencies can then benefit from this additional information to make better policies. In Mexico, data analysts used daily financial transaction information from the BBVA banking group to measure post-hurricane economic resiliency and to find how long it took for payments and cash withdrawals to return to normal levels. This in turn allowed agencies to better target emergency response and reconstruction programmes (“Hurricane Odile,” 2016). Finally, the UN Pulse Lab Jakarta determined that “geo-located tweets have the potential to fill the information gaps from the official commuting statistics” in Indonesia’s capital city (“Inferring Commuting Statistics” 2017). Without this information, it is difficult to determine which infrastructure projects will improve transportation efficiency.

As the concepts of corporate social responsibility and social enterprise spread, several companies find themselves involved in developing infrastructure projects or tools aimed at having a positive impact. Facebook notably developed Internet.org in India in partnership with the Reliance Communications company to offer the country’s underserved communities a free version of an internet connection with access to news, health, and job information in addition to a text only version of Facebook. The project has been rebranded as Free Basics and is now present in 25 developing countries (Goel 2015). Initiatives can also come from governments; Indian authorities created Aadhaar, a 12 digit number which identifies each adult through a fingerprint and iris scan, in order to improve citizen identification and the distribution of subsidies. This will help to increase the distribution of ration cards in remote parts of the country and reduce subsidies that go to the wrong people, such as ghost children on school rolls (“Registration for all” 2017). Although the project faces criticism from the security community and NGOs with regards to privacy concerns on the selection of applications included in the Free Basics package, it is also a step in ensuring that all Indian citizens have access to proper government services.

Finally, emerging technologies such as the Internet of Things (IoT) can improve living conditions for people with different kinds of disabilities. According to IoT BusinessNews, the "global market for connected home devices was valued at USD 58.4 billion in 2014 and is expected to rise to nearly USD 410 billion by 2022” (Marc, 2017). These technologies will allow people to dim lights, lock doors, and adjust temperature control with voice commands or smartphone apps, improving autonomy for many disabled people. For example, a “person with little to no vision could use appliances throughout their home with greater ease, and a deaf person could receive security alerts about disturbances they might not have noticed on their own” (Marc, 2017). In this case, technology helps disabled people participate in society on a more independent and equal basis.

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