When reading the material concerning big data, most of the time it seems very polarized. It is either a dangerous and dystopian image that is presented (O’Neil, 2016), or, as Ilario wrote in a previous post, almost Evangelical in which big data and algorithms will be our saviour as in Krings Ted Talk.
According to Spratt and Baker (2016), one of the defining features of big data is the ability to synthesize different data sets in ways that were not previously possible. They mention the possibility of using remotely sensed and crowd sourced data to ‘map’ problems of many types, such as tracking and modelling the spread of malaria, and that is exactly the theme in the Ted Talk How telecom data can radically change the way development aid works by Gautier Krings. He explains how we, to be able to reach the SDG:s by 2030, need to change and improve how we work with development. According to Krings, the basis for development workers and policy makers to function properly is good and accurate information, something he sees as lacking in traditional data collection.
In a recent article called “Does Trump’s Rise Mean Liberalism’s End?” in The New Yorker, Yuval Noah Harari claims that in the wake of the collapsing “Liberal Story”, no new story has taken its place and as a consequence we get Donald Trump. This happens due to the disillusion among Americans after having believed in the promises and assurances presented in the liberal dream which claims that “if we only liberalize and globalize our political and economic systems, we will produce paradise on earth, or at least peace and prosperity for all. According to this story (…) humankind is inevitably marching toward a global society of free markets and democratic politics.”
After having read the literature assigned to us in this course, I feel like I have a somewhat unclear reflection of the concept and critique of big data, especially in regards to the concerns discussed related to the risk of discrimination, bias, the consequences and risks presented by a ´digital divide’ and a lack of statistical certainty and accuracy (Spratt & Baker 2016).