22.03.2018 || Research and Source Evaluation 3

In order to meet the deadline set in my project plan of completing research by 1/4/2018, I have been reading around my project title over the past week. I wrote up and evaluated each source immediately after reading/ watching, in order to overcome the potential problems I described in my plan, along with the full reference in case I use it in my project.


‘How big data enables economic harm to consumers, especially to low-income and other vulnerable sectors of the population.’

Newman, N. 2014. ‘How big data enables economic harm to consumers, especially to low-income and other vulnerable sectors of the population.’ [online] Available at: https://www.ftc.gov/system/files/documents/public_comments/2014/08/00015-92370.pdf [Accessed 18 March. 2018].

I found that this article provided a very useful, detailed insight into the specific impact of big data on low-income members of the population. Though the information is directly related to my hypothesis, I will need to research further into some of the case studies mentioned in this document, in order to confirm the legitimacy of them. As well as this, I will need to bear in mind that this particular article is from 2014, making it almost four years old now. Much regulation has changed over these years which means I will have to reconsider the likelihood of some of the issues raised in this document happening in the present day.

Despite the few issues with this source, I have found it to be enlightening and informative. I learnt about the concept of ‘algorithmic profiling’ through this article, particularly with relation to how it is often used to exploit vulnerable members in the population (i.e. low-income families) with ‘services’ such as payday loans and sub-prime mortgages. The lack of awareness about how our data can be used to generate an image of us that can be sold on to third-parties means that many people do not know that this happens, and leads to a huge asymmetry in information within the market which stems on to economic inequality. I found this article to be eye-opening and frankly quite shocking as I had no idea that some of the issues discussed existed! An example of this is price discrimination, which involves identifying a ‘pain point’ for consumers to tailor prices of good and services online to maximise profits. This source raises a number of points that support my hypothesis: that big data increases inequality within society. However, I will need to do more research, particularly in the way of gathering poverty-related statistics before I can come to a conclusion. As well as this, I will need to research into how data-handling online services are currently regulated to check whether the points raised in this article would still be valid to this day.


Big data in global health: improving health in low- and middle-income countries

Wyber, R. Vaillancourt, S. Perry, W. Mannava, P. Folaranmi, T. Anthony Celi, L. 2015. ‘Big data in global health: improving health in low- and middle-income countries’. Bulletin of the World Health Organization, [online] Volume 93(3), p. 133-208. Available at: http://www.who.int/bulletin/volumes/93/3/14-139022/en/ [Accessed 20 March. 2018].

This was an article from the World Health Organisation about the use of big data in health-care. This is not something I have come across in my research yet, and so I found this topic very interesting and it’s left me thinking about the potential of big data in health care in developing countries.

The writers discussed how there is potential for a huge amount of health information to be collected and used to offer more precise, personalised treatments and thus improve the health of the population as a whole. Analysing aggregated data can be used to inform public health policy as well as identify symptoms and monitor the general health of the population to make more efficient diagnoses and be more proactive in fighting disease. There are examples of this having been implemented successfully, such as in Durham county, North Carolina, where the pooling of metrics was used to identify where the risks of lead exposure were highest and thus tailor health care accordingly. Similarly, India’s introduction of Aadhar cards shows a commitment to improving the planning and delivery of health care by observing health statistics electronically.

This article outlined the many ways in which the implementation of big data could be hugely beneficial to developing countries which I would like to explore the potential of further in my essay. However, it did raise some problems that inevitably arise as a result, many of which crossed my mind as I read the article. For example, low-income countries tend to have unstable government structures and lax regulation which could lead to the unregulated use of personal data, potentially resulting in data breaches and therefore outrage and discrimination. Therefore there is a risk of these systems in fact dividing the population further, and giving control of public data to a select few private companies who can easily misuse it without regulation in place. The article identifies that it is essential there is legislation in place to combat this, but again, implementing this could pose a challenge, and the whole movement might prove counter-productive. The amount of money required is another big issue, and the question of whether it is worth spending on this.

I really enjoyed reading this article as it brought to light another use of big data that I had not previously considered that challenged my hypothesis. It also, however, pinpointed the problems that come with this supposed ‘solution’ to poverty, and I would like to explore the impact of using big data in healthcare on the vulnerable members of the population in terms of both its potential advantages and drawbacks. Following reading this article, I would like to research further into the idea of ‘data philanthropy’, which was touched upon on this extract. This is the idea of making anonymised public data accessible by anyone, which could lead to massive developments. Again, I want to evaluate the feasibility of solutions such as this, and the measures that will need to be taken to make these uses of big data thoroughly effective and not counterproductive.


Impact of Big Data & Artificial Intelligence on the Healthcare Industry!

Singularity Prosperity, 2017, Impact of Big Data & Artificial Intelligence on the Healthcare Industry!, [online video] Available at: https://www.youtube.com/watch?v=h6oljItZjxw [Accessed 21 March 2018]

Following my recent reading into the use of big data in the healthcare industry, I decided to look into a more recent source about its potential impact. One of the economic impacts of this growth in the use of analytics (across all industries) will be an increased demand for data scientists who are highly sought after already. Amongst other jobs, they make data interactive so that it is understandable by the common population. This visualisation can be tremendously useful in identifying patterns and connections in areas that seem separate. An example of this was provided in the video, in which a professor discussed how their research involved identifying the parts of the brain that are inactive. Based on this, these parts of the brain are stimulated by VR to prevent against Alzheimer’s disease.

With regards to healthcare, the use of health records can be used to predict and identify genetic diseases, which can help with primary intervention, and possibly cures. The more data points that are aggregated, the more accurate and effective the results (i.e. diagnoses and identification of disease). This is because information of genes, genomes from entire population can be used to tailor healthcare and find what works for a person on an individual basis. Therefore treatments can be personalised based on your location, physiology, social media etc. I think prevention could be key in stopping individuals falling into poverty as a result of spending on healthcare (in countries where it is not available free to the public).

One example of a data point could be fitness trackers which have rocketed in popularity over the past few years. This would be used in combination with individuals’ medical records to generate a thorough, detailed insight into their health. One of the professors in the video spoke about the potential of using social media as another source of information, which sounded bizarre to me! However upon reflection, I do see how this could provide a different perspective and alternative view into their thinking and lives. Naturally there will be privacy concerns with this, as there always will be in the big data sector but at the same time, the individuals who share their data will not only gain from it personally but will be contributing to a better healthcare system for the collective system. Further development could occur as a result of making databases open access, enabling more people to test new hypotheses which could be ground-breaking. I think I would like to evaluate the social gain against the cost in terms of privacy further in my essay.

Besides creating jobs in data science, the growth of big data will heighten demand for statisticians, computer scientists and with healthcare, scientists to research into the insights generated by the data collected.


The role of big data in medicine

Schadt, E (2015). The role of big data in medicine. [online] McKinsey & Company. Available at: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-role-of-big-data-in-medicine [Accessed 21 March. 2018].

Though not as relevant to my project title, this source helped me understand the economic impact of an introduction of big data into the health care industry. This article described the increased use of fitness trackers as a ‘wearable-device revolution’ which is expected to be refined over time to make data collection suitable for clinical research. Not all of the data will need to be actively logged in, but as I found from other sources, consumers have an incentive to participate as they should benefit from personalised, improved healthcare as a result.

I found that though it was informative and an interesting read, much of what I read in this article was similar to what I have already learned through my other research. Besides this, it focuses more on the supply-side of these data collecting devices and therefore talks about impacts on businesses rather than consumers, so it difficult to link to my project title which is more applicable to consumers. There is a link in that development in this sector should increase employment, but again, this would be limited to predominantly data scientists, computer scientists etc. and so is unlikely to have a significant impact on the poor, and those in low-skilled jobs.

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