Data is often described as a valuable resource that can improve public services, stimulate economic activity, enable innovation, and support national development. Its value, however, is not inherent. It is shaped by the processes and institutions that collect, transform, verify, govern, and ultimately use it. These choices determine not only what can be built with data, but also who benefits—and is excluded—from systems built on it. Becoming data rich is therefore not the same as building the capability to use data for public benefit. To transform data into a valuable asset, political commitment, institutional design, societal trust, data quality, and the capacity to coordinate across and within stakeholder groups is vital and separates data richness from meaningful data capability.

This is important particularly for countries with institutional fragility, climate vulnerability, or constrained state capacity, where the existence of data does not guarantee that it can be mobilised effectively.

Who captures value?

Unlike finite physical resources, data can be reused and combined across multiple contexts. Linking datasets can increase their value by informing decisions across sectors and stakeholder groups. The ability to realise this value, however, is unevenly distributed. Governments and communities generate valuable data, while private firms and well-resourced institutions often possess greater capacity to analyse it and convert it into commercial or strategic advantage. Data’s value must therefore be understood through power, market structure, and sovereignty, rather than volume alone. The rules governing access decide who can use data, for what purposes, and with whose consent or authorisation. These choices influence whether data supports better policymaking, research, crisis response, and service delivery, or enables commercial extraction, surveillance, and automated decisions that reproduce existing inequalities.

This is also why Trusted Data Sharing (TDS) systems built as DPI are socio-technical. Building on Aapti’s previous work on the value of the socio-technical approach to TDS, a political economy perspective draws attention to how these systems both reflect and reshape the social, political, and economic realities in which they operate. Choices about what is recorded, how people are classified, and who can access information shape whose needs become visible and which institutions can act on them. When the services built on this data inform welfare eligibility, credit access, or resource allocation, they determine who has access to opportunities. Deployment can reinforce existing inequalities or help change them.

The socio-technical question for TDS systems is twofold: a) how do people interact with TDS systems, and b) how is power is exercised and redistributed through them?

Protecting and unlocking value

TDS models must balance accessibility, security, utility, and the protection of people and communities. Their design depends on the source and type of data, how it is transformed and stored, and its intended use. Data is not a monolith: health and financial records often require stringent safeguards, while administrative or agricultural data can also become sensitive depending on how it is combined, whom it identifies, and what it is used for.

Enabling secure and useful sharing requires moving beyond a binary choice between keeping data closed and making it freely accessible. Rules for access, consent, authorisation, secondary use, auditing, liability, and redress must be embedded in institutional and technical arrangements. Authority, risk, and value are distributed through these arrangements alongside the data itself. Community data stewardship offers one way to give those generating data a greater role in governing its use and sharing its benefits.

India’s “data-rich” development narrative brings out both the opportunity and the limitations of treating data as a developmental resource. The promise is that individuals and small businesses can use their data to obtain services and economic opportunities. Realising this promise requires reliable data, enforceable rights, strong institutions, and deliberate mechanisms through which individuals and communities benefit from downstream use. In the absence of these processes, new fragmentations proliferate, leaving countries with additional repositories that are not interoperable and therefore, not useful.

Climate and disaster response offer an example to highlight this distinction. Governments need to connect information held across meteorological, health, environmental, and emergency-response agencies. In many low- and middle-income countries, however, climate and health data remain fragmented across institutional silos, with limited interoperability, data skills, and coordination preventing timely action. Without clear responsibilities, incentives, and sharing arrangements, new platforms risk becoming additional repositories rather than improving the state’s ability to respond.

Systems inherit their politics

Governments do not build data systems on a blank slate. They inherit administrative databases, paper records, sector-specific laws, procurement relationships, bureaucratic silos, and legacy technologies. They also inherit histories of trust or distrust, patterns of exclusion, and relationships between the state, market, and citizen. These conditions shape which systems can work, how quickly they can scale, and whether people will trust them. State capacity here, goes beyond having the technical skill and infrastructure to build a platform, to include maintaining datasets, coordinating agencies, enforcing standards, overseeing vendors, funding long-term operations, auditing use, protecting rights, and resolving grievances. Deployment should be matched to the administrative capacity needed to sustain it, rather than assuming that capacity will follow the technology.

Digital access and inclusion are equally important. Where digital public infrastructure becomes the primary pathway to essential services, it can deepen exclusion without accessible alternatives and

effective redress. Connectivity alone is insufficient. Populations should also have the skills and support to use digital services meaningfully. Communities that struggle to access these systems may also be inaccurately represented in the records used to assess needs. If those records then guide resource allocation, exclusion risks becoming self-reinforcing.

Conclusion

Assessing demand itself is, therefore, socio-political. Existing databases reflect what administrations have historically chosen to recognise and record. Often, this means they have not captured the needs of populations previously overlooked. Choosing a data governance regime is a political act, and relying primarily on available datasets, vendors, or the most visible stakeholders, risks building for administrative convenience rather than public need. The objective of model implementation and governance rooted in these realities of data should therefore, centre the institutions, capabilities, and accountability required to use TDS valuably. This separates notions of being data rich and having data capability and consequently, systems that reproduce existing distributions of power and those that provide channels for change.