Synchronizing Data in Organizations

Synchronizing Data in Organizations

Siri Lamoureaux (Universität Siegen), Tobias Röhl (PH Zürich), Matthias Röhr (Universität Siegen), Nadine Taha (Universität Siegen), Axel Volmar (Universität Siegen) (SFB 1187 Medien der Kooperation, Universität Siegen)
SFB 1187 Medien der Kooperation, Universität Siegen
Universität Siegen
Vom - Bis
09.09.2021 - 10.09.2021
Matthias Röhr, Universität Siegen, SFB Medien der Kooperation

This conference aims to investigate how organizations deal or have dealt with the temporal and socio-technical heterogeneity of various forms of data.

Synchronizing Data in Organizations

Organizations have always produced and relied on a wide variety of different forms of data. Originating from different sources and times, they are aggregated and operationalized with the aid of technologies and become part of situated ‘data practices.’ Thus, data seem to be in constant need of synchronization to enable their harmonious use across places and times. At the same time, practices of synchronization within organizations themselves rely on certain forms of data and data processing technologies. Synchronization, however, should not be considered a smooth technical process, as data are visible for and directed at different publics and can appear as open and mundane in some instances and as exclusive and confidential in others. Moreover, data tend to be selective, incomplete or even “broken” (Pink et al. 2018). As “lively data” (Lupton 2016), they are open to interpretation, carry a history and may yield future potential. They also seem to be in constant need of care and are subject to various forms of data work, as they need to be stored, retrieved, cleaned up or re-/formatted for specific purposes.

Above all, data shape and are shaped by organizational and social temporalities (Wajcman and Dodd 2016), “temporal and normative orderings” (Coletta et al. 2020) as well as complex media ecologies and “infrastructures of time” (Volmar and Stine 2021). Their usefulness for the present moment needs to be established in situ and determined in relation to the availability of technologies and other data. Such forms of data synchronization include the intra-organizational curation, transformation and adjustment of data in everyday work practices as well as inter-organizational forms of data exchange. Even though it is well known that prognoses are likely to fall short and that the future can never be fully anticipated and planned, data-driven “future-making practices” (Wenzel et al. 2020) based on digitally networked information infrastructures are becoming increasingly ubiquitous within organizations. Data is used to make sense of the organizational pasts, predict future challenges, and guide decision-making processes in the face of organizational environments increasingly perceived as volatile, uncertain, complex and ambiguous (VUCA). In this regard, data form the basis for the creation of “fictional expectations” (Beckert 2016) that lend credibility and accountability to decisions.

Data synchronize and are being synchronized as part of organizational practices (Nicolini and Monteiro 2017). Organizations in particular make use of a range of technologies and media that makes this possible – ranging from relatively simple tools like calendars (Hoof 2019), timetables (Zerubavel 1982) and clocks (Gregg and Kneese 2019) to planning tables (Conrad 2019), databases (Haigh 2009), real time bidding systems (Vurdubakis 2019), and other “logistica media” (Peters 2015; Rossiter 2015). Moreover, data circulates in infrastructures, which exert their influence through formats, standards, platforms, APIs etc., what and how data can be handled. Consequently, synchronizing data also transforms how organizations operate and coordinate work practices. When, for example, organizations coordinate workers via digital platforms, the relationship between organizations and their workforce shifts from traditional forms of managerial control towards algorithmic management of gig economies (Rosenberger and Stark 2015; Duggan et al. 2020). With cloud computing and big data (boyd and Crawford 2012), organizations rely increasingly on data created by other organizations and that now may collide with data practices and tacit knowledge already present within the organization. As boundaries of organizations become less clearly defined (Büchner 2019), different data practices and their diverging temporalities are further confronted with each other.

How do new ways of data aggregation and processing adjust temporal patterns of work, governance, leadership, collaboration and decision-making, and how, in turn, do changing forms of cooperative planning and data practices alter what kinds of data (such as qualitative data, user data, sensor data etc.) emerge and are being used in organizations? How are organizational data translated, interpreted and related to other data? How are they represented and “re-represented” (Gerson and Star 1985), and what frictions arise between new forms of data processing and situated work practices? What kind of “accountability relations” (Woolgar and Neyland 2013; Neyland and Coopmans 2014) are enacted through these processes? What are problems, challenges and issues revolving around data and temporality in organizations? Therefore, we invite scholars to explore the specific temporal indices of data in and between organizations along with the practices that shape or are shaped by those data.

Conceptually, the workshop aims towards a mutual and reciprocal development and appreciation of media studies and organizational research in various disciplines. The practice turn in organizational research leads to an understanding of media and technology not as mere tools, but as constitutive of organizational work and its social order (Schubert and Röhl 2019; Orlikowski 2017). Accordingly, organizations are seen as sites where media and practices of organizing meet (Schatzki 2006). At the same time, media studies have taken an increasing interest in how media and data technologies reshape organizational practices and how these dynamics reflect back on the meaning of the notion “medium” itself (Beyes, Holt and Pias 2019; Schüttpelz 2017). We understand organizations broadly as sites where people coordinate their activities and thus do not want to limit contributions to organizations, such as business enterprises or public administrations, but also invite research on political movements and activist groups.

We look forward to receiving proposals for oral presentations of 20–25 mins from a broad range of disciplines – such as media and communication studies, sociology, organizational and management research, science and technology studies, socioinformatics/CSCW, infrastructure studies, anthropology, history and others – that address issues of synchronization, prediction, coordination, translation, interpretation etc. in relation to the dis-/continuity of heterogeneous data and their role for organizational practices and operations.

We see data as socially and temporally multiple, heterogeneous and ubiquitous. They are circulating more and more through organizations but also between organizations and external entities and other stakeholders. Following these assumptions we are particularly – but not exclusively – interested in the following themes:

- Legacy of data: While data can be extracted, migrated and thus translated or channeled into new contexts, they always bear traces of the situations in which they have been produced or aggregated. How do organizations deal with the inevitable inconsistencies and asychronities of data? How is data that was produced in the past made relevant for the situation at hand?

- Interoperability of data: Data must be worked on before its synchronization which is evidenced in the many practices of sorting, filtering, caring, repairing and formatting data and also in the process of defining common interfaces for sharing, extracting, migrating and exchanging data. This is arguably one of the biggest data-related problems in organizations today – making sense of heterogeneous data sets. How is data made interoperable and prepared for synchronization? What is inevitably lost when data is cleaned and translated in this process?

- Reversibility and accountability of data: Due to the necessity for evidence that organizations are confronted with – not exclusively, but increasingly when algorithmic decision making is involved – processes of merging and matching different data sets and algorithmic data chains must be traceable, and thus demand a range of additional data documentation as well as agreements and conventions. How does data documentation interfere with issues of synchronization? To what extent can reversibility and accountability be described as practices of data synchronization?

Please submit abstracts of 300 to 500 words, along with a title and a short biography (max. 150 words) by February 28, 2021 to

Note: We plan on conducting the conference on location in Siegen. Should future COVID-related restrictions prohibit the event to be held in physical co-presence, it will take place online. The format will be adjusted accordingly.

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