- 19/02/2018
- Category: Commentaries
INTRODUCTION
In an era where the vast majority of human activities are assisted by technology, we’re leaving a vast amount of digital trail that’s being mined into information. From social interactions, medical diagnoses to financial transactions, we’ve become contributors to what’s coined as ‘big data’. Big data is the exponential body of information that is stored on a network of ‘supercomputers’ (fast-running servers), also known as the ‘cloud’.[i] It’s collected through numerous means, such as online activity in forms of social media usage, search engine searches and even CAPTCHA tests. Not limited to online activity, it’s also collected from ‘The Internet of Things’, which is the data created from machinery and sensors that are connected through the internet—this includes surveillance cameras, manufacturing equipment and satellite images.[ii] Big data extracts value by transforming raw data into patterns, predictions and other insights.[iii] This is then sold to corporations or organizations for a number of uses. Sectors that mostly benefit from big data include marketing, manufacturing, product and service innovation as well as commerce.[iv] Here are some questions you might want to check out on big data.
Q1. Is it ethical for ‘big data’ companies to generate economic gain from the invasion of privacy?
In exchange for using the Internet and its services for free, we pay with a new form of currency: our privacy. Hirst coins the monetization of our personal data as the ‘surveillance economy’. We give the rights of our privacy to Internet services by clicking “Agree” to the terms and conditions, which is usually unavoidable for the use of these services. Not to mention it’s often an extensive and ambiguously worded document that the ordinary citizen won’t bother reading or can’t decipher. Thus, the ordinary citizens’ personal privacy is decreasing whilst the corporations’ secrecy is increasing.[v]
However, personal privacy is often overridden as big data can benefit research, security and public health. If we were to refrain from contributing to big data, we would hinder the efforts to combat issues like the improvement or innovation of life-saving medications. Privacy shouldn’t be denied, but rather recognized as a communal good. The goal for big data success should be the balance between private and public good. Data companies should also establish trust with users through algorithmic transparency, privacy-sensitive corporate policies and government regulations that are well publicized to digitally literate citizens.[vi]
Q2. How will big data affect the global economy in the future?
The continual flow of data has shifted the traditional paradigms of economy, becoming a form of digital currency itself. According to IDC, a market-research firm, by 2025 the ‘digital data universe’ will reach 180 zettabytes, which is 180 followed by 21 zeros. The big data economy utilizes a powerful strategy called the ‘data-network effect’, which is a cycle of the use of data to attract more users who will create more data, in attempt to ultimately improve services to attract even more users.[vii] With the information revolution, a large amount of analog data has become digital. Thus, users have become the producers of goods and/or services, an example of this is Wikipedia. However, users aren’t being compensated for their contribution, who are largely free digital labor.[viii]
There are two categories for technological innovations that affect the economy: one that replaces labor with machines and one that creates novel and complicated tasks for humans. So far, these two categories have existed simultaneously in balance, and technology-induced unemployment predictions have mostly proved to be untrue. However, this can change if data becomes cheaper than wages and the workforce lacks the skills required in a new economy.[ix] Although some propose this could be remedied with a digital labor reform, where users also financially profit from contributing towards big data.[x]
Q3. Is ‘thick data’ the new solution for ‘big data’?
Mian and Rosenthal remind us to pose this question regarding big data, are Internet users representative of the broader population? [xi] O’Neil also debates that we shouldn’t blindly trust big data. Algorithms can be wrong, and when it is, it erupts quietly and gradually. Algorithms can also be biased, as it tends to repeat past practices and reinforce the status quo. This is a form of private power that stifles the minority. Biased algorithms are caused by data laundering, where data isn’t representative of the population as a whole behind the disguise of objective data that is deemed as reputable by the general population.[xii] This automates discrimination in contexts of employment, housing, financial services and incrimination.[xiii]
The classic three ‘V’s of big data—volume, variety and velocity—now have two progenies, value and veracity.[xiv] As of 2016, big data is a $122 billion industry, however over 73% of big data isn’t profitable. Wang argues this is because big data lacks value. Our society has an embedded quantification bias, where we trust quantitative numbers to be more empirical. By choosing a select group of users to contribute more comprehensive data, instead of taking it from every internet users, data companies will be able to gain what Wang coins as ‘thick data’. ‘Thick data’ focuses on ethnographic and qualitative information, it portrays the human narrative, and ensures the minority are also part of the data.[xv]
CONCLUSION
Big data as capital is more complex, as the same data can be used by multiple corporations or organizations, which means that data can easily be misused. This also complicates the ownership of data.[xvi] Big data may also only benefit those with access to it, such as Internet giants (such as Google or Facebook) or the big players of the ‘Triple Helix’ (university–industry–government) model in developed economies. Thus, this creates new forms of digital divides, such as data divide, computational divide and interpretational divide.[xvii] [xviii]
Big data create promises for economical and social change that were unimaginable before, but we must be critical of using it appropriately. The use of big data requires the participation of everybody, not just corporations and governments, as users are simultaneously consumers and producers. With collective action, the current practices of big data can be improved.[xix] This paradigm shift is beneficial to an open society, however privacy must not be disrespected or exploited and the data contribution must benefit everybody, not just the majority.
Editor: Atin Prabandari, MA(IR)
Picture: Pexels
[i] Hirst, M. (2014). “Big Data” is Creating a Surveillance Economy. Issues, 109, pp.19–21.
[ii] The Economist (2018). Should internet firms pay for the data users currently give away?. [online] The Economist.
Available at: https://www.economist.com/news/finance-and-economics/21734390-and-new-paper-proposes-should-data-providers-unionise-should-internet [Accessed 22 Jan. 2018].
[iii] The Economist (2017). Data is giving rise to a new economy. [online] The Economist.
Available at: https://www.economist.com/news/briefing/21721634-how-it-shaping-up-data-giving-rise-new-economy [Accessed 22 Jan. 2018].
[iv] Wang, X., White, L. & Chen, X. (2015). Big data research for the knowledge economy: past, present, and future. Industrial Management & Data Systems, 115(9).
[v] Hirst, M. (2014).
[vi] Alen, A. (2016). Protecting one’s own privacy in a big data economy. Harvard Law Review, 130(71), pp. 71–78.
[vii] The Economist (2017).
[viii] TED. (2013). How data will transform business: Philip Evans. [Video online]
Available at: https://www.ted.com/talks/philip_evans_how_data_will_transform_business [Accessed 22 Jan. 2018]
[ix] The Economist (2018). Economists grapple with the future of the labour market. [online] The Economist.
Available at: https://www.economist.com/news/finance-and-economics/21734457-battle-between-techno-optimists-and-productivity-pessimists [Accessed 22 Jan. 2018]
[x] Alen, A. (2016).
[xi] Mian, A. & Rosenthal H. (2016). Introduction: Big Data in Political Economy. The Russell Sage Foundation Journal of the Social Sciences, 2(7), pp. 1–10.
[xii] TED. (2013). The era of blind faith in big data must end: Cathy O’Neill. [Video online]
Available at: https://www.ted.com/talks/cathy_o_neil_the_era_of_blind_faith_in_big_data_must_end [Accessed 22 Jan. 2018]
[xiii] Alen, A. (2016).
[xiv] Wang, X., White, L. & Chen, X. (2015).
[xv] TED. (2013). The human insights missing from big data: Tricia Wang. [Video online]
Available at: https://www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data [Accessed 22 Jan. 2018]
[xvi] The Economist (2017).
[xvii] Skoric, M. (2013). The implications of big data for developing and transitional economies: Extending the Triple Helix?. Scientometrics, 99, pp. 175–186.
[xviii] Ibid. “’Computational divide’: the disparity in access to technological resources needed for processing large datasets, including the bandwidth, computers and software. ‘Interpretational divide’: the gap between those who possess the experience and skills needed to make sense of the data and extract maximum value from it”.
[xix] Alen, A. (2016).
