What impact will the central bank’s digital currency have on the financial industry?

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September

2021

5/5 (3)

The central bank digital currency is different from the encrypted digital currency designed and promoted by private organizations (such as Bitcoin)

The central bank digital currency (CBDC) has a similar use scenario to the encrypted digital currency designed by private organizations such as Bitcoin-both are stored and traded in electronic devices, but there is an essential difference between the two: the issuance of encrypted currencies such as Bitcoin It is based on a certain encryption algorithm, and the central bank’s digital currency issuance is based on national credit. Cryptocurrencies such as Bitcoin are decentralized, while central bank digital currencies are centralized-in China, the issuance of tokens by commercial institutions is even forbidden. In this sense, the central bank’s digital currency is not essentially different from traditional banknotes, and can be issued by the central bank. Accordingly, its currency value is generally more stable than encrypted digital currencies.

The central bank’s digital currency will transform the service form of financial institutions

Central bank digital currency is essentially the same as banknotes, but its medium of use is electronic equipment, so it is more convenient to use than banknotes. Users can deposit digital currency in the bank, and then directly trade through electronic devices. On the one hand, it saves the trouble of taking out paper money. On the other hand, it allows the bank to obtain more and more detailed transaction information, and the use of physical counters and ATM machines will be greatly reduced. For banks, the traditional offline marketing model may also be replaced, because there is a large amount of accurate user savings data, the bank can perform user portraits and then conduct business marketing more accurately.

Digital capabilities will become an important factor for financial institutions to compete in the market in the future

Why is digital capability more important for financial institutions? On the one hand, financial institutions with higher R&D technology priority can provide data interfaces with the central bank more quickly, and when the central bank issues additional currencies, they can seize the market faster; on the other hand, financial institutions with stronger data analysis capabilities Institutions will make it easier to improve the customer experience by interpreting the savings and investment of customers. According to a report by PANews, in the United States, the stock prices of cryptocurrency-friendly banks have risen faster with the increase in cryptocurrency trading volume.

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Sherlock, can you solve cybercrime?

30

September

2021

5/5 (3)

Phishing mails in the junk of my mailbox is ‘quite’ normal. Sometimes they end up in your inbox. For some people it is easier to fish out the real ones than for the other when such is addressed to you personally or the company you work for. Last year, webshop Bol.com received a short email written in Dutch from Brabantia. At least that was what Bol.com thought. The message was filled with typos and grammatical errors while the lay out looked professional. It stated that the money must be transferred to Brabantia’s bank account in Spain. Unfortunately, employees of Bol.com fell for it and deposited around 750,000 euros to the scammers (Mous, 2021).

Phishing and online scams as effective threats are just the tip of the iceberg to enable other types of cybercrime, e.g., disruptive malware (DDoS and ransomware), data harvesting malware, malicious domains, fake news and misinformation (Interpol, 2020). The persistence of cybercrime-as-a-service continues to ensure that less skilled criminals can also carry out cyber-attacks (Bijzonder Strafrecht, 2021). Cybercrime cannot or can hardly be solved with investigation. In general, controlling this form of crime rests with the users of the internet.

According to the Cyber Security Council, companies tend to think that no one is deliberately targeting them. They assume that there is only a small chance of becoming a victim. A fundamental fallacy. “You may not be a conscious target, but distance and time do not play a role on the internet,” says Pim Takkenberg of security company Northwave. Criminals search the internet for victims. “So if you don’t take care of your security, you can become a victim, even though no one is deliberately targeting you” (Schellevis & Andringa, 2021).

Ransomware attacks on large companies and institutions are an increasing threat to economic and social security. A part of the ransom money received by hackers is directly invested in new attacks, according to the Dutch police (Bijzonder Strafrecht, 2021). Ransomware is also increasingly being combined with the publishing or resale of information during the attack.

Data has globally become a new natural resource which creates new opportunities for innovation in our digitized economy but also unforeseen malice. Cyber threats are expanding from networks, computers, and smartphones to railways, power grids, cars, hospitals, people and anything with an electronic pulse or a heartbeat (Morgan, 2020). Many ‘Things’ are connected to corporate network to some extent which will further complicating cybersecurity.  

This may sound a bit daunting, but when COVID-19 is slowly out of sight, I think that the world will be down for a moment (again) if cybercrime takes over. Especially when the government pays little attention to this matter.

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Bijzonder Strafrecht, 2021. Aanpak cybercrime: capaciteit OM blijft achter. [Online]
Available at: https://www.bijzonderstrafrecht.nl/home/aanpak-cybercrime-capaciteit-om-blijft-achter

Interpol, 2020. INTERPOL report shows alarming rate of cyberattacks during COVID-19. [Online]
Available at: https://www.interpol.int/News-and-Events/News/2020/INTERPOL-report-shows-alarming-rate-of-cyberattacks-during-COVID-19

Morgan, S., 2020. Cybercrime To Cost The World $10.5 Trillion Annually By 2025. [Online]
Available at: https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/

Mous, A., 2021. Phishingmail kost Bol.com 750.000 euro. [Online]
Available at: https://www.vpngids.nl/nieuws/phishingmail-kost-bol-com-750-000-euro/

Schellevis, J. & Andringa, R., 2021. Digitale deuren staan soms wagenwijd open, ‘situatie is alarmerend’. [Online]
Available at: https://nos.nl/artikel/2375676-digitale-deuren-staan-soms-wagenwijd-open-situatie-is-alarmerend

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Streamr and the advent of decentralized data networks

30

September

2021

5/5 (3)

As the use of blockchain becomes more widespread, many are starting to establish new ways to leverage distributed ledger technology. Although blockchain has been most traditionally associated with cryptocurrencies like Bitcoin, its core technologies are applicable far beyond finance and investment. Recently, many new companies have started to explore the potential of linking decentralized networks and data and create new platforms that connect IoT devices through the use of tokenization and smart contracts. One of the most prominent companies in the industry is Streamr. But what can these platforms offer that traditional centralized cloud based services can’t? 

To start, decentralized networks also offer similar on-demand scalability, minimal up-front investment and benefits from economies of scale. However, what sets these networks apart is the lack of a centralized party; they are by definition peer-peer, which through the use of blockchain encryption allows added security and an increase in transaction speeds. It also benefits from network effects, as the network grows a shared standard is formed across industries, making it even safer and efficient. What makes the technology really exciting is the potential for transactions to be made in between IoT devices, bypassing human intervention. For example, a self-driving electric car that is connected to the Streamr network can use tokens to purchase real-time traffic congestion data, electricity price data of nearby charging stations and weather forecasts for the area; all the while funding these purchases by automatically selling data back to the network.

Because the implementation of decentralization is so novel and complex, many companies like Streamr are still trying to fully implement the technology and deliver on the promise of full decentralization. However, even with the current limitations, the potential that decentralized data networks hold could fundamentally change how data is produced, monetized and integrated with IoT devices.

Source: https://streamr.network/docs/introduction

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The New Way Of Digital Marketing; Programmatic Advertising

30

September

2021

5/5 (3)

Within the advertising sector, the digitization of processes and automation has become a trend that is omnipresent. A particular form of digital marketing that is advancing fast is that of programmatic advertising. It entails automated bidding for digital campaigns or website display, and is established to replace extensive human negotiations with real-time online auctions (Rask, 2021).

It has been estimated that by this year, the US digital display spending via automated transactions will be approximately $81 billion (Fisher, 2019). Programmatic advertising thus enhances the efficiency, costs and transparency for both parties (advertisers and publishers) on the advertising platform, also called Ad Exchange. The described automated or real-time bidding process is a form of programmatic advertising that uses the technologies of Artificial Intelligence and Machine Learning to optimize the advertising process.
Where before it was needed to go through negotiations and pre-set prices to buy an advertising spot on a website, it now takes milliseconds to find the optimal price and target group for both the supplier, the website owner, and the demand, the advertisers, through ad exchange (Rask, 2021). This ad exchange is similar to a platform where you can buy online ad space from different ad networks or groups of websites that offer advertising space. The real-time bidding process will be explained in more detail below, to illustrate the efficiency of it compared to traditional human price bargaining;

When a website visitor clicks an URL, a request is sent to the ad exchange with their information such as related interests, if the individual visited the website before, etc. This visitor is then matched against the available advertisers and the system evaluates which advertisers’ willingness to pay (WTP) is the highest for this particular website visitor. A real-time auction takes place and the highest WTP or bid will be the advertisement that the website visitor sees when the webpage is loaded. The advertiser with the highest bid can hence show you their ad via the webpage, of which the corresponding webspace is supplied by a publisher on this ad exchange platform. This whole process happens in a few milliseconds and is based on data analysis and available information about each individual website visitor, whereas before advertisers would show their ads to all website visitors of a particular page, solely based on the fact that they thought these visitors would be interested in their respective product or service. Due to the implementation of real-time bidding, it is possible for your ad to show up on multiple different webpages of a particular ad network via the connection through ad exchange (Rask, 2021). If interested, the process of real-time bidding is described in more technical detail in the following picture.

The usage of real-time bidding in the advertising sector shows the impact of automation for digital marketing, given the usage of technologies as AI and algorithms to find the optimal advertising spot for advertisers. It can have a disruptive impact on this industry, since advertising is made more accessible for companies with a smaller budget, while the targeting is way more precise given the granular data that is included in the decision-making process during real-time bidding.

References

Rask, O. (2021, September 22). What is Programmatic Advertising? The Ultimate 2020 Guide. Match2One. Retrieved on 24-09-2021 at https://www.match2one.com/blog/what-is-programmatic-advertising/.

Fisher, L. (2019, April 25). US Programmatic Ad Spending Forecast 2019. Insider Intelligence eMarketeer. Retrieved on 25-09-2021 at https://www.emarketer.com/content/us-programmatic-ad-spending-forecast-2019.

Reagan, R. (2013, November 11). Turn Infographic: The Life of an Ad. ExchangeWire. Retrieved on 28-09-2021 at https://www.exchangewire.com/blog/2013/11/11/turn-infographic-the-life-of-an-ad/.

Photo Credits: https://www.hetdeventernieuws.nl/2018/09/21/online-marketing-van-ondernemingen-in-deventer-is-ondermaats/



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Cloud Computing and Machine Learning: The new racing formula

30

September

2021

5/5 (4)

In recent years, cloud computing and machine learning have been a focal point of discussion when talking about future directions of many different industries. The use of big data has the potential to revolutionise many different fields, yet there is one industry, which has not been given much attention, that started integrating these practices resulting in an immense leap forward: the racing industry, more specifically Formula 1 (F1).

In 2018, F1 partnered with Amazon Web Services (AWS) to redesign the sport by making use of all the data generated by the cars to improve both the fan experience as well as the car development. Just to give an idea of the potential these technological advancements have on F1, it is estamated that a modern F1 car contains on average 120 sensors generating approximately 1.1 million points of data telemetry per second.

In terms of improved fan experience, F1 is now able to provide real-time insights on races such as ‘battle forecasts’, ‘pit stop strategies’, and ‘tyre performance’ based on the wide range of available data such as weather, tyre compounds, timing, car position, telemetry, etc. Not only they are able to analyse strategies and predict battle outcomes, F1 is now able to analyse and display the driver’s performance and effort based on the 65 plus years of historical data available. In turn, the data can be used by the drivers themselves to compare their performance to the ones of their competitors in order to improve. Lastly, machine learning has re-defined the way in which teams develop new parts of their cars, leading them to more accurate and reliable results.

Figure: Example of real-time insight provided by AWS

All in all, I think that adopting of machine learning and cloud-based computing in areas, such as the racing world, re-enforces their potential. F1 has been known to be an old-fashioned sport, yet, now with the adoption of these new practices it has been able to revolutionise itself into a technology-driven environment. This could serve as a lesson and starting point for many other sports as well as industries to start adopting these tools in order to improve or even disrupt their current state. I am sure those improvements are the first of many and it will be really exciting to see what the next additions will be.

References:

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Digital Kick to the World’s Oldest Industry

30

September

2021

5/5 (3)

While the world is steadily changing pace and face every single day in terms of technology and advancements, we often forget to upgrade and update some of the most fundamental industries that hold the world together. These industries have been present since the presence of mankind. Agriculture, Supply Chain, Construction, etc industries are some of the world’d oldest industries, with a presence well beyond the historical data.


But at the same time, these industries are becoming one of the weakest links in our society and economy. This is because they cannot adapt to the changing environment, which is mainly led by digital transformations. This blog post will mainly focus on the new era of the digital transformation the construction industry is going to have through data.


The construction industry still operates just like in the olden days. The professionals and industry are still heavily dependent upon experience and rely on old methods of contract management, project management, etc for decision making. While this has been something that industry has used to run the projects, this type of management is no longer viable due to the rise of strict requirements such as time, sustainability, etc as well as costs rising due to wastage, inefficiency, etc.


On the other hand, the latest industry which is the digital industry has been able to tap into the potential of their latest information source: Data. Tech companies and many other industries have utilized the data to create new revenue streams and insight for more accurate and better decision making thus creating greater efficiencies in their processes. This is the something construction industry could not tap into but very soon this will change.


With tons and tons of historical data, Engineering and Construction (E&C) can firms can utilize their data in three main stages which are namely pre-construction, construction, and post-construction phases. The preconstruction stage mainly involves contracts, bidding, planning, etc of the project. The construction stage involves the construction, schedule management, safety, material consumption, etc. The post-construction stage involves quality assurance, repairs, and maintenance, etc.


In the preconstruction stage, data from previous projects can be used to draw information and insights. Information could be based on costs of the projects, timelines, material requirements, risks, bidding processes, etc. This can help firms gain advantage through insight and predictions on new projects contracts, costs, scheduling, etc. This ultimately helps the firms price the projects appropriately, schedule the projects in the right manner, have an early idea about the risks involved, etc, thus saving lots of time and money.


In the construction stage, digital tech has to mold itself to be adaptable to both physical works as well as digital works. Physical works would involve on-site execution such as construction, material consumption, labor management, etc and digital works would involve designing, project management, inventory control, etc. The time and money saved in this process are extremely important as a major portion of capital is being utilized in this stage. Every percent saved in money and time adds to a lot in the end.


Finally, in the postconstruction stage, data can help in quality assurance, maintenance, document management, etc. With several teams, contractors, etc are involved in a project, maintaining the quality of the project is a vital aspect to measure performance as well as to ensure the practices employed are up to standards. With data, quality can be measure, tested, and also can be used to mitigate potential problems wherever the quality is subpar. Maintenance is also an important step that many projects fail to consider. This step ensures the longevity of the project as well as ensures the safety of usage. With sensors, switches, etc coupled with data and software, monitoring of building health becomes very easy and efficient.

Resources:

https://www.mckinsey.com/business-functions/operations/our-insights/the-new-age-of-engineering-and-construction-technology

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Would you like to know whether it will rain in two hours’ time?

30

September

2021

5/5 (4) Recent research shows artificial intelligence can help us to predict the weather. The system is called the nowcasting system developed by Scientists at Google-owned London AI lab DeepMind and the University of Exeter partnered with the Met Office.

In a traditional way, we use complex equations to forecast for only between six hours and two weeks’ time. The main global issue – climate change makes it difficult to make more accurate and reliable predictions for the weather. Sudden changes in the weather are caused by climate change. The decrease in the temperature difference between the North Pole and the equator challenges our current methods to make accurate weather forecasts. This issue urged the meteorological institutes to develop their tools and methods to follow the changes.

So, there is a big problem that the traditional methods and their tools cannot provide the same performance for the society and community. If we cannot forecast critical storms and floods, it can have more dangerous catastrophic consequences. The recent news all around the world showed unexpected critical storms and floods can cause life losses. This can lead to bigger damage for the society, and to increase the financial costs.

Good news is that! The scientists claim that AI systems can be the solution for this problem! It can make more accurate short-term predictions, including for critical storms and floods.

The research, published in the journal Nature, claimed that meteorologists significantly preferred the AI approach to complete their methods. AI approaches can make the method more powerful by decreasing time for forecasters with predicting the continuous growing data. Instead they can spend more time focusing on gaining a better understanding of the implications for the forecasts. For this reason, the AI approach can help to mitigate the negative effects of climate change and prove better predictions for the people. So, it can potentially save lives!

Sources: https://www.bbc.com/news/technology-58748934
https://www.sciencedaily.com/releases/2019/03/190322105718.htm

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Google collects data in incognito mode. Is online privacy an illusion?

30

September

2021

5/5 (3)
The Illusion Of Privacy by Susan Spellman Cann

Google Chrome the most widely used browser in the U.S. since 2013 (Statista, 2021) is sued based on the claim that when a user uses Google’s incognito mode the company continues to track activity. According to the claimants this is a violation of privacy laws and a wiretapping.

The claimants claim that Google is secretly collecting information from people in incognito mode regarding where they browse online and what they view. This constitutes for many as a daunting fact and a break of trust since the misleading thing about it is that the user himself loses all the cookies and history after the tab is closed (which is of course the idea behind incognito mode) whilst while Google saves it all. Google has requested for the case to be trown out, which is understandable since the claim could be worth as much as $5 billion, luckily the judge did not comply with this request (Keach, 2021). Google has publicly responded claiming that it is clear for all users what happens in their incognito mode. Google spokesperson José Castañeda said, “We strongly dispute these claims and we will defend ourselves vigorously against them. Incognito mode in Chrome gives you the choice to browse the internet without your activity being saved to your browser or device. As we clearly state each time you open a new incognito tab, websites might be able to collect information about your browsing activity during your session.” (Roe, 2021).

Although I am shocked with this news it does not come entirely as a surprise. In my honest opinion I believe that for a regular internet user online privacy is an illusion. If a capable entity, like the government for example, wants to know or track a certain person they almost always succeed. Take for example the founder of the largest online illegal drug/weapons store SilkRoad Ross Ulbricht. Ross Ulbricht at the time a 29 year old computer science engineer was caught by the FBI after a long lasting man hunt even though he did almost everything possible to conceal his identity and location (CBS News, 2020). I do understand that one of the largest online criminals is rather different than Google tracking people in incognito mode but it is an illustration of the illusion of online privacy.

I fear that the use of the internet through the largest and most convenient suppliers of it always goes paired with possible invasion of privacy. The Google lawsuit seems to indicate that privacy is a relative term when technology is used as a medium. The question now is; should we as a society just take this for granted or is there something that we can do to secure our own online privacy?

In my opinion lawmakers should strike down severely on companies who see online privacy as a relative term by forcing them to guarantee online privacy or making them pay fines large enough to hurt the company and force them to change.

References:

Statista. (2021, 20 juli). U.S. desktop internet browsers market share 2015–2021. https://www.statista.com/statistics/272697/market-share-desktop-internet-browser-usa/

Keach, S. (2021, 15 maart). Google Incognito Mode ‘secretly scooped your data’ as $5BILLION lawsuit approved. . . The Sun. https://www.thesun.co.uk/tech/14342457/google-incognito-mode-privacy-collecting-data-secretly/

Roe, D. (2021, 23 maart). Why Incognito Browsing Data Is Not Really Incognito At All. CMSWire.Com. https://www.cmswire.com/information-management/why-incognito-browsing-data-is-not-really-incognito-at-all/

CBS News. (2020, 11 november). Inside the FBI’s search for Ross Ulbricht, dark web kingpin of Silk Road. https://www.cbsnews.com/news/ross-ulbricht-dread-pirate-roberts-silk-road-fbi/

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The next steps for banking: tokenizing (green) bonds

30

September

2021

5/5 (3)

This blog will discuss the potential of using blockchain and tokenizing (green) bonds for the banking industry. A green bond is similar to a normal bond, namely that it is a loan which periodically pays the owner interest. However, the proceeds of a green bond issue should solely be invested in projects that assist in mitigating climate change. The current (green) bond market is inefficient, which results in unnecessary costs (Shaikh & Zaka, 2018).  The market for green bonds specifically relies heavily on centralized third parties to provide trust (Malamas, Dasaklis, Arakelian, & Chondrokoukis, 2020). To illustrate how tokenization of (green) bonds will help the banking industry, this blog is structured as follows. To start, it provides a brief refresher on blockchain and tokenization. Secondly, it explains opportunities in the current system that can be improved. Thirdly, the blog discusses how to move towards a new system.

Blockchain can be defined as a distributed ledger, meaning all participants in the network own the same, continually updated, copy of the ledger. Furthermore, blockchain requires no intermediary for safely storing, recording and sending information on a peer-to-peer network. The information on the network is encrypted such that only the rightful owners to the information are able to access it. Tokenization can be defined as a method to digitize real-world assets into digital tokens that can be freely traded. Meanwhile, all transactions that are made with these tokens are safely recorded on the blockchain. Digital tokens are highly divisible, which means ownership of an asset can be shared between investors. More importantly, it could reduce minimum investment amounts of assets (Laurent, Chollet, Burke, & Seers, 2018). In short, blockchain is the main technology, tokenization of assets is an application of blockchain technology.

To illustrate the areas of opportunity of tokenizing (green) bonds, consider the example of dividend payments and bond ownership. Currently, dividend payments are not transferred automatically. However, dividend payments are predictable events, meaning programmers can write code that will automate such processes. Secondly, tracking ownership of (green) bonds is a long and difficult process due to the reliance on intermediaries to register the records of the (green) bonds. When a bank decides to call or redeem a bond (which means to pay back the loan earlier than the maturity date), it often issues an advertisement in newspapers as the bank does not know who owns their bonds (!). If (green) bonds are tokenized, ownership can be easily recorded on the blockchain. Furthermore, the immutable property of blockchain resolves the possibility of fraudulent activities (Nakamoto, 2008), such as trading a (green) bond ‘twice’.

To move towards the new structure, banks need to develop smart contracts. Smart contracts are in essence computer programs running on top of the blockchain which are able to automatically execute tasks when pre-determined conditions are met (Malamas et al., 2020). Smart contracts will be able to automate dividend payments and simplify auditability and reporting, because issuers of (green) bonds can put all documents on the ledger (Zhang, Aranguiz, Xu, Zhang, & Xu, 2018). A limitation of tokenization is the inherent incompatibility of blockchain technologywith GDPR regulation, as the ‘right to be forgotten’ is inherently violated, as once (personal) ownership data is stored, it will permanently remain on the blockchain.

To conclude, tokenization has potential to modernize the dividend payment process and ownership recording of (green) bonds. This would reduce costs for banks as the dividend payments will be automated and the search for owners of their bonds. There are more applications for banking but implementing these would be a good start for the (green) banking industry.

Laurent, P., Chollet, T., Burke, M., & Seers, T. (2018). The tokenization of assets is disrupting the financial industry. are you ready? Inside. Triannual insights from Deloitte(19), 62–67.

Malamas, V., Dasaklis, T., Arakelian, V., & Chondrokoukis, G. (2020). A block-chain framework for increased trust in green bonds issuance. Available at SSRN 3693638.

Nakamoto, S. (2008). Bitcoin whitepaper. URL: https://bitcoin. org/bitcoin.

Shaikh, S., & Zaka, F. (2018). Blockchained sukuk-financing. In International workshop on enterprise applications, markets and services in the finance industry (pp. 66–76).

Zhang, X., Aranguiz, M., Xu, D., Zhang, X., & Xu, X. (2018). Utilizing blockchain for better enforcement of green finance law and regulations. In Transforming climate finance and green investment with blockchains (pp. 289–301). Elsevier.

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How Facebook uses our information to make money

30

September

2021

5/5 (3)

Facebook is seen as the largest social media platform in the world. It was founded by Mark Zuckerberg in 2004, and it currently bolsters a monthly active user base of 2.85 billion users (Statista, 2021). It has since become one of the world’s wealthiest companies, and one may dare to ask, how can Facebook make so much money whilst not charging its users any fees. The answer to that partly lies in the data that Facebook has on us, and more specifically, the data we give Facebook about us. 

Facebook relies on data monetization to acquire additional revenues. The latter can be defined as the process whereby companies use their data to acquire additional revenues. Facebook has developed a natural feel for data, and data now plays a critical role in Facebook’s revenue strategy. The data that we provide to Facebook is of a certain value to Facebook, but it is of even greater value to third parties, such as advertisers and other brands. 

As opposed to what many people believe, Facebook does not simply sell the data that we as users provide to it (Gunnars, 2020) . Information relevant to our names, age, gender, preferences and so on remain within the Facebook database. Yet, this data is not merely stored for information purposes only, it is often used to satisfy the needs and requirements of third parties. 

If we take the example of a newly established fictive urban clothing brand called DreeX, the latter is seeking to increase its awareness in the hope of attracting new customers. It thus pays Facebook to promote their Facebook page and to place advertisements on user’s feeds, but not on any random user’s feed, but on a user who is potentially interested by this brand’s feed. How does one establish who that person may be?  Well, that’s when Facebook’s data plays its role. 

As user’s, anything that we like, comment, share and create on Facebook is stored and remembered in Facebook’s database. Patterns and trends can be assimilated following this information, and if the latter is deemed to match the visions of DreeX, then this user will be more likely to get DreeX advertisements in their feed. 

Therefore, it can be safely said that Facebook’s treatment of our data is how the brand makes money off our data.  In combination with a plentitude of advertisement requests, Facebook’s data strategy can be seen as if the company was sitting on a gold mine. That is why Facebook is far from being the only social media network to use such a strategy. Facebook’s Messenger and Instagram applications also use a variant of this strategy (Shead, 2019). Other new and arising social networks such as TikTok also monetise user data to procure additional revenue. 
Yet, one may wonder about the ethics that lay behind the usage of our data for the benefit of other companies. But that is another question that is an ongoing debate and will probably remain so for the years to come. 

References

  • Statista. (2021, September 10). Countries with the most Facebook users 2021. https://www.statista.com/statistics/268136/top-15-countries-based-on-number-of-facebook-users/
  • Gunnars, K. B. (2020, February 17). How Does Facebook Make Money? 7 Main Revenue Sources. Stock Analysis. https://stockanalysis.com/how-facebook-makes-money/
  • Shead, B. S. (2019, December 18). Facebook owns the four most downloaded apps of the decade. BBC News. https://www.bbc.com/news/technology-50838013

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