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Big data for a smaller group

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Big data for a smaller group
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   El No (e.no@lse.ac.uk) 1 Discuss the implications of the significance of big data analysis for society. Big Data for A Smaller Group Introduction Big data and the relevant analytic techniques are receiving great attention. It is believed to have enormous potential benefits not only for profit maximization but also for social good.Thus, people are trying to get most out of big data. It is true that big data is generally improving many aspects of our lives. The benefits, however, do not seem to be  proportionately distributed. Rather, the threats to equality are often overshadowed by the significant benefits brought by big data. My question is about whether big data works for all as many believes. Hence, this essay will focus more on the inherent threats and its consequences. After reviewing the notion of big data and its positive impacts, it will move on to illustrating how big data can possibly reinforcing inequality by excluding certain group of  people. It then, will discuss similar imbalance and gap happening in institutions. The rise of big data as endless possibilities Big data is actually not a new concept.If the term merely refers to the size of dataset, there is no point to claim because a large amount of data has long been existed such as census data. Therefore, boyd and Crawford (2012, p. 663) notesthat big data is less about data, butpoints to capacity to search, aggregate, and cross-reference large data sets.During last decade, the sources of data have greatly diversified, in particular, the data about people generated by their  behavior (Marcus& Roger, 2013).What makes today ’ s big datadistinctive is that, it is the  people where the data come from. Thus, big data are considered topurely reflect the behavior and the thought of people. The benefits of big data have been captured by a variety of sectors. The agile players in  private sector have already started to hugely invest in Analytics. Many corporations have  brought it to decision-making process. Big data is a prospective tool for commercial advantage, specifically for marketing. In fast-changing media landscape, big data helps   El No (e.no@lse.ac.uk) 2 companies make more effective and efficient media buys and optimize their marketing  budget. Fur  ther, “narrow -casting ” strategy has become proliferated based on the tracking of consumers’ click and behavior on the net.Government agencies are trying to adopt big data to transportation, climate and security.Big data is also used for social good. Robert Kirkpatrick, the director of the UN global pulse  –   an initiative for using big data for development,says that “UN sees data as the greatest opportunity for relief and development that the world has ever seen.”  McKinsey Global Institute (2011, p.7) sees individuals - either as consumers or citizensas  beneficiaries of big data-related innovation. Because the use of big data can enable improved health services, savings on the time spent traveling and lower prices led by price transparency.There seem to be endless possibilities to harness big data for both productivity and social good. Growing threats toequality However, it should be questioned whether the beneficiaries are inclusive of whole society, and whether these benefits are allocated on the ground of equality. As shown in how other technologies worked in the history, they not only bring about improvement but also function as a means of divide. But it is often overlooked in the discourse of big data because people  believe it is about “ neutral ”  data. As a result, many believe that big data would bring positive changes for all. I would challenge this utopian belief. Despite the improved analytics technology, people on the periphery are likely to remain excluded. Moreover, big data exacerbate social and knowledge gap resulting in unequal decision-making. Exclusion in the stage of data creation and aggregation First, the nature of data creation and aggregation exclude certain communities. Many believe that the Internet provides people more democratized and egalitarian public sphere. To some extent, it is true, but when it comes to the production of data, the inequality becomes significant.Schradie (2011), in her research, examined a class-based inequality in creating online content. It is turned out that people with high school education are less capable of   El No (e.no@lse.ac.uk) 3 generating contents online. They are less contributing to the pool of online data than more educated population. Hence, she claims of a big data gap. Data are often assumed to accurately reflectreal world. US Library of Congress in the white  paper (2013)stresses that social media promises to be a rich resource that provides “a fuller  picture of today’s cultural norms, dialogue, trends and events to inform scholarship, the legislative process, new works of authorship, education and other purposes. ”  However, in fact, there should be a significant gap between the world constructed by big data and real social world.Worldwide, 39% of the population uses the internet (International Telecommunications Union, 2013). It means billions of people have no presence in the pool of big data. A world shaped bybig data is only for those who are able to generate electronically harvestable information. They can access the web via their own personal computers, use credit cards for purchase, and build relationship on social media. With the help of big data, the governments and other institutions will take their habits and preferences, while entirely overlook marginalized people from datasets (Lerman, 2013).Those people in the shadow of  big data overlap minority groups in real world, who are rarely heard and visible. It is  predicted that big data would reinforce and exacerbate the existinginequalities. Systemic omission of people happens, due to poverty, geography, life-stage, or life style. Therefore, it should be questioned that big data is representative of all society. If big data only take account into the people who regularly contribute to the right data flows, real world is reconstructed based on that. More prevalentbig data use will lead to wideningthe gap. Lerman (2013) warns that it intimidatespolitical and social equality by relegating vulnerable  people to an inferior status. Knowledge gap in the stage of analysis and interpretation Second, the sophistication of analytics privileges a small number of the experts who are able to read and manipulate the data. In the Manovich ’ s (2011, p.10-11) classification of people in realm of big data: “ those who create data; those who have the means to collect it; and those who have expertise to analyze it ” , the last group is apparently the smallest, and the most   El No (e.no@lse.ac.uk) 4  privileged. They also have got the authority to set the rules on how big data should be used. In this sense, skills and knowledge about big data are seen as “power”.  The techniques applied to big data are highly complex. It is a mix of pattern matching, statistical inference and other automated deductive algorithms.Thus, the resultsare hardly explained in an understandable manner to normal people(Marcus R., W.& Roger C., 2013). This implies tremendous possibilities that the results are manipulated according to the interest of certain group of people who have the power. As I witnessed how analytics firm worked at first hand for years,subjective decision-making is involved in every stage of the project, from the designing of model to interpretation of the results. Consultants  –   well educated data magicians, design a statistical model to solve their client’s specific issue, mostly, optimizing marketing budget.They decide which variable to include in their model based on significance and availability of data. After collecting the data from various sources, cleaning are done. Next, the experts play with data sets by leveraging the beauty of manipulation to find out the best matching. They can also “ customize ”  the results based on scientific methodology to please the decision makers. In many cases, big data is abused to support the decision already made as quantitative evidence, rather than the decision is made based on the analysis of data. Because there is a blind belief that data is objective and accurate, few people challenge the results from the big dataanalysis. Thus, in the same organization, only a few people can really understand how it works, while the rest remain blind. This shapes new power relations and further, engenders tension between the groups. If big data is controlled by small group, it would obviously be more difficult to include sufficient people in decision-making. Conclusion As big data gets more significance, the associated inequalities are increasingly emerging. Larger amount of data do not necessarily cover more people’s perspectives, neither reflects real society in more comprehensive view. Rather, a world shaped by big data seems to reproduce underrepresentation of minority in real world. Similarly, sophisticated techniques   El No (e.no@lse.ac.uk) 5 do not mean objective method.Due to the complexity of analysis, it is very possible that big data is controlled by smaller group of people making most people blind, and having decision-making process less transparent.I do acknowledge the great benefits out ofbig data, and expect that it would bring more drastic transformation. Nevertheless, the impacts would be limiting unless the lack of inclusion and equality are taken into account. Reference Bollier, D. (2010).The promise and peril of big data.The Aspen Institute.  boyd, d.& Crawford, K.(2012). Critical questions for big data.  Information, Communication & Society , 15:5.662-679. Crawford, K. (1 April 2013). The hidden biases in big data.Harvard Business Review Blog.http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/  International Telecommunications Union. (2013). The world in 2013: ICT Facts. Lerman, J. (2013). Big data and its exclusion. Stanford Law Review , 66, 55. 55-63. Library of Congress (2013).Update on the Twitter Archive at the Library of Congress. http://www.loc.gov/today/pr/2013/files/twitter_report_2013jan.pdf  Manovich, L. (2011). Trending: The Promises and the Challenges of Big Social Data. In Debates in the Digital Humanities, ed. M. K. Gold, The University of Minnesota Press, Minneapolis, MN.
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