Sacred Excess: Organizational Ignorance in an Age of Toxic Data (original) (raw)
Related papers
Data and Organization Studies: Aesthetics, emotions, discourse and our everyday encounters with data
Organization Studies, 2021
Despite the growing "data imperative" and "fetishization of data" across organizational contexts, critical scholars have adhered to a set of normative understandings for how people experience and engage with data and datafication in and around organizations: namely, as numbers and statistics that are "captured", interpreted, and operationalized. In reality, however, data and datafication are experienced within organizational life in a multiplicity of ways that often have very little to Organization Studies
Organizations Decentered: Data Objects, Technology and Knowledge
Organization Science, 2022
Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal...
In this article, I counter persistent claims of big data revolutionising managerial decision-making, by tracing the technological and cultural origins of data-based management in the United States back to the 1970s and 1980s using historical source materials from the trade magazine Datamation. I argue that innovations in database technology within this period – database management systems and the relational database model – shaped and reinforced a data-based mindset. This mindset, I demonstrate, is manifested in four interlinked concepts of data: data as asset, data as raw, data as reality, and data as relatable. These concepts, I argue, provide a basis for current associations of big data with ideological values of objectivity and truthfulness. The article contributes to a growing body of work in media and communication studies that deconstructs the ideological discourses facilitating big data's unquestioned integration in the business world.
Social Epistemology, 2016
The question we pose in this paper is: How can wisdom and its inherent drive for integration help information systems in the development of practices for responsibly and ethically managing and using big data, ubiquitous information and algorithmic knowledge and so make the world a better place? We use the recent financial crises to illustrate the perils of an overreliance on and misuse of data, information and predictive knowledge when global Information Systems are not wisely integrated. Our analysis shows that the global financial crisis was in part caused by a serious lack of integration of information with the larger context of social, cultural, economic and political dynamics. Integration of all the variables in a global and information hungry industry is exceptionally difficult, and so "exceptionality" of some kind is needed to make sufficient integration happen. Wisdom, we suggest, is the exceptionality needed to lead successful integration. We expect that a wisdom-based shift can lead to more organizationally effective and socially responsible Information Systems.
Tension in the data environment: How organisations can meet the challenge
Technological Forecasting and Social Change, 2021
Big Data is becoming ubiquitous-widely applied across organisations, industry sectors and society. However, the opportunities and risks it presents are not yet fully understood. In this paper we identify and explore the tensions that Big Data can create at multiple levels, focusing on the need for organisations to meet the challenges that can arise. We draw on insights from twelve papers published in the Special Issue of Technological Forecasting & Social Change entitled "Tension in the Data Environment: Can Organisations Meet the Challenge?" in order to build a 'Multi-Layer Tensions Model' that highlights key pressures and challenges in the BD environment. We find evidence of tensions of three types, which we summarise as "Organisational Learning", "Organisational Leadership" and "Societal" tensions. We contribute, first, by identifying and developing a nuanced understanding of the tensions faced in the Big Data environment; and second, by elaborating on the capabilities that can be developed and the actions taken to maximise the benefits of Big Data. We end with a "Learning, Leading, Linking" framework, which points to implications for practice and a future research agenda.
Sociology Compass, 2020
The prominence of data and data technologies in society, such as algorithms, social media, mobile technology, and artificial intelligence, have heralded numerous claims of the revolutionary potential of these systems. From public policy, to business management, to scientific research, a “data-driven” society is apparently imminent – or currently happening - where “objective” and asocial data systems are believed to be comprehensively improving human life. Through a review of existing sociological literature, in this article we critically examine the relationship between data and society, and propose a new model for understanding these dynamics. Drawing on Hayles’ (1999:313) conceptualisation of the “informatic”, we argue the relationship between data and society can be understood as representing the interaction of several different social trends around data; that of Data Interfaces (that connect individuals to digital contexts), Data Circulation (trends in the movement and storage of data), and Data Abstraction (data manipulation practices). Data and data technologies are founded to be entwined and embedded in numerous social relationships, and while not all are fair and equitable relationships, there is ample evidence of the deeply social nature of data across many streams of social life. Our three-part informatic framework allows these complex relationships to be understood in the social dynamic through which they are witnessed and experienced.
Journal of marketing Management, 2019
The twin pillars of big data and data analytics are rapidly transforming the institutional conditions that situate marketing research. In response, many proponents of culturalist paradigms have adopted the vernacular of 'thick data' to defend their vulnerable position in the marketing research field. However, thick data proselytising fails to challenge several outmoded ontological assumptions that are manifest in the big data myth and it situates socio-cultural modes of marketing thought in a counterproductive technocratic discourse. In building this argument, I first discuss the relevant historical continuities and discontinuities that have shaped the big data myth and the thick data opportunism. Next, I argue that culturally oriented marketing researchers should promote a different ontological frame-the analytics of marketplace assemblages-to address how big data, or more accurately its socio-technical infrastructure, produces new kinds of emergent and hybrid market structures, modes of social aggregation, consumption practices, and prosumptive capacities.
The data archive as factory: Alienation and resistance of data processors
Big Data & Society, 2021
Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a taxonomy of the forms of alienation that data processing generates, but also the types of resistance that processors develop, across four categories: routine, speed, skill, and meaning. This synthetic approach demonstrates, first, that data processing reproduces typical forms of factory worker’s alienation: processors are asked to work along a strict standardized pipeline, at a fast pace, without acquiring substantive skills or having a meaningful involvement in their work. It reveals, second, how data processors resist the alienating nature of this workflow by developing multiple tactics along the same four categories. Seen through this dual lens, data processors are therefore not only invisible workers, but also factory workers who follow and subvert a workflow organized as an assembly line. I conclude by proposing a four-step framework to better value the social contribution of data workers beyond the archive.