Big data: critical questions for sport and society
In July 2015, IBM announced its collaboration with the large US pharmacy chain CVS Health. In this collaboration, IBMrsquo;s supercomputer Watson plays a central role. Using data from the 7800 drugstores owned by CVS Health, Watson cognitive computing will analyse an almost infinite quantity of medical information from medical records, insurance claims, environmental factors and smart fitness devices in order to monitor patientsrsquo; health. According to IBM, Watsonrsquo;s analyses will help to predict individuals lsquo;at riskrsquo; of declining health, encourage patients to adopt lsquo;healthy behavioursrsquo;, including adherence to prescribed medicines and healthy lifestyles, and suggest lsquo;appropriate use of cost-effective primary care and out-patient providersrsquo; (IBM, 2015IBM. (2015). CVS Health and IBM tap Watson to develop care management solutions for Chronic disease, 30 July 2015. Retrieved fromhttps://www-03.ibm.com/press/us/en/pressrelease/47400.wss. [Google Scholar]).
What could be called lsquo;Dr Watsonrsquo;s health detective agencyrsquo; is only one of many examples of big data analytics. Some observers even say that we live in the age of big data. The term lsquo;big datarsquo; has been variously defined. Broadly – and rather imprecisely – speaking, big data refers to high volumes of data, velocity of processing the data, and variety of data sources. The impact of the unprecedented and ever-increasing quantities of data being produced by computers and other modes of technology is far-ranging, to the point where it is now widely believed that big data will leave almost no sphere of life, business and governance untouched. Various aspects of our lives are recorded and monitored as digital data which are stored, aggregated, linked, analysed and traded. Big data is viewed as being able to change the way we think about measuring and making inferences about human behaviour by offering greater precision and predictive powers to improve government and business performances, for example, in the areas of healthcare, policing, urban planning and product design.
Ever since the best-selling book Moneyball by Lewis (2003Lewis, M. (2003). Moneyball: the art of winning an unfair game. New York: W.W. Norton. [Google Scholar]), the discourse of big data has also reached the sports industry as well as sport science. Sports organisations invest in the use of big data to enhance their business performance and decision-making in various ways. This process has been fast-forwarded by the proliferation of optical and device tracking systems in sports facilities in recent years. Most professional organisations and clubs in sports such as baseball, basketball, football (soccer), American football and tennis, have data analysts who crunch statistics and develop metrics on virtually every aspect of athletic performance. Universities have also begun to offer courses on big data analytics for sports.
Critical questions need to be asked about the nature, usefulness and ethical dimensions of big data. Big data discourses and practices have the power to change how we define knowledge and how we think about research (boyd amp; Crawford, 2011boyd, d., amp; Crawford, K. (2011). Critical questions for big data. Information, Communication amp; Society, 15, 662–679. doi: 10.1080/1369118X.2012.678878[Taylor amp; Francis Online], [Web of Science reg;], , [Google Scholar]; Lupton, 2015Lupton, D. (2015). Digital sociology. London: Routledge. [Google Scholar]). The production and use of big data are social, cultural and political processes that reveal a powerful doxa – a set of unstated, taken-for-granted assumptions regarding the higher form of knowledge that it supposedly generates. This includes the (often implicit) assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis (Lazer et al., 2014Lazer, D., Kennedy, R., King, G., amp; Vespignani, A. (2014). Big data. The parable of Google Flu: traps in big data analysis. Science, 343, 1203–1205. doi: 10.1126/science.1248506[Crossref], [Web of Science reg;], , lt;a href='http://scholar.google.com/scholar_lookup?hl=enamp;publication_year=2014amp;pages=1203-120
Big data: critical questions for sport and society
In July 2015, IBM announced its collaboration with the large US pharmacy chain CVS Health. In this collaboration, IBMrsquo;s supercomputer Watson plays a central role. Using data from the 7800 drugstores owned by CVS Health, Watson cognitive computing will analyse an almost infinite quantity of medical information from medical records, insurance claims, environmental factors and smart fitness devices in order to monitor patientsrsquo; health. According to IBM, Watsonrsquo;s analyses will help to predict individuals lsquo;at riskrsquo; of declining health, encourage patients to adopt lsquo;healthy behavioursrsquo;, including adherence to prescribed medicines and healthy lifestyles, and suggest lsquo;appropriate use of cost-effective primary care and out-patient providersrsquo; (IBM, 2015IBM. (2015). CVS Health and IBM tap Watson to develop care management solutions for Chronic disease, 30 July 2015. Retrieved fromhttps://www-03.ibm.com/press/us/en/pressrelease/47400.wss. [Google Scholar]).
What could be called lsquo;Dr Watsonrsquo;s health detective agencyrsquo; is only one of many examples of big data analytics. Some observers even say that we live in the age of big data. The term lsquo;big datarsquo; has been variously defined. Broadly – and rather imprecisely – speaking, big data refers to high volumes of data, velocity of processing the data, and variety of data sources. The impact of the unprecedented and ever-increasing quantities of data being produced by computers and other modes of technology is far-ranging, to the point where it is now widely believed that big data will leave almost no sphere of life, business and governance untouched. Various aspects of our lives are recorded and monitored as digital data which are stored, aggregated, linked, analysed and traded. Big data is viewed as being able to change the way we think about measuring and making inferences about human behaviour by offering greater precision and predictive powers to improve government and business performances, for example, in the areas of healthcare, policing, urban planning and product design.
Ever since the best-selling book Moneyball by Lewis (2003Lewis, M. (2003). Moneyball: the art of winning an unfair game. New York: W.W. Norton. [Google Scholar]), the discourse of big data has also reached the sports industry as well as sport science. Sports organisations invest in the use of big data to enhance their business performance and decision-making in various ways. This process has been fast-forwarded by the proliferation of optical and device tracking systems in sports facilities in recent years. Most professional organisations and clubs in sports such as baseball, basketball, football (soccer), American football and tennis, have data analysts who crunch statistics and develop metrics on virtually every aspect of athletic performance. Universities have also begun to offer courses on big data analytics for sports.
Critical questions need to be asked about the nature, usefulness and ethical dimensions of big data. Big data discourses and practices have the power to change how we define knowledge and how we think about research (boyd amp; Crawford, 2011boyd, d., amp; Crawford, K. (2011). Critical questions for big data. Information, Communication amp; Society, 15, 662–679. doi: 10.1080/1369118X.2012.678878[Taylor amp; Francis Online], [Web of Science reg;], , [Google Scholar]; Lupton, 2015Lupton, D. (2015). Digital sociology. London: Routledge. [Google Scholar]). The production and use of big data are social, cultural and political processes that reveal a powerful doxa – a set of unstated, taken-for-granted assumptions regarding the higher form of knowledge that it supposedly generates. This includes the (often implicit) assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis (Lazer et al., 2014Lazer, D., Kennedy, R., King, G., amp; Vespignani, A. (2014). Big data. The parable of Google Flu: traps in big data analysis. Science, 343, 1203–1205. doi: 10.1126/science.1248506[Crossref], [Web of Science reg;], ,
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