Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation

McCosker, Anthony; Wilken, Rowan

doi:10.1080/1472586x.2014.887268

Cited by 92 publications

(33 citation statements)

References 9 publications

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“…They can show evaluators and decision makers how an intervention worked, down to individual level if necessary, and offer 'productive possibilities' (McCosker and Wilken 2014) when they are viewed as 'door openers' (Gelman and Unwin 2013) or the 'means for generating understanding' (McCosker and Wilken 2014) rather than the end of statistical analyses. But they are not yet ready for evidence synthesis, where aggregated information from multiple studies of a similar phenomenon is necessary for policy decisions.…”

Section: Resultsmentioning

confidence: 99%

Impact Visualisation in Educational Interventions

Xiao¹,

Higgins²,

Kasim³

2017

Preprint

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Reporting of research data analysis often resorts to numerical summaries, such as effect size estimates in Randomised Controlled Trials (RCTs). Summary statistics are helpful and important for evidence synthesis and decision making. However, they can be unstable and inconsistent due to diversity in research designs and variability in analytical specifications. They also mask the dynamics of individual responses to a certain intervention by focusing on average treatment effect on the treated, even though the variation in impact may be crucial information for policy makers. To establish stability and consistency of impact estimates and to reveal the dynamics of individual responses in RCTs, we conduct variable selection, harness the power of noise, implement Cumulative Quantile Analysis (CQA), and devise umbrella plots of loss and gain in this study, using real datasets from over 30 educational interventions funded by the Education Endowment Foundation (EEF) in England. For the purpose of comparison, which is essential in data visualisation, all the aforementioned methods are built upon multiple analytical approaches. We show that the importance of an intervention can be ordered through variable selection, and that the power of noise or the bias induced by inappropriate variables, can be utilised to assess the stability of an impact estimate. We also demonstrate that estimates of average treatment effect cannot fully capture the impact of an intervention on sub-groups of participants with varying levels of attainment at baseline, not to mention individual responses to the intervention. Using CQA and umbrella plots, we are able to supplement what common effect size estimates lack in educational interventions. We argue that the impact of an intervention is often more complex than the average treatment effect suggests, and that until a summary is more informative and able to speak directly to the eye, evidence-based policy and practice cannot be fully achieved.

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Section: Resultsmentioning

confidence: 99%

Impact Visualisation in Educational Interventions

Xiao¹,

Higgins²,

Kasim³

2017

Preprint

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“…Zynga's waning fortunes and revenues are, in part, a reminder of McCosker and Wilken's (2014) important point that big data is far from self-explanatory, and that amassing gigantic data sets and figuring out how to analyse these for any purpose, including profitability, is still an extremely imprecise exercise. Regardless of success, though, the argument made here remains equally valid: companies whose business model relies on data mining and big data analytics are doing nothing intrinsically wrong if they are being transparent, or at least honest, about their operations, and allowing users or players to make an educated choice about whether to exchange the trail of their use and activity for free access to the game or service.…”

Section: Resultsmentioning

confidence: 99%

Zynga’s FarmVille, social games, and the ethics of big data mining

Willson

Leaver

2015

Communication Research and Practice

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The increasing necessity of engaging in social interaction through online commercial providers such as Facebook, alongside the ability of providers to extract, aggregate, analyse, and commercialise the data and metadata such activities produce, have attracted considerable attention amongst the media and academic commentators alike. While much of the attention has been focused on the data mining of social networking services such as Facebook, it is equally important to recognise the widespread adoption of large-scale data mining practices in a number of realms, including social games such as the well-known FarmVille and its sequels, created by Zynga. The implicit contract that the public who use these services necessarily engage in requires them to trade information about their friends, their likes, their desires, and their consumption habits in return for their participation in the service. This paper will critically explore the realm of social games utilising Zynga as a central example, with a view to examine the practices, politics, and ethics of data mining and the inherent social media contradiction. In determining whether this contradiction is accidental or purposeful, this paper will ask, in effect, whether Zynga and other big data miners behind social games are entrepreneurial heroes, more sinister FarmVillains, or whether it is possible at all to draw a line between the two? In doing so, Zynga's data mining approach and philosophy provide an important indicator about the broader integration of data analytics into a range of everyday activities.Keywords: social games; big data; data mining; Facebook; FarmVille; Zynga; social media IntroductionThe increasingly complex intermingling of online social interactions and data mining that take place through social media, web search, and online games necessitates further discussion and examination. Both activities -the increasing necessity of engaging in social interaction through online commercial providers such as Facebook, alongside the ability of providers to extract, aggregate, and commercialise the outcomes of such activities as a result of the data trail that is produced -have attracted considerable attention amongst the media and academic commentators alike. The implicit contract that the public who use these services necessarily engage in requires them to trade information about their friends, their likes, their desires, and their consumption habits in return for their participation in the service. The release of Facebook's Graph Search functionality makes this trade off quite explicit, demonstrating how multifaceted data mining of social interactions creates complex, useful, and importantly marketable information.

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“…If a phenomenon, as Manovich claims, is reduced to the scale of reason, what it is argued here is that he is simply referring back to the nature of the beautiful, not the anti-sublime. To further develop this rejection to Manovich's argument and articulate who precisely it is in the sublime where artistic data visualization has its most potential, a lengthy section of Chapter 1 is dedicated to revision key authors of the sublime (Acosta, 2012;Burke, 1757Burke, , 1796Heinrich, 2015;Kant & Goldthwait, 2003;McCosker & Wilken, 2014;Morley, 2010;Pseudo-Longinus, 1890) and drawing connections to artistic data representations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yet we arrive, resist to surrender reason entirely, and subjectively construct an understanding over the unbound. The relationship between data representation and the sublime are presented here through the analysis of authors that have defined our understanding of the sublime (Pseudo-Longinus, Burke, Kant, Deleuze), and those who had already made the connection between the term and issues of data representation (De Landa, 2000;Heinrich, 2015;Manovich, 2002;McCosker & Wilken, 2014). In the developing field of data representation, the understanding of the sublime requires a revision in light of the affordances of digital technologies.…”

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confidence: 99%

Data driven drawings

Jiménez¹,

Camilo²

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This practice led doctorate investigates the potential of artistic representations that express subjective views derived from traumatic personal experiences, using a series of impersonal datasets relating to acts of violence in Colombia as the point of origin. This investigation is led by a series of animated projects of my own creation in which I utilized data that contextually relates to deeply personal events that affected my life, seeking to build a defense for the subjective experience to permeate and inform the representation of data. These artistic projects seek to interrogate complex experiences that indicate unresolved states of mind in constant states of flux, leveraging the potential of data to be an index of phenomena with these characteristics. Relevant theoretical research that contextualizes these works has been conducted to elucidate contrasting arguments for the potential of representing data either as an expression of these complexities to allow for clearer reasoning or the opposing view that investigates the representation of the complex and the limitless as states where reason becomes forestalled. The first chapter contextualizes artistic data representation and examines the role of subjectivity in it. The second chapter proposes a connection between data visualization and the tradition of graphic notations in music and animation to study a point of intersection between functional data visualization and artistic practice. The third chapter examines in more detail the contrasting arguments for approaching data representation. Once the theoretical proposal for subjectivity in data representation is established, chapter 4 discussed my own practical work as an embodiment of it. Formally and aesthetically Data Driven Drawings investigate the possibilities and affordances of animation to develop temporal articulations of data, in order to explore the mutating flux of underlying themes that motivate artistic projects. The practice and theory are interwoven to create a single unified argument for the notion of Data Driven Drawings presented here.

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Rethinking ‘big data’ as visual knowledge: the sublime and the diagrammatic in data visualisation

Cited by 92 publications

References 9 publications

Impact Visualisation in Educational Interventions

Impact Visualisation in Educational Interventions

Zynga’s FarmVille, social games, and the ethics of big data mining

Data driven drawings

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