Philosophising Data: A Critical Reflection On The ‘Hidden' Issues

Philosophising Data: A Critical Reflection On The ‘Hidden' Issues

Jackie Campbell, Victor Chang, Amin Hosseinian-Far
Copyright: © 2016 |Pages: 12
DOI: 10.4018/978-1-4666-9840-6.ch016
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This chapter aims to critically reflect on the processes, agendas and use of Big Data by presenting existing issues and problems in place and consolidating our points of views presented from different angles. This chapter also describes current practices of handling Big Data, including considerations of smaller scale data analysis and the use of data visualisation to improve business decisions and prediction of market trends. The chapter concludes that alongside any data collection, analysis and visualisation, the ‘researcher' should be fully aware of the limitations of the data, by considering the data from different perspectives, angles and lenses. Not only will this add the validation and validity of the data, but it will also provide a ‘thinking tool' by which to explore the data. Arguably providing the ‘human skill' required in a process apparently destined to be automated by machines and algorithms.
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2. Big Data: The New Oil Or Fools’ Gold?

There are some amazing data mining discoveries based on the leverage of Big Data; UPS used predictive analysis by monitoring and replacing specific parts they had saved on repair costs (FieldLogix, 2014). Deadly manhole explosions were predicted in New York (Ehrenberg, 2010). Walmart discovered that just before a hurricane, people in America bought an unusually large number of pop tarts (Hays, 2004). Data usually serves a purpose to prove or disprove hypotheses and theories, and excellent example of which is an investigation into the relationship between mobile phone usage and brain tumours (Frei et al, 2011). The Danish cell phone operators and health care service worked together to provide the data for a study into the relationship between brain tumours and cancer. The results showed no direct correlation. The benefits of adopting Big Data are as follows. First, the process of selecting data or population sampling is not required, as researchers can just take it ‘all’ and being persuaded to ignore data validity issues based on the ‘general trend’ provided by such a vast dataset. Second, Big Data processing can highlight the part of the datasets which reflects the core part of the problem. For example, medical data analysis can find the direct correlation between the lifestyle and genetic history with breast cancer.

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