The time of Big Data has been activated with a big bang. Computer scientists, economists, physicists political scientists mathematicians, biologist, sociologists, and many other academic disciplines are demanding access to the massive amounts of information produced by our societies’ interactions indicating preferences and behaviors in the way they search for specific data.
Diverse groups debate about the potential benefits and costs of analyzing information from social sites such as Google Facebook Twitter, Wikipedia, and every other location where large groups of people leave digital traces as activity data. This significant amount of data can be used to help or hurt people depending how this information is used and analyzed.
In the recent past historians, psychologists, sociologists and anthropologists were the leading scholars with interest in social networks. At the time, data about personal relationships was collected through interviews surveys, observations, and investigation. Big Data introduces two new popular types of social networks derived from data analyses which contrasts the explicit (articulated) and implicit (behavioral) networks.
But, the big question has to be who gets access to this data for a specific application and where to place boundaries of legality and privacy. Today, the torrent of research using data sets from social media sources would suggest that access is available in a straightforward way. However, only social media companies have access to the data they collect, especially, financial transactions, like ordering, invoicing and paying for goods or services.
A social scientist working for Google or another giant data collector can have access to data that the rest of the academic community will not. These companies restrict access to their data entirely while others sell the access for a high fee and offer only small data sets to university-based researchers. The result is a multiple-tier-access-system restricted to those with money or those inside a data collecting company which can produce a different type of research than those on the outside who have no privileged access to the same data.
Given the rise of Big Data as a socio-technical phenomenon, it is critically important and a must ask question is—what the assumptions and biases of Big Data in this new emerging socio-technical culture with two diverging dispositions: A utopian view that this new technology can build social chains of understanding or a dystopian view of the same technology used for destruction of sociological progress and modernity.
Supporters argued that it will advance innovating research, but opponents challenge the premise as unpredictably and out of context data, with inadequate sourcing for accurate prediction or understanding of social science research of a mobile society. On the other hand, scientists need effective tools to measure big data and interpret errors and how these data points are being associated in context. Traditionally, these tasks are accomplished using trained research assistants or specific algorithmic applications. However, such approaches may not be viable when using massive but unfiltered data that can only increase skepticism. An alternative method is needed for validating data that may increase value for researchers when interpreting the significance of big data in a given context. Perhaps the solution is data expansion with particular emphasis on how it can be applied to tasks that will accelerate the acceptance of big data among scientists. For instance, expand data points into using automated coding verification of the academic discipline being researched; linking the Library of Congress catalogue with the existing literature of a subject matter; gathering as much validating data from colleges, universities and existing research banks. The costs and benefits of expanding big data to include verifiable resources will provide better guidelines and best practices for academic research using dependable information. Additionally, a standardized methodology should be developed that is accepted across the spectrum of all scientific research institutions around the world.
Notwithstanding, a boundary needs to be place between privacy scientific research and commercial interests when consumers enter personal data in a search box but have not been previously warned that the information entered will become part of the public domain and be sold to vendors and advertisers. Consumers are the owners of the data and must agree that any personal information entered on a personal transaction can be sold to commercial interests. Without proper privacy safeguards, the jury for Big Data is still out there waiting for legislators to act.
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