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As accurate as it is, talking about how vast amounts of data can help solve most of the world’s pressing issues has become a cliché. This, however, does not mean that the importance of big data in solving these problems should be overlooked. Big data is helping in solving many problems more efficiently than before. In many industries, data is being leveraged alongside the advances in data science and the tremendous growth in computing power to solve specific problems fast and effectively. In industries where people have been struggling to make meaning out of vast amounts of text data, for example, machine learning can be used to identify patterns hidden in this data that would have otherwise have gone unnoticed.

The use of rigorous computational methods together with ready-made and custom-made data to provide solutions for problems is called computational science. This type of application has been in use in industries such as technology and manufacturing, among others, but had not found its way in other industries. The social science realm is one of the areas that for long remained in the dark. However, this area is now increasingly embracing big data and analytics to improve operations. Big data is bridging the gap for researchers and other people in this area by bringing content expertise. These aspects give analysts and academics tools to use powerful data analytics in research.

With the right tools, computational scientists are now exploring vast sets of data to extract meaning from the text and other forms of data that is exchanged in today’s society. They are now able to track the online activities of people, explore digital books and other historical documents. Additionally, they can harvest data from wearable devices that record people’s daily activities and contacts. From massive data such as the one collected from online surveys and experiments, secrets that exist about the society can be extracted with the help of big data analysis tools.

Over the past few years, researchers have used these methods to identify topics that have eluded social scientists for many years. A topic such as foundations of human morality and misinformation, as well as aspects that make some people successful than others, have all benefited from the advancements in big data and analytics. For instance, a study carried out in Rwanda uncovered an extensive spread of racism in algorithms that are used to make critical healthcare decisions. The big data algorithms used mobile phone data to map under-developed regions in Rwanda.

Another example is in the US, where public health and behavioral economics researchers in 2019 used data from healthcare records of more than 50,000 across the US to analyze algorithms that recommend people that have complex healthcare needs for enhanced supervision. The results of the modeling showed that the algorithm was discriminating against black people and therefore affected how people of African descent.  The knowledge of such disparity helped to trace the source of bias and find ways of addressing them.

While there has been some adoption of this big data in social sciences, not everyone has embraced it. Some social scientists still prefer the traditional methods in carrying out the research. Unfortunately, most of the traditional methods are insufficient in an era where the speed with which data is generated has increased, and the demand for delivery of results has more than doubled.

Big data is indeed helpful in the social sciences. However, researchers must look and, at the same time, consider causes before concluding certain topics from incomplete and poorly arranged data mainly from social media platforms and other sources. This ensures that conclusions are well-grounded and helpful.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief


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