Big Data = No Privacy?

Is privacy no longer existing when companies are collecting big data. What, how and why is it collected and what is done to prevent it...

Written by Anna Bakalarska

(Krasting Bruce, 2012)

The other week I stumbled across the episode “Nosedive” of the TV-series Black Mirror. It depicted a morose picture of the future. A Social Media age gone wrong where everyone was ranked based on their actions and information available on them online; privacy wasn’t existing. A week after this I was met with the news that the Chinese government is collecting big data on its citizens in order to develop a social trust ranking (Clover, 2016). Today I was met with the news that UBER covered up a major security breach regarding big data collected and stored in the year of 2016 (BBC News, 2017). All of this got me thinking, is the morose future to some extent our present reality?

What is this “Big Data”?

When entering a website, I don’t reflect upon the consequences the “This web-page is using cookies, by continuing to visit this page you accept the use of cookies” has on my privacy. According to Aguirre et al. (2015) this is one of the primary ways that companies gain information and collect big data regarding what you are doing online. The development from Web 1.0 to 2.0 (Fournier & Avery, 2011) and the development of “the internet of things” enables more objects in our lives to have access to the internet which allows big data regarding us to be collected from more sources. More sensors are being incorporated into products in our homes which elevates the risk of getting spied on and threatens our privacy (Harvard Business Review, 2014).

Why Big Data?

The collection of big data enables more effective targeting where companies gain a better understanding of the customer needs and wants, which allows them to create better suited offerings (Nunan & Di Domenico, 2013; Tran, 2017). Advertising has due to the collection of big data evolved from being created in a standardized manner with a “one size fits all” approach to being personalized. Personalized advertising based on big data is the most effective and profitable type of advertising (Estrada-Jiménez et al., 2017). 48,4% of the Fortune 100 companies report using and achieving profitable results from investments in collection of big data (Bean, 2017).

Information collected on you as a customer is: clickstreams, browser history, habits (for example regarding what you buy), location, age, gender, ethical background and social interactions (Estrada-Jiménez et al., 2017). The big data collected can be classified in two ways; voluntary and involuntary. The voluntary data is collected by companies with your knowledge. It is big data generated by for example Netflix and Amazon which is used to generate content adapted to your preferences. Involuntary data is when companies collect and track personal information without your knowledge or consent and it has the biggest implication on our privacy (Christiansen, 2011). A terrifying fact is that if companies were to ask us for consent every time they collected big data it would take us approximately 2.5 times longer to do what we aim to do on the internet (Aguirre et al.,2015).

How is it collected?

(https://thenextweb.com, 2017)

Why is it collected?

The companies examined in How Companies Say They’re Using Big Data say that the access of information about customers enable them to create refined products that meet the needs and wants of their target customers (Bean, 2017).

Research has shown that customers who are reached with personalized advertising are more prone to purchase the product which makes personalized marketing based on big data the most profitable type of advertising (Estrada-Jiménez et al., 2017). This however is argued to be half of the truth. Personalized targeted advertising is good when customers aren’t aware of the fact that it has been generated based on their likings but when this personalization becomes obvious it creates a feeling of vulnerability, no privacy and reactance (Aguirre et al.,2015). This feeling is based on the human being wanting to have as much freedom as possible, and when we feel that the freedom being threatened, in this case privacy, a feeling of discomfort is experienced (Aronsson, 2012). With these findings in mind, targeted marketing based upon big data seems to be a double-edged sword as it does create advertising and offerings better suited to the target audience, but it needs to be done in a refined manner so it doesn’t become transparent which can have consequences for our privacy.

Privacy not the only issue

Have you ever experienced that your flight prices differ from your friends? This phenomenon is caused by data collection and the use of cookies (Lesk, 2013). There are several issues and dangers with the collection of big data that we need to be aware of. We usually don’t read through the privacy policies that pop up on websites we visit. There is a general lack of awareness and we don’t reflect upon what data is being collected and the implications it can have on our lives long term (Christiansen, 2011; Martin & Murphy, 2016). Companies don’t always inform us about big data being collected because it allows it to be unbiased which gives a more truthful insight to the customer behavior and allows a more accurate offering to be developed (Aguirre et al.,2015). Many companies prefer keeping their customers in the dark regarding the big data collected, and rather ask for forgiveness when something goes wrong than for permission beforehand (Morey, Forbath & Schoop, 2017).

The big data collected is by many thought to be anonymous but anonymization is said to be doomed with the extensive amount of data available. It is impossible to anonymize it in an effective way (Berinato, 2015; Acquisti et al., 2011). This lack of anonymity becomes a problem for our privacy; because without anonymity, there is no privacy. Other issues related with the collection and storage of big data are racism, sorting of people based on their social class, fraud, exclusion, security breaches and identity theft (Estrada-Jiménez et al., 2017).  And with data collected there is a danger of data breaches where your information can be leaked. Another danger with the personalized content based on big data is the phenomenon of the “filter bubble” where you only are met with information adapted to what you already know which makes you unaware of things outside of this scope (Lesk, 2013).

What can be done?

Measures to ensure data security and the preservation of our privacy are being taken, one is the encryption and anonymization of big data. Berinato (2015) however argues that these measures aren’t sufficient as it has been shown that hackers still are able to identify the person behind the data through advanced techniques and the vast amount of data available. To protect yourself blocking tools can be downloaded, the most known one is AdBlock (Estrada-Jiménez et al., 2017).

Companies need to be transparent regarding the collection of big data as there is no way for people to disable this collection or ad-personalization and it has effects on our privacy (Estrada-Jiménez et al., 2017). Laws on data protection in the EU are being revised and say they that data shouldn’t be stored for “longer time than needed” and a debate regarding the right to be forgotten has risen. Data protection can be profitable. This, because it long-term will increase trust from customers, a feeling of retained privacy and security breaches will not be an issue that can affect the brand negatively (Schneider et al, 2015). In the future, gaining consumer trust is said to be the key for a good customer relationship. Giving customers the choice when and what big data is collected, showing transparency and giving a value in return for the collection of big data will be the key to success (Morey, Forbath & Schoop, 2017).

The reality is that big data to some extent is eliminating privacy as we are used to it. We need to be aware of the consequences and think of when we click “accept” when entering websites. As security and privacy become valuable assets for company reputation, the involuntary collection of big data hopefully soon will be stopped. But, until then there is some truth to our privacy being threatened because of the collection of big data.

References

Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K. and Wetzels, M. (2015). Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. Journal of Retailing, 91(1), pp.34-49.

Aronsson, E., ()2012. The Social Animal. 10th ed. New York: Worth Publishers

BBC News. (2017). Uber concealed huge data breach. [online] Available at: http://www.bbc.com/news/technology-42075306  [Accessed 22 Nov. 2017].

Bean, R. (2017). How Companies Say They’re Using Big Data. [online] Harvard Business Review. Available at: https://hbr.org/2017/04/how-companies-say-theyre-using-big-data  [Accessed 12 Nov. 2017].

Berinato, S. (2017). There’s No Such Thing as Anonymous Data. [online] Harvard Business Review. Available at: https://hbr.org/2015/02/theres-no-such-thing-as-anonymous-data?referral=03759&cm_vc=rr_item_page.bottom  [Accessed 11 Nov. 2017].

Christiansen, L. (2011). Personal privacy and Internet marketing: An impossible conflict or a marriage made in heaven?. Business Horizons, 54(6), pp.509-514.

Clover, C. (2016). China: When big data meets big brother. Financial Times. [online] Available at: https://www.ft.com/content/b5b13a5e-b847-11e5-b151-8e15c9a029fb  [Accessed 22 Nov. 2017].

Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A. and Forné, J. (2017). Online advertising: Analysis of privacy threats and protection approaches. Computer Communications, 100, pp.32-51.

Fournier, S. and Avery, J. (2011). The uninvited brand. Business Horizons, 54(3), pp.193-207.

Krasting, B. (2012). banksy-graffiti-street-art-what-are-you-looking-at. [image] Available at: https://www.flickr.com/photos/bruce_krasting/6857656852  [Accessed 22 Nov. 2017].

Lesk, M. (2013). Big Data, Big Brother, Big Money. IEEE Security & Privacy, 11(4), pp.85-89.

Martin, K. and Murphy, P. (2016). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), pp.135-155.

Morey, T., Forbath, T. and Schoop, A. (2017). Customer Data: Designing for Transparency and Trust. [online] Harvard Business Review. Available at: https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust  [Accessed 13 Nov. 2017].

Nunan, D. and Di Domenico, M. (2013). Market research and the ethics of big data. International Journal of Market Research, 55(4), p.505.

Schneider, M., Jagpal, S., Gupta, S., Li, S. and Yu, Y. (2017). Protecting customer privacy when marketing with second-party data. International Journal of Research in Marketing, 34(3), pp.593-603.

Tran, T. (2017). Personalized ads on Facebook: An effective marketing tool for online marketers. Journal of Retailing and Consumer Services, 39, pp.230-242.

The Next Web. (2017). TNW – International technology news, business and culture. [online] Available at: https://thenextweb.com [Accessed 22 Nov. 2017].

Harvard Business Review. (2014). With Big Data Comes Big Responsibility. [ONLINE] Available at: https://hbr.org/2014/11/with-big-data-comes-big-responsibility . [Accessed 15 November 2017].

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