Big Data: Blessing or Curse? Part 2

Source: Vishal Krishna/ (27 June 2016)

Big Data are mostly discussed for two reasons. The first reason is whether those data that include personal information are safe or not. The second one is about how the data that are provided are used.

To begin with, I need to clarify what I am talking about when I use the term Big Data. CERN also uses Big Data but with another meaning for example. In this post I refer to the specific kind of Big Data that include private information and are mostly generated by humans through social media platforms or records of mobile phone lines.

Regarding the first main reason of criticism that I mentioned above, we all know that at least partly of all the information that are available on Facebook are accessible to corporations. This can be easily understood by seeing the way in which Facebook advertisements function. There is targeted advertising based on a variety of possible data. Usually in the social media context, the information that are datafied are deeply personal. Friendships, relationships and experiences are turned into data (Mayer-Schönberger & Cukier, 2013) and corporations have access to those data in order to spot the market fragment that they target. On top of that, Facebook constantly expands with new features (Meikle, 2016). As a result we share more information and we consequently become more exposed.

Besides the commercial use of personal information, another data security problem is information leak. On one occasion Facebook had to admit that 6 million users had personal information exposed. If we take into consideration that Facebook did admit this very late, it is normal to assume that similar events may have happened in the past on social media platforms and users just never got informed.

The other main problem that we face with social media, is our inability to use properly the mass of data produced through them. From a humanitarian point of view, it is hard to know whether a post on social media is accurate or not. As Meier (2015) describes, when there is an indication that there is need for help somewhere, it is hard to validate whether this is true or not. Taking also into consideration how many those indications may be, it becomes even harder to validate them all in a short period of time, if needed.

This takes us to another dimension of the same problem which is our inability to analyse the data that we have. There is a huge discussion on whether or not private data should be used even in humanitarian occasions. The problem is that, even if researchers had full access to Big Data, the problem would not be solved. The existing need is not necessarily for more data but for more relative data (Taylor & Schroeder, 2015) that will be targeting every time the existing needs.


  • Mayer-Schönberger, V. and Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.
  • Meier, P. (2015). Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.
  • Meikle, G. (2016). Social Media: Communication, Sharing and Visibility. New York: Routledge.
  • Taylor, L. and Schroeder, R. (2015). Is bigger better? The emergence of big data as tool for international development policy. GeoJournal, 80: 503-528.

3 thoughts on “Big Data: Blessing or Curse? Part 2”

  1. Great post – thank you. It would be interesting to look at the use of other data sources in the history of development. Big Data for me isn’t something as radically new as it is sometimes claimed to be. The volume, velocity and variety of data are surely new, but the application of data to development has a long and troubled history as William Easterly makes clear in his book, The Tyranny of Experts. More data requires more analysts, better controls, global ethics to do with data privacy. But even if that is sorted out, there is a worry that it just provides a better tool in an outdated development paradigm. What is more interesting perhaps, is to look at how and why data is used in a development context and whether a new source of big data requires a corresponding new approach to development itself. As Tim Harfood wrote, ““Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever.”

    I’m definitely a skeptic (!), but the signs are worrying – take mPesa in Kenya – once heralded as a money solution in Kenya, and now at the centre of a privacy and control row, as the government has ordered telecom companies to hand over the huge data sets to a third party private company outside of any due governance. For what ends? They claim it is to monitor tax evasion.

    William Davies concludes his article on “How Statistics lost their power – and why we should fear what comes next” with the observation that, “the battle that will need to be waged in the long term is not between an elite-led politics of facts versus a populist politics of feeling. It is between those still committed to public knowledge and public argument and those who profit from the ongoing disintegration of those things.”

  2. My initial though when I read the 2-part blog on Big Data was; “with big data comes big responsibility”, something that is said more and more frequently now days.
    The ability of companies and governments collect, store and analyse massive amounts of data on us, means that more than ever before, means that more than ever before, data is playing a bigger role than ever in influencing almost every aspect of our lives. In fact, what isn’t touched upon and could make for interesting angle, is the power dynamic of big data ownership, for example, Amazon can “own” vast amount of data on us as individuals, and can use that data for profit, but it can also be legally compelled by the US government for example, to hand that data over, but what data is handed over is decided by a court, so who really has the power in that dynamic?
    The post makes a very important point as well about the issue of capacity. The capacity gap and the fundamental inability of decisions makers as well as ordinary citizens to make use of data means that with all its promise to increase impact of development and humanitarian intervention, Big Data can only realise its potential when the unintended consequences, ethical aspects, implications for equality and privacy issues are tackled.

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