Social Data Migration Concept: Analyzing Transborder Data Flows in the Post-Industrial Economy

Abstract

Transborder data flows offer opportunities, such as health data sharing, but they also bring risk. Research has explored the tensions between transnational and regional linkages, striving to understand when transborder flows of data bring benefits or drawbacks. By viewing global data flows as a social change process, this commentary strives to complement existing perspectives. It advocates embedding data studies within the framework of social transformation theory to transcend the distinction between theory and its empirical application across diverse social and cultural contexts. Inspired by Stephen Castles’ approach to human migration, it introduces the concept of “social data migration” as a dynamic social transformation. This approach enhances our understanding of the complex, interconnected, and context-dependent nature of transnational flows of data across platforms amid rapid global changes.

Introduction

The research community has long strived to find a solution to the vital paradox of openness and closure in the flow of transborder data, characteristic of the post-industrial digital economy. However, we lack a framework for a comprehensive analysis that combines data platform technological affordances with an evaluation of local social and cultural specificities, essential for understanding the complexities of exercising power through transborder data flows.

The data relations between the United States and China, being major players in the global data and platform economy, exemplify these contradictions (World Bank, 2021). China’s mandatory data localization requirements, aimed at safeguarding critical information infrastructure like health data, have been feared to hinder global data sharing and the development of new medicines. In contrast, the Chinese social media company TikTok is feared to be a potential risk for the United States due to its capacity to share sensitive user data with the Chinese government (Gray, 2021). However, the underlying reasons and potential consequences of these contradictions, limitations, and enabling of exporting personal information overseas remain unclear. Therefore, there is a need for a framework that facilitates a deeper understanding and assessment of the uncharted complexities, contradictions, heterogeneities, and variations within global data flows.

Numerous studies have addressed this paradox. Some acknowledge that external and internal power asymmetries similar to colonialism are intrinsic to transborder data flows (Couldry & Mejias, 2019; Milan & Treré, 2019; Thatcher et al., 2016). These studies have shown that data movements often occur from vulnerable and powerless regions to the developed world (see, e.g., Milan & Treré, 2019), but also vice versa, where the experiences of developed countries and the benefits of advanced data technologies are leveraged in developing countries (Heeks & Renken, 2018; Masso et al., 2022). However, we are still working to investigate these transnational and regional linkages within data flows. Furthermore, we have yet to fully grasp the conceptual and epistemological implications that the transborder data flow example illustrates for our understanding of the globalized digital economy.

In this commentary, we suggest and explain the concept of “social data migration” as a potential framework for examining transborder data flows in the post-industrial economy, building on and extending previous approaches.

Data Flows, Journeys, and Movements

Many different concepts have been used to examine the transborder data flows. For example, the notion of data migration emphasizes the technical processes for enabling data sharing across locations, application domains, data formats, and systems (see, e.g., Morris, 2012). Other concepts like openness (Kitchin, 2014), flow (Edwards, 2013), journeys (Bates et al., 2016), transfer (Suda, 2019), reuse (Thylstrup et al., 2022), or relocation (Loukissas, 2019; Masso et al., 2022) are used to emphasize that data are never neutral but carry significant information about the people and places where the data are collected or used. Therefore, moving data from one location to another is always a recoding process (Kitchin & Dodge, 2011) involving negotiations of social and cultural norms, practices, or symbols.

Nevertheless, a challenge persists in integrating diverse social and cultural characteristics into research on data sharing, as well as in the design and development of models based on these data that are applicable across various social contexts. One potential reason for this could be the tendency to perceive data movements either as a critical issue to be resolved or as a normative phenomenon to be managed to achieve effective and efficient results in a particular social context. For example, the normative studies have analyzed the drivers and barriers to reach the desired future state of borderless data movements as a means to facilitate social innovation and its diffusion across societies (see, e.g., Janssen et al., 2020). Other studies have taken a critical stance and strived to identify the unintended negative consequences potentially accompanying the data and its movements across various cases, domains, and societies (see, e.g., Boyd & Crawford, 2012; Edwards, 2013; Hepp et al., 2022; Iliadis & Russo, 2016). Negative consequences are inequality in reproduction, increased discrimination, and constant surveillance. However, studies have often been conducted within single-country contexts, initially in Western societies or among potentially vulnerable user groups in the global south. Yet, data transfer involves individuals from multiple countries concurrently. Therefore, we still have challenges in grasping the complex social processes of transborder data flows across various socio-spatial levels, extending from global to regional and local scales.

Thus, a comprehensive examination of the diverse outcomes of data flows across various socio-spatial contexts is needed. To achieve this, we recommend grounding the study in social theories that have previously explained the varied impacts of human movement, while also incorporating insights from data studies and frameworks from other disciplines.

The Social Data Migration Framework

This commentary draws inspiration from migration studies, highlighting the normalcy of human migration throughout history, much like the sharing of information. However, much like migration theories, an understanding of complexity, diversity, and the significance of context might lead to the notion that each case appears distinct (Castles, 2010), and therefore, could foster further fragmentation in the study of transborder data flows. While migration theories provide a wide range of perspectives, they still operate within an increasingly universal framework of power relationships, which can offer a valuable framework for data studies.

Social Data Migration as a Concept

We claim that analysis of the transborder data flows using the framework of migration studies could provide the basis for a new understanding of the global change processes in the post-industrial digital economy. We do not strive to provide an alternative but combine and synthesize the previous approaches to offer knowledge that could be valuable in future research.

One framework that can be useful in understanding data movement is Stephen Castles’ approach to human migration (Castles, 2001, 2007, 2010), where we see many parallels with the movements of data. Castles’ theory emphasizes that migration is a complex, multifaceted process deeply intertwined with broader social, economic, and political changes. Government policies and state interventions play crucial roles in regulating and influencing migration flows, though they often face limitations due to global interconnectedness. Importantly, Castles highlights that people, who are directly affected by migration, are active agents who make strategic decisions based on their aspirations, constraints, and opportunities. Similarly, we suggest that, akin to human migration, data movements (see, e.g., Bates et al., 2016; Kitchin, 2014; Thylstrup et al., 2022) involve active human agents who initiate the complex social change processes, encompassing social, economic, and political changes related to data movements.

Based on this, we introduce the concept of “social data migration,” which we define similarly to human migration (Castles, 2010, p. 1578) as “not merely as a result of social transformations, nor as one of its causes, but as an integral and essential part of social transformation processes.” The term “social data migration” could be viewed as a metaphor that highlights the complexities of social processes associated with transborder data flows. Social data migration can be also regarded as an analogy or a method, comparing two seemingly distinct processes to emphasize their similarities within the intricate realm of complex, diverse, and multi-level social processes. However, our interpretation of social data migration goes beyond being merely a metaphor or an analogy. We strive to provide a framework that might facilitate an understanding and examination of the technical, political, economic, and socio-cultural processes related to the movements of data across locations.

When introducing the concept of social data migration, an immediate question may arise: why is the term “migration” preferred over others that are related to transborder movements, mobility, and flows? We assert that similarly to debates in human migration studies situated within political discourses, parallel processes can be observed in the realm of data studies. Whereas people’s mobility and movements are mostly considered neutral and normal aspects of everyday life, migration is often perceived as a challenge that poses potential threats to cultural identity and the economy (see, e.g., Castles, 2010). As data use, reuse, and sharing become routine aspects of daily life in the digital economy, this process is similarly accompanied by inherent power relations. The power dynamics operating in various directions are exemplified by several disruptive events in recent history of data flows.

For example, in 2019, emerging economies like India, Indonesia, and Vietnam implemented stringent data localization mandates to assert domestic control and counter US tech dominance (Basu, 2020). Although lobbying by Western firms led to some relaxation of these mandates, the contest over data sovereignty persists. Similarly, China’s strict data localization policies prioritize political security and technological self-reliance, despite the risks of trade conflicts (Liu, 2020). As a result, as digital trade grows, cross-border data flows are reshaping competitive advantages (Chen & Gao, 2022). However, these countries have adopted distinct data governance strategies to address the challenges associated with data flows: while the United States and EU advocate for open data flows to promote free trade, China and India emphasize localization to strengthen their domestic markets, underscoring data’s role as a key global asset. Thus, power dynamics related to data movements are evident in the complex and interconnected relationships among various countries and stakeholders involved in data movement, including developers, decision-makers, and the individuals whose data is being collected.

Another important question that might be raised is why we need a new concept, such as “social data migration,” alongside the infrastructural notion of “data migration.” Data migration intrinsically implies social processes mediated by local historical and cultural patterns through which people develop varying forms of agency and resistance. As data is not isolated but intertwined with people, data migration encompasses the collision of meanings and social practices associated with the data. Therefore, the existing body of scholarship on technical data sharing across various locations, application domains, data formats, and systems should be supplemented by the study of the social and cultural processes that accompany it. The approach to data migration with a significant emphasis on the social processes allows for the comprehension of complex processes, encompassing data infrastructure, regulatory and normative principles, as well as values, cultural meanings, and social interactions. However, we need a specific, comprehensive conceptual framework capable of explaining these complex and multi-level processes of social data migration.

Data Migration as Social Transformation

We suggest that there is a need for a closer alignment of approaches related to transborder flows of data with general social theories. We propose that adopting a social change perspective could enhance our understanding and evaluation of the economic, political, social, and cultural processes accompanying global data flows.

In the article, we recommend that the approach to social transformation, which has proven fruitful in migration studies (Castles, 2010), may prove beneficial in understanding and analyzing cross-border data flows. Examining data migration in terms of social change entails a significant shift in emphasis. This implies that an analysis of data migration processes involves not just assessing potential positive outcomes, such as enhanced economic efficiency and operational effectiveness, or considering potential negative consequences, such as privacy and security risks, as we emphasized earlier in this commentary. It also extends beyond a sole focus on examining the causes that initiate these changes, such as the availability of cloud services providing a platform for data migration, or compliance with data transfer regulations. Instead, a key emphasis is that movements of data are a process of social change by itself. Below, we will elaborate on what this change precisely entails and how it unfolds.

An essential aspect of understanding any process of change is the concept of embeddedness (see, e.g., Polanyi, 1965) which has also previously been used in interpreting the social changes related to human migration (Castles, 2010). Embeddedness asserts that a comprehensive understanding of major historical transformations should acknowledge the interconnectedness of structural processes within broader social changes. For example, to understand the inequalities in the post-industrial digital economies, we need to critically analyze power dynamics in the platform-governed world, as Couldry and Mejias (2024) emphasize.

Evidence of such intertwined economic, social, political, and cultural transformations is prominent in regions of rapid and fundamental changes, notably observed in the post-Cold War East and Central Europe (Masso et al., 2020; Vihalemm et al., 2017). We propose that such changes and periods that significantly altered global power dynamics, can provide a useful foundation for analyzing contemporary geopolitical relations shaped by data movements between major global actors, notably the United States and China. Such regional studies perspective has been emphasized in the studies of data (e.g., Valente & Grohmann, 2024). However, the perspective of social transformations and the experiences of transition countries can serve as a good example of how to analytically look beyond the dichotomous approaches of constructed borders, such as north and south, east, and west.

Nevertheless, the concept of change is not entirely novel in the field of data studies. Prior research emphasizes the vital necessity of understanding social changes through data (Boyd & Crawford, 2012; Dorr et al., 2018). These frameworks place an emphasis on the involvement of different parties, both developers and users of data solutions, institutional or human actors, in the processes of data sharing and the associated negotiations between them. Still, a distinctive feature of the global social transformations inherent in data migrations is their operation on multiple spatial levels concurrently, including local, regional, and global scales, paralleling the dynamics of human migration. This “data glocalisation” (Masso et al., 2022) emphasizes the necessity of considering the connections between global forces and local lifeworld in the study of any data movements. Therefore, social transformation processes are always shaped not only by historical and cultural patterns but also by the development of various forms of agency and resistance in different localities.

Therefore, conceptualizing social data migration as a social change facilitates addressing emerging questions related to complexity, variability, contextuality, and multi-level mediations of transborder data flows, similarly to early 21st-century migration studies. This approach enables the exploration of shifts in platform-governed societies, moving beyond incremental social change to examine the interconnections between data movements and global change.

Conclusions

This commentary aimed to address conceptual challenges associated with the study of transborder data flows. The primary question was to effectively incorporate social and cultural features into the analysis of data movements. The solution does not involve seeking alternatives to the rich research within social science, which deals with the dynamic nature of information. Conversely, abandoning the search for conceptual advancements based on the specific social context of data transfer processes is not the answer either.

We suggest, that in examining transborder data flows, researchers can enhance their understanding by drawing insights from the study of human migration. This approach enables the integration of approaches in the analysis of data, allowing for a thorough examination of the patterns and variations in a range of data migration processes within specific social and cultural contexts. Such integration lays the ground for a conceptual framework for social data migration, which considers the embedded nature of infrastructural data transfer in broader social processes. This framework serves as a starting point for comprehending the evolving patterns of transborder data flows. This conceptual framework involves a detailed mapping of the social mechanisms explaining data migratory processes and their interconnectedness.

Incorporating a social element into the equation goes beyond merely expanding the concept of data migration to the study of societies. The proposed approach aims to foster discussion and identify ways to analytically and theoretically integrate the economic, political, infrastructural, institutional, social, and cultural processes that are closely intertwined with global data movements. Furthermore, the social data migration concept allows us to compare the universal reconfigurations and specific manifestations of the social processes accompanying data movements within the highly contested digital economy. It facilitates the analysis of social data migration in its various forms, along with the related social changes that differ in scope and speed. Social data migration is closely linked to power dynamics, involving movements between the vulnerable and the developed world, and research should recognize the interconnectedness across different social contexts, whether local, regional, or global.

We present several critical questions that the conceptual framework of social data migration would enable us to pose, as well as to seek and find answers to. Which historical patterns are driving the social transformation processes through information movements? What are the varying forms of agency and resistance to data movements that people develop? What are the universal aspects of data migration—the events, activities, or actors—that are similar across countries, and what are the specific differences in data migration mechanisms, influenced by cultural context? The social data migration research agenda explores the links between social transformation and data flows across different scales, focusing on how human agency influences responses to structural factors. However, the complete development of a theoretical and methodological framework necessary to establish a new and promising research direction in social data migration will remain the focus of subsequent research.