Numbers > Number 16 > The use of big data in politics or the politics of big data
ISSN: 1885-365X
ARDINI, Claudia G. Contact 0000-0001-8106-4070

The use of big data in politics or the politics of big data

10 de diciembre de 2019
13 de diciembre de 2019


The exponential growth of social media, digital video and photography online, smart and connected devices, the massive collecting of data by corporations and governments, etc, are achieving a novel increase in the quantity of digital information on the planet. Big Data occupies today all the spaces in which life is resolved in our societies. For the last few years, it has come to define also the level of politics, deeply modifying the rules of the game and calling into question the legitimacy of democracy due to the boundless interference of Big Data in political processes, especially electoral ones. In this study, we refer briefly to the evolution of Big Data from its origins to present day, its incidence in the field of politics, and the possibilities, particularly in Latin American countries, to put a limit to the politics of Big Data, reclaiming politics through mobilization and social organization.


1. What is the Big Data

From the first writing techniques originated in the fourth millennium B.C. Born in Egypt, Mesopotamia and China to computer languages, the ability to condense, store and dispose of information was one of the constants in the evolutionary leaps of communication technologies.

Extracting Value From Chaos 2011, by John Gantz and David Reinsel, published by IDC magazine analyze the future and sponsored by the storage giant EMC corporation, predicted that by 2015 40 zettabytes of information would have been generated, equivalent to 57 times the number of grains of sand existing on all the beaches in the world.

The exponential growth of social networks, online digital videos and photos, smart and connected devices, which have massive use of mobile network services and, on the other hand, the greater capacity for data storage by corporations and governments, etc. . They are achieving an impressive increase in the amount of digital information on the planet and will be even more so in the future (Gantz and Reinsel, 2011).

While the concept of big data itself is relatively new, the origins of large data sets date back to the 1960s and 1970s, when the data world had just started with the first data centers and the development of relational databases. Around 2005, people began to realize the amount of data that users generated through Facebook, YouTube and other online services. That same year, Hadoop, an open source framework created specifically to store and analyze large data sets, would be developed. At this time, NoSQL would also begin to gain popularity. The development of open source frameworks, such as Hadoop (and, more recently, Spark), would be essential for the growth of big data, as they made data easier to use and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users continue to generate huge amounts of data, but humans are not the only ones now. With the advent of the Internet of Things (IoT), there are an increasing number of objects and devices connected to the Internet that generate data on customer usage patterns and product performance. The rise of machine learning and artificial intelligence have produced even more data. Although big data has come a long way, its usefulness begins now. Cloud computing has further expanded the possibilities of big data. The cloud offers truly flexible and unlimited scalability, where developers can simply streamline ad hoc clusters to test a subset of data.

There is no standard and unique definition of Big Data1, but we can say that its characterization is based on the diversity, complexity and volume of the data that is currently being generated, stored and analyzed, and on the necessary technological devices, both for its analysis as for its management. According to Tascón’s definition, «(…) it involves large volumes of information that are moved or analyzed at high speed and that can present complex variability in terms of the structure of its composition» (Tascón, 2013, p. 48) .

Enabled by technology, big data emerges as an element capable of promoting market research, based on massive CRM (Customer Relationship Management) data collection and management systems, to optimize customer management at unthinkable levels. The more data accumulated from customers, the greater the ability to design and generate customized marketing forms. Along with this colossal accumulation of data, geosemantics appears simultaneously, “as an examination and expectation of the cloak of sense in sight with the communicative and interactive means of social georeferencing” (Cerda Seguel, 2015).

This is possible from the development of information and geolocation layers carried out by Google, which identifies information about certain points on the planet and then accumulates data about those points. This geolocated accumulation of data can have different destinations. Thus, for Cerda Seguel there is a social value linked to the meaning built by the voices that speak of a certain place:

«Once a place is spoken (annotated, georeferenced) by users and opens to the interest of other individuals, communities, organizations and / or institutions, it can acquire a new role as a place, but it can also acquire new identities according to those who speak about said place; and in this act, these annotations are understood as “voices of the place”, that is to say: annotations that make a place speak. Social annotations are the food of constructions of meaning on digital maps, which can acquire aspects, dimensions and relevant territorial validity »(Cerda Seguel, 2015).

But these senses, built from georeferencing, are an extraordinary input of information for the corporations that manage this data. Thus, these technical devices configure big data as a possibility of managing human relationships and interactions. Big data provides information that allows understanding human behavior and, fundamentally, predicting that behavior. Hilbert Martin, a big data specialist, points out that «(…) with 150 likes, the automated learning algorithm can detect your personality and with 200 likes, it knows you more than your partner. And with 250 likes, he can know more about you than you do yourself ”(Landaluce, 2017).

From this exhaustive knowledge, the possibility of designing communication strategies that allow working with that knowledge about what people do, what expectations and desires they have, grows.

In a few years this market logic is transferred to politics. Market logics are assimilated to political dynamics. In the same way that marketing strategies are designed to position a brand or product, they begin to design candidacies and campaigns based on tastes, fears, expectations of identified and segmented consumers through information filters so that only the information reaches them. friendly and not the one that can cause any form of cognitive dissonance and therefore rejection of the information received. That same logic then reaches the governmental sphere in relation to the design of public policies. Test balloons2 are made to see if a certain policy works and has social acceptance or not and based on that its application is defined.

In this way, the logic of commercial marketing begins to order the field of political communication. Thus, it begins to be written in another key what throughout history has meant the ways in which societies seek to organize themselves to provide themselves with the best way, different in each time, to function, grow and evolve towards more sophisticated forms of organization: the politics. But this time, the key chosen for the resolution of the political, paradoxically, in its evolution, got rid of politics.

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