Data is frequently described as a resource, often compared to oil or natural commodities. This analogy has shaped how governments, corporations, and institutions approach the data economy. Data is collected, stored, and extracted to generate value. Yet this framing is increasingly insufficient. Unlike traditional resources, data is not finite, not depleted through use, and not bound by physical constraints.
To understand the role of data in the digital economy, it is necessary to move beyond the metaphor of extraction and toward a more nuanced understanding of power and justice. Data is not simply a resource. It is a relational and dynamic element that shapes how value is created, distributed, and governed.
The Limits of the Resource Analogy
The comparison between data and natural resources has intuitive appeal. Both can be extracted, processed, and transformed into economic value. However, the analogy obscures fundamental differences.
Natural resources are finite and rivalrous. Their use by one actor limits their availability to others. Data, in contrast, can be replicated and reused across contexts. Its value often increases through aggregation and recombination.
Moreover, the extraction of natural resources is tied to physical geography. Data extraction, on the other hand, is embedded in digital infrastructures that operate across borders. This allows data to be collected from one context and monetized in another.
Srnicek (2017) argues that digital platforms rely on data as a core input for value creation, but this process is distinct from traditional resource extraction. It involves continuous capture and analysis of user activity rather than one time extraction.
Data as a Site of Value Creation
In the digital economy, value is generated through the processing and analysis of data.
Users produce data through everyday interactions, including communication, transactions, and mobility. Platforms collect and aggregate this data, transforming it into structured datasets. Analytical systems then derive insights, predictions, and recommendations, which are monetized through various business models.
This process highlights that value is not inherent in raw data. It is created through infrastructure, computation, and interpretation.
However, the distribution of value is uneven. While users generate data, the economic benefits are largely captured by organizations that control data infrastructures and analytical capabilities.
Couldry and Mejias (2019) describe this dynamic as a form of data colonialism, where human experience is appropriated and transformed into economic value without equitable return.
Power and Control in the Data Economy
Understanding data as a resource requires examining power.
Power in the data economy is not only about ownership of data. It is also about control over the systems that collect, process, and distribute it. This includes platforms, cloud infrastructures, and algorithmic systems.
Large technology companies play a central role in this ecosystem. They have the capacity to collect data at scale, develop advanced analytics, and shape market dynamics. This concentration of power allows them to influence not only economic outcomes, but also social and political processes.
Zuboff (2019) highlights how surveillance based business models rely on the extraction and monetization of behavioral data, creating asymmetries between those who collect data and those who generate it.
States also exercise power through data governance. Policies related to data protection, localization, and access shape how data flows and who can use it. These regulatory frameworks reflect competing priorities, including economic growth, security, and individual rights.
Inequality and the Data Economy
The data economy is deeply intertwined with inequality.
At the global level, there is a divide between countries that have advanced digital infrastructures and those that do not. This affects their ability to generate, process, and benefit from data.
At the societal level, individuals and communities differ in their access to digital technologies and their capacity to leverage data. Those with greater resources are better positioned to benefit from data driven systems, while others may be excluded.
These inequalities are reinforced by data practices. Systems trained on incomplete or biased data can produce outcomes that disadvantage certain groups. This can affect access to credit, employment, healthcare, and public services.
Kitchin (2017) notes that data driven systems can reproduce existing social inequalities if they are not designed with attention to fairness and inclusion.
Governance and the Question of Justice
Rethinking data as a resource requires a focus on governance.
Who has the right to collect data. Under what conditions can it be used. How are benefits distributed. These questions are central to the politics of the data economy.
Current governance frameworks often prioritize innovation and economic growth, sometimes at the expense of equity and accountability. Data protection regulations address privacy concerns, but they do not fully address issues of value distribution and power.
A data justice perspective provides a broader framework.
Representation concerns who is included in data systems and how their experiences are captured.
Distribution relates to how value and benefits are allocated.
Governance addresses who controls data infrastructures and decision making processes.
These dimensions highlight that data governance is not only a technical issue, but also a political and ethical one.
Beyond Extraction Toward Fairer Systems
Moving beyond the resource analogy opens possibilities for more equitable data systems.
Alternative models of data governance are emerging. These include data trusts, cooperatives, and frameworks for collective ownership. Such models aim to redistribute control and benefits, giving individuals and communities a greater role in how data is used.
At the policy level, there is increasing recognition of the need to regulate dominant platforms, promote competition, and ensure fair access to data.
Investments in digital infrastructure and data literacy can also help reduce inequalities, enabling broader participation in the data economy.
However, these changes require not only technical solutions, but also political will and institutional capacity.
Conclusion
Data is often framed as a resource, but this framing captures only part of its role in the digital economy.
Data is a dynamic and relational element that shapes how value is created, how power is exercised, and how inequality is structured. Understanding this complexity is essential for developing more just and inclusive systems.
Rethinking data as a resource is therefore not only an analytical exercise. It is a political project that involves redefining how value, power, and justice are organized in a data driven world.
References
Srnicek, N. (2017). Platform Capitalism. Polity Press.
Couldry, N., and Mejias, U. (2019). The Costs of Connection. Stanford University Press.
Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.
Kitchin, R. (2017). Thinking Critically About and Researching Algorithms. Information Communication and Society.
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