Data Analytics

Shepherd, Ifan D. H. and Hearne, Gary ORCID: https://orcid.org/0000-0003-2146-4878 (2019) Data Analytics. In: Data in society: challenging statistics in an age of globalisation. Evans, Jeff and Southall, Humphrey, eds. Bristol University Press/Policy Press, UK, pp. 35-45. ISBN 9781447348221. [Book Section]

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Abstract

This chapter sets out to illustrate the dictum that there is (almost) nothing new under the sun. More specifically, its goal is to make the unfamiliar familiar within the field of data analytics. The need for such a treatment can be gauged from the plethora of terms currently vying for attention in the contemporary data analysis landscape, which can be puzzling even for seasoned researchers. These terms include: data mining, data science, data analytics, machine learning, deep learning, neural networks, and artificial intelligence. Hybrid terms such as ‘big data analytics’ are also emerging. As for the current front-runner term, data analytics, the evidence provided by the number of search engine hits reveals multiple competing versions subdivided by application domains, ranging from business analytics and crime analytics, to performance analytics, visual analytics, and many more. There is also an emerging software sub-industry providing tools for data analytics, many of which are named after the company which originally developed them.

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