Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order (...) to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in (...) the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission—aiding infection control measures and policies—emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: automation and algorithmic reliance impacting freedom of choice, big data analytics complexity impacting informed consent, reliance on profiling impacting individual and group identities and justice/fair access and increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement. (shrink)

In this article, we show how it is possible to lawfully buy an election. The method we describe for buying an election is novel. The key things that make it possible to buy an election are the existence of public voter registration lists where one can see whether a given elector has voted in a particular election, and the existence of Big Data Analytics that with a high degree of accuracy can predict what a given elector will vote (...) in an upcoming election. Someone interested in buying an election can enter an employment contract with all, or some of, the opponent electors where these electors are paid to do a job that prevents them from voting. By purchasing access to the public voter registration lists, it is possible to verify ex-post whether the opponent electors that one has signed a contract with have abstained. In the last two sections of the article, we discuss several barriers that can undermine an attempt to buy an election in the manner we identify. (shrink)

Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision-making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey’s five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of (...) manipulative power to initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery). It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness-to-pay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry. (shrink)

Currently, most studies are moving towards Big Data Analytics because they are important in research, and this is becoming increasingly important as Internet and Web 2.0 technologies become increasingly popular and how to handle this massive data. Moreover, this proliferation of the Internet and social media has revolutionized the search process. With this Big Data of data generated by users using social media or electronic platforms, the use of these details and daily activities is integrated (...) with tools designed for analysis. The topic of analyzing big social media will be discussed and an intensive explanation will be given to the topic of Big Data. This paper compares Big Data analysis techniques using several methods of analysis, the first technique using neural networks and the second technique using data clustering. The purpose of this study is to infer the ages that use social media and what are their interests in writing and in the end, who are the most widely used social media males or females. (shrink)

Although big data analytics capability leads to competitive performance, the mechanism of the relationship is still unclear. To narrow the research gap, this paper investigates the mediating roles of two forms of resource integration in the relationship between two forms of BDAC [big data analytics management capability and BDA technology capability] and competitive performance. Supported by Partial Least Squares-Structural Equation Modeling and the cross-sectional survey data from 219 Chinese enterprises, the results show that the resource (...) bricolage plays a significantly mediating role in the relationships between BDA management capability and competitive performance as well as in the relationship between BDA technology capability and competitive performance. Furthermore, the mediating effect in the former relationship is stronger than that in the latter relationship. Additionally, BDA technology capability only has a direct effect on resource bricolage, while BDA management capability has a stronger effect on resource optimization than that on resource bricolage. Finally, resource bricolage has a stronger impact on competitive performance than resource optimization. These findings contribute to understanding how enterprises could apply different forms of BDAC to other kinds of resource integration to achieve outstanding competitive performance. (shrink)

As organizations are benefiting from investments in big data analytics capabilities building and education, our study has analyzed the impact of big data analytics capabilities building and education on business model innovation. It has also assessed technological orientation and employee creativity as mediating and moderating variables. Questionnaire data from 499 managers at enterprises in Jiangsu, China have been analyzed using Structural Equation Modeling in SmartPLS. Big data analytics capabilities building and education strengthen technological (...) orientation and increase business model innovation. Technology orientation increases business model innovation and plays a mediating role. Employee creativity also boosts innovation. These findings show that business managers should adopt and promote a technological orientation. They should hire and train employees with big data education and training. Organizations can try to select and support employees who show creativity. (shrink)

Much bioethical literature and policy guidances for big data analytics in biomedical research emphasize the importance of trust. It is essential that potential participants trust so they will allow their data to be used to further research. However, comparatively, little guidance is offered as to what trustworthy oversight mechanisms are, or how policy should support them, as data are collected, shared, and used. Generally, “trust” is not characterized well enough, or meaningfully enough, for the term to (...) be systematically applied in policy development. Yet points made in the philosophical literature on trust can help. They allow us, not only to better distinguish the different ways the term “trust” may be interpreted, but also to better determine how different approaches to trust can align with policy and governance—in what ways they may relate to key bioethical concepts and related laws, and in what ways they can help to balance individual and group interests in data sharing. This article draws from the philosophical literature on trust to identify a relationship among consent, trust, and justice. Specifically, parallels are drawn between “character-trustworthiness” and “natural justice,” a set of widely held legal safeguards intended to ensure decision-makers follow a pattern of procedural fairness which protects the rights of the individual and thereby maintains public confidence in the decision-making process. Relevance to traditional bioethical principles, established laws, and consent procedures are addressed throughout. In conclusion, policy actions are suggested. (shrink)

With the development of network technology, enterprises face the explosive growth of data every day. Therefore, to fully mine the value of massive data, big data analysis technology has become the key to developing the core competitiveness of enterprises. However, few empirical studies have investigated the influencing mechanism of the BDA capability of an enterprise on its operational performance. To fill this gap, this study explores how BDA technology capability influences enterprise operation performance, based on dynamic capabilities (...) theory and resource-based theory. It proposes the key variables, including the connectivity, compatibility, and modularization of big data analysis technical capability, enterprise's operational performance, and the fit between data and tools, to establish a model and study the correlation between the variables. The results highlight the mediating role of data-tool fit in the relationships between BDA capability and the enterprise's operational performance, which is a major finding that has not been underlined in the extant literature. This study provides valuable insight for operational managers to help them in mobilizing BDA capability for enterprises' operational management and improving operational performance. (shrink)

This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an (...) epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics. (shrink)

Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that (...) does not necessarily align with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms. (shrink)

This paper offers a sketch of the complicated conflicts which arise—and metastasize seemingly daily—in the era of Big Data. Given the public’s ubiquitous-yet-ostensibly-voluntary data surrender, and industry’s ubiquitous-yet-ostensibly-anodyne collection of the same, inaction is not an option for any near-just society. By revisiting the philosophical basis for Panoptic apparatus, sketching the tumultuous history of US contract law trying to protect the public from itself, and comparing existing industry codes for similarly-situated—read: terrifyingly invasive—fields, the paper will provide a preliminary (...) framework for identifying and confronting the galaxy of problems associated with data analytics. (shrink)

This study aims to explore the role of digital education in the development of skills and employability for engineering students through researching the role of big data analytics courses. The empirical study proposes the hypothesis that both soft and hard skills have positive effects on human capital, individual attributes, and the career development dimensions of engineering students. This is achieved through constructing a framework of three dimensions of engineering students’ employability and two competency development dimensions of big (...) class='Hi'>data analytics courses. A questionnaire survey was conducted with 155 college engineering students and a structural equation model was used to test the hypotheses. The results found that courses on big data analytics have a positive impact on engineering students’ abilities in both hard skills and soft skills dimensions, while soft skills have a more significant impact on engineering students’ employability. The study has practical and theoretical implications that further enriches the knowledge base on engineering education and broadens our understanding of the role of digitalization in enhancing the skills and employability of engineering students. (shrink)

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence. Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as (...) Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted. (shrink)

Social media posts are full of potential for data mining and analysis. Recognizing this potential, platform providers increasingly restrict free access to such data. This shift provides new challenges for social scientists and other non-profit researchers who seek to analyze public posts with a purpose of better understanding human interaction and improving the human condition. This paper seeks to outline some of the recent changes in social media data analysis, with a focus on Twitter, specifically. Using Twitter (...) data from a 24-hour period following The Sisters in Spirit Candlelight Vigil, sponsored by the Native Women’s Association of Canada, this article compares three free-use Twitter application programming interfaces for capturing tweets and enabling analysis. Although recent Twitter data restrictions limit free access to tweets, there are many dynamic options for social scientists to choose from in the capture and analysis of Twitter and other social media platform data. This paper calls for critical social media data analytics combined with traditional, qualitative methods to address the developing ‘data gold rush.’. (shrink)

Constellations, Volume 29, Issue 2, Page 146-160, June 2022.

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to (...) make sense of culture, history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

This short article argues that an adequate response to the implications for governance raised by ‘Big Data’ requires much more attention to agency and reflexivity than theories of ‘algorithmic power’ have so far allowed. It develops this through two contrasting examples: the sociological study of social actors used of analytics to meet their own social ends and the study of actors’ attempts to build an economy of information more open to civic intervention than the existing one. The article (...) concludes with a consideration of the broader norms that might contextualise these empirical studies, and proposes that they can be understood in terms of the notion of voice, although the practical implementation of voice as a norm means that voice must sometimes be considered via the notion of transparency. (shrink)

Big Data Analytics promises to help companies and public sector service providers anticipate consumer and service user behaviours so that they can be targeted in greater depth. The attempts made by these organisations to connect analytically with users raise questions about whether surveillance, and its associated ethical and rights-based concerns, are intensified. The articles in this special themed issue explore this question from both organisational and user perspectives. They highlight the hype which firms use to drive consumer, employee (...) and service user engagement with analytics within both private and public spaces. Further, they explore extent to which, through Big Data, there is an attempt to expand surveillance into the emotional registers of domestic, embodied experience. Collectively, the papers reveal a fascinating nexus between the much-vaunted potential of analytics, the data practices themselves and the newly configured intimate spheres which have been drawn into the commercial value chain. Together, they highlight the need for conceptual and regulatory innovation so that analytics in practice may be better understood and critiqued. Whilst there is now a rich variety of scholarship on Big Data Analytics, critical perspectives on the organising practices of Big Data Analytics and its surveillance implications are thin on the ground. Combined, the articles published in this special theme begin to address this shortcoming. (shrink)

Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system. For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D. “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, (...) and charting the affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society. (shrink)

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone (...) psychotherapy chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., they enable early diagnosis and intervention through digital phenotyping; they defy the stigma of mental illness diagnosis and treatment; they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises. (shrink)

The Snowden revelations and the emergence of ‘Big Data’ have rekindled questions about how security practices are deployed in a digital age and with what political effects. While critical scholars have drawn attention to the social, political and legal challenges to these practices, the debates in computer and information science have received less analytical attention. This paper proposes to take seriously the critical knowledge developed in information and computer science and reinterpret their debates to develop a critical intervention into (...) the public controversies concerning data-driven security and digital surveillance. The paper offers a two-pronged contribution: on the one hand, we challenge the credibility of security professionals’ discourses in light of the knowledge that they supposedly mobilize; on the other, we argue for a series of conceptual moves around data, human–computer relations, and algorithms to address some of the limitations of existing engagements with the Big Data-security assemblage. (shrink)

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big (...) Data traits to 26 datasets drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (shrink)

In 1990, Gilles Deleuze publishedPostscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey (...) their own personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the (...) audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high (...) demand for this type of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental case study, we found that not only are solutions like Cloudera’s Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines. (shrink)

What's wrong with targeted advertising in political campaigns? Are echo chambers a matter of genuine concern? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges.

BIG DATA AND ARTIFICIAL INTELLIGENCE: A LOOK INTO THE FUTURE To say or write something innovative on the ongoing revolution in the fields of Big Data and Artificial Intelligence is very difficult. The advent of these two new technologies is in fact among the most relevant events in human history since in a little more than a decade it will likely lead to the creation of the First Artificial Intelligence of the Fourth level: i.e capable to think and (...) create autonomously. This is a strong statement but like it or not is also irrefutable and fully compatible with the current technological trends and with what is happening right now, in front of our eyes. For the time being, however, we are dealing just with a weak Artificial Intelligence deeply rooted in Big Data and Big Data Analytics. Even so, however, the epochal changes which are currently taking place in the social, economic, and political structure of our world, have no historical equivalent. In this essay, after a short introduction (for the nonprofessional) of Machine Learning and of the basics of the weak Artificial Intelligence, I will try to explain why Artificial Intelligence and Big Data inextricably connect to each other. I will also try to summarise some among the many positive and negative aspects of the problem. (shrink)

The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid (...) out by Beauchamp and Childress. Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor–patient relationship towards improving population health outcomes and creating a healthier society. (shrink)

The contribution of Big Data to social science is not limited to data availability but includes the introduction of analytical approaches that have been developed in computer science, and in particular in machine learning. This brings about a new ‘culture’ of statistical modelling that bears considerable potential for the social scientist. This argument is illustrated with a brief discussion of model-based recursive partitioning which can bridge the theory and data-driven approach. Such a method is an example of (...) how this new approach can help revise models that work for the full dataset: it can be used for evaluating different models, a traditional weakness of the ‘traditional’ statistical approach used in social science. (shrink)

Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected (...) data and built tools. These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them. (shrink)

Purpose – Investments in the technologies of borders and their securitisation continue to be a focal point for many governments across the globe. This paper is concerned with a particular example of such technologies, namely, “Big Data” analytics. In the past two years, the technology of Big Data has gained a remarkable popularity within a variety of sectors, ranging from business and government to scientific and research fields. While Big Data techniques are often extolled as the (...) next frontier for innovation and productivity, they are also raising many ethical and political issues. The aim of this paper is to consider some of these issues and provide a critical reflection on the implications of using Big Data for the governance of borders. Design/methodology/approach – The author draws on the example of the new Big Data solution recently developed by IBM for the Australian Customs and Border Protection Service. The system, which relies on data collected from Passenger Name Records, aims to facilitate and automate mechanisms of profiling enable the identification of “high-risk” travellers. It is argued that the use of such Big Data techniques risks augmenting the function and intensity of borders. Findings – The main concerns addressed here revolve around three key elements, namely, the problem of categorisation, the projective and predictive nature of Big Data techniques and their approach to the future and the implications of Big Data on understandings and practices of identity. Originality/value – By exploring these issues, the paper aims to contribute to the debates on the impact of information and communications technology-based surveillance in border management. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, (...) especially in terms of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, (...) especially in terms of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the (...) specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

The Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous complicity of internet companies and the international controversies that followed provide a perfect segue into contemporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and networks to a C21st focus on data, currently crystallized in “Big Data.” Big Data intensifies certain surveillance trends associated with information technology and networks, and is thus implicated in fresh but (...) fluid configurations. This is considered in three main ways: One, the capacities of Big Data intensify surveillance by expanding interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size, qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity's predilection for certain definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and foreclose surveillance. (shrink)

Data analytics, particularly the current rhetoric around “Big Data”, tend to be presented as new and innovative, emerging ahistorically to revolutionize modern life. In this article, we situate one branch of Big Data analytics, spatial Big Data, through a historical predecessor, geodemographic analysis, to help develop a critical approach to current data analytics. Spatial Big Data promises an epistemic break in marketing, a leap from targeting geodemographic areas to targeting individuals. Yet (...) it inherits characteristics and problems from geodemographics, including a justification through the market, and a process of commodification through the black-boxing of technology. As researchers develop sustained critiques of data analytics and its effects on everyday life, we must so with a grounding in the cultural and historical contexts from which data technologies emerged. This article and others develop a historically situated, critical approach to spatial Big Data. This history illustrates connections to the critical issues of surveillance, redlining, and the production of consumer subjects and geographies. The shared histories and structural logics of spatial Big Data and geodemographics create the space for a continued critique of data analyses’ role in society. (shrink)

The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. (...) This body of knowledge provides an opportunity to reflect on the ‘promise’ of Big Data, both in relation to service delivery and policy formulation. This includes, issues associated with the quality and reliability of data, from mixing public and private sector data, issues associated with the ownership of raw and manipulated data, and ethical issues concerning surveillance and privacy. These insights and the issues raised help assess the value of Big Data in government and smart city environments. (shrink)

This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering (...) and neo-tribalism, and both data-intensive and qualitative methods to the case of people’s engagements with the news of Cheddar Man’s DNA on Twitter. We show that heritage-based tribalism in Big Data ecologies is uniquely shaped as an assemblage by the coalescing of different forms of antagonistic othering. Those that co-occur most frequently are the ones that draw on ‘Views on Race’, ‘Trust in Experts’ and ‘Political Leaning’. The framings of the news that were most influential in triggering heritage-based tribalism were introduced by both right- and left-leaning newspaper outlets and by activist websites. We conclude that heritage-themed communications that rely on provocative narratives on social media tend to be labelled as political and not to be conducive to positive change in people’s attitudes towards issues such as racism. (shrink)

Recent work on Big Data and analytics reveals a tension between analyzing the role of emerging objects and processes in existing systems and using those same objects and processes to create new and purposeful forms of action. While the field of science and technology studies has had considerable success in pursuing the former goal, as Halford and Savage argue, there is an ongoing need to discover or invent ways to “do Big Data analytics differently.” In this (...) commentary, I suggest that attempts to produce new ways of working with Big Data and analytics might be hindered by how science and technology studies-influenced scholars have conceptualized assemblages. While these scholars have foregrounded objects’ relations within existing assemblages, new materialist philosophers draw attention to properties of objects that transcend those relations and might indicate opportunities for more creative or generative uses of Big Data and analytics. (shrink)

Our study considers the governance, ethics and operational challenges associated with the acquisition, manipulation and commodification of ‘big data’ in the financial services sector. To the best of our knowledge, there are no published studies describing empirical research undertaken within companies in this sector to understand how they are responding to such challenges: our field-based research is a significant initial contribution in this respect. We describe the results of a micro-ethnographic study undertaken in a small-to-medium-sized company developing disruptive, technology-related (...) platforms and services in the banking and retail sectors based on big data and associated analytics: these are used to derive commercially valuable insights from personal customer data in exchange for cash back and targeted rewards. The company was found to employ a multi-level innovation governance approach, underpinned by an ethical strategy based on a principle of mutual benefit. Opt-in and informed consent for using data for specific purposes was supported by principles of data minimisation and anonymisation, with unrestricted use of secondary, anonymised and aggregated data to develop insights. Governance, which included contextual data protection legislation, payment-card industry data usage standards and internal corporate controls, presented as bespoke organisational practices relating to data security and privacy. These in total set the governance and ethics frame for big data innovation at the company within which it has had to be both adaptive and responsive under conditions of normative and regulatory uncertainty. (shrink)

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the (...) ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing. (shrink)

Can we measure and predict character with predictive analytics so a business can better assess, ideally objectively, whether to lend money or extend credit to that person, beyond current objective measures of credit scores and standard financial metrics like solvency and debt ratios? We and the analysts probably do not know enough about character to try to measure it, though it might be more useful to measure and predict a person’s temperance and prudence as virtues, or self-control as psychology, (...) or sense of obligation, particularly a moral commitment or sense of duty to honor a contract and re-pay a loan. The pervasive data surveillance of people that goes with “big data” and predictive analytics is not only an invasion of privacy in general, but an impairment of the aspect of privacy called autonomy that will constrict and alter a person’s choices and development of self. (shrink)

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the (...) availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann. (shrink)

Very large databases are a major opportunity for science and data analytics is a remarkable new field of investigation in computer science. The effectiveness of these tools is used to support a “philosophy” against the scientific method as developed throughout history. According to this view, computer-discovered correlations should replace understanding and guide prediction and action. Consequently, there will be no need to give scientific meaning to phenomena, by proposing, say, causal relations, since regularities in very large databases are (...) enough: “with enough data, the numbers speak for themselves”. The “end of science” is proclaimed. Using classical results from ergodic theory, Ramsey theory and algorithmic information theory, we show that this “philosophy” is wrong. For example, we prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which—as we will prove—implies that most correlations are spurious. Too much information tends to behave like very little information. The scientific method can be enriched by computer mining in immense databases, but not replaced by it. (shrink)

IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction (...) between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract “sense” from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts. (shrink)

The health care industry will be a large customer of big data while predictive analytics already underlie important health care and public health initiatives. Yet big data are not benign. For example, data brokers, the businesses that buy, process, and sell “big data” are performing an end-run around health data protection by creating data “proxies” outside of HIPAA-protected space. From 2012-14 various branches of the federal government published five major reports on privacy. All (...) five were in favor of increased regulation of data brokers. However, their recommendations for legislative or regulatory intervention were quite diverse. This essay describes the various proposals and offers a critical synthesis. (shrink)