Data is often described as a resource, something to be collected, stored, and extracted. Yet this analogy is no longer sufficient. In today’s digital economy, data behaves less like a fixed asset and more like a liquid one. It flows across platforms, is continuously recombined, and generates value through movement rather than mere possession.
Understanding data as a liquid asset changes how we think about value, power, and ownership. It shifts attention from extraction to circulation, from storage to flow, and from static control to dynamic governance.
From Resource to Flow
Earlier discussions of the data economy often compared data to natural resources. The metaphor of oil suggested scarcity, extraction, and centralized control. While useful, this framing overlooks a key feature of data.
Data does not diminish when used. It can be copied, shared, and repurposed across contexts. Its value increases as it moves through networks, interacts with other datasets, and is processed by analytical systems.
In this sense, data resembles liquidity in financial systems. It gains value through circulation. Platforms, applications, and institutions rely on continuous data flows to generate insights, optimize processes, and create economic value.
This shift from resource to flow marks a fundamental transformation in how value is produced in the digital economy.
The Circulation of Value
If data is liquid, then value is not simply extracted at a single point. It is created across multiple stages.
Users generate data through everyday activities such as communication, transactions, and mobility. Platforms collect and aggregate this data, transforming it into structured forms. Analytical systems process it to produce predictions, recommendations, and insights. These outputs are then monetized through advertising, services, or strategic decision making.
At each stage, value is added. However, this value is not distributed evenly.
Most of the economic benefits are captured by actors who control the infrastructure and analytical capabilities. Meanwhile, individuals and communities that generate the data often receive little direct return.
Couldry and Mejias (2019) describe this dynamic as data colonialism, where value is extracted from human activity without corresponding recognition or compensation.
Power in Motion
Viewing data as a liquid asset also reveals new forms of power.
Control is no longer limited to ownership of data. It extends to control over flows. Actors who can direct, restrict, or amplify data flows hold significant influence.
Platforms, cloud providers, and data intermediaries play a central role in this system. They determine how data moves, who can access it, and under what conditions. This gives them the ability to shape markets, influence behavior, and define opportunities.
Power also operates through standards and protocols. Decisions about interoperability, data sharing, and access rules influence the structure of data ecosystems. These decisions are often made by a limited set of actors, raising concerns about concentration of power.
As Zuboff (2019) argues, the asymmetry between those who collect and process data and those who generate it creates new forms of economic and social dominance.
Rethinking Ownership
The liquidity of data challenges traditional notions of ownership.
In conventional systems, ownership implies clear boundaries and exclusive rights. Data complicates this model. It is generated through interactions, shared across platforms, and embedded in networks.
Who owns a dataset that combines inputs from millions of users. Who controls derived data, such as predictions or behavioral profiles. These questions do not have straightforward answers.
Legal frameworks often struggle to keep pace with these complexities. While data protection laws focus on privacy and consent, they do not fully address issues of value and ownership.
This gap has led to proposals for new models, such as data trusts, data cooperatives, and collective ownership arrangements. These approaches aim to redistribute control and benefits, but their implementation remains limited.
Inequality in the Data Economy
The liquidity of data does not eliminate inequality. In many cases, it intensifies it.
Actors with advanced infrastructure and analytical capacity are better positioned to capture value from data flows. This includes large technology firms and well resourced institutions.
At the same time, individuals and communities with limited digital access or data literacy may be excluded from the benefits of the data economy. They may generate data without having the capacity to leverage it.
This creates a dual inequality. First, in the distribution of value. Second, in the ability to participate in data driven systems.
Kitchin (2014) notes that data infrastructures are unevenly distributed, shaping who can access and benefit from data. These inequalities are not incidental. They reflect broader patterns of economic and technological power.
Governance in a Liquid System
If data flows continuously, governance must adapt to this fluidity.
Traditional regulatory approaches, which focus on static control points, may be insufficient. Instead, governance needs to address how data moves across systems, jurisdictions, and actors.
This includes questions of cross border data flows, interoperability, and accountability. It also requires mechanisms to ensure that individuals and communities have meaningful control over how their data is used.
Transparency becomes more complex in a liquid system. It is not enough to know where data is stored. It is necessary to understand how it circulates, how it is transformed, and how it generates value.
Effective governance must therefore combine technical, legal, and institutional approaches.
The Data Justice Perspective
Understanding data as a liquid asset highlights key dimensions of data justice.
Representation concerns who generates data and how their contributions are recognized. In a liquid system, contributions are continuous and distributed.
Distribution relates to how value is allocated across the data lifecycle. Current systems often concentrate value in a small number of actors.
Governance addresses who controls data flows and sets the rules. Concentration of control raises concerns about accountability and fairness.
These dimensions underscore that data liquidity is not only an economic issue. It is a question of justice.
Toward More Equitable Data Futures
Rethinking data as a liquid asset opens new possibilities for more equitable systems.
Policies can be designed to ensure fairer distribution of value, such as mechanisms for data sharing, benefit sharing, or collective ownership. Investments in digital infrastructure and data literacy can expand participation.
At the same time, governance frameworks must address power imbalances. This includes regulating dominant platforms, promoting interoperability, and supporting alternative models of data governance.
Importantly, individuals and communities should be recognized not only as data sources, but as stakeholders with rights and interests in how data is used.
Conclusion
When data becomes liquid, value is created through movement, interaction, and transformation. This reshapes the dynamics of power and challenges traditional notions of ownership.
Understanding these changes is essential for navigating the digital economy. It reveals that control over data flows is as important as control over data itself.
The challenge is to ensure that this liquidity does not lead to greater concentration of power, but instead supports more equitable and inclusive systems.
Ultimately, the future of the data economy depends on how we rethink value, power, and ownership in a world where data is always in motion.
References
Couldry, N., and Mejias, U. (2019). The Costs of Connection. Stanford University Press.
Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.
Kitchin, R. (2014). The Data Revolution. Sage.
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