There's a question few public institutions want to answer honestly: who controls the data the government collects on its citizens? This isn't a technical question. It's a political one, and the difference matters more than it seems.

The data we generate by paying taxes, crossing a border, requesting a health service, or simply existing as taxpayers forms an increasingly detailed profile of who we are. The State knows this. The tech companies that sell systems to the State know it too. What isn't always clear is whether citizens have any real control over that profile, or whether we're simply the subjects of a governance experiment that no one bothered to explain to us.

Mgter. Lucas Roberto Fenoglietto puts it precisely in his analysis of the State's algorithm: data governance shouldn't be understood as an end in itself, nor as a technocratic fad. It's a structural condition for government to function better—or worse—depending on how it's applied. That distinction, "better for whom," is exactly where the conversation gets complicated.

This dynamic repeats across other contexts. When information systems are centralized without clear accountability mechanisms, what was initially designed as operational efficiency ends up becoming a power asymmetry. Not necessarily out of malice. It's the natural logic of any structure that accumulates information without checks: information becomes leverage, and leverage gets used.

Mexico's SAT tax authority is a good illustration. Automatic digital tax enforcement with AI-driven data cross-referencing represents, in theory, greater collection efficiency. In practice, the citizen faces a structure that knows precisely when they've made a mistake, but rarely explains how it reached that conclusion. The algorithm fails, and the burden of proof falls on whoever has the fewest resources to defend themselves. Opacity with a modern interface.

The geopolitical dimension of this problem tends to get left out of the domestic conversation, and that's a mistake. Geopolitics shapes digital governance in ways individual citizens rarely perceive directly. When a government adopts technological infrastructure from a foreign provider, it's not just buying software. It's transferring informational sovereignty. Its citizens' data travels, gets processed, and is stored under jurisdictions answerable to other laws, other interests, and other pressures. Cybersecurity stops being a technical matter and becomes one of national sovereignty.

That's not speculation. It's the actual architecture of how global digital infrastructure works. Researchers have spent decades documenting how data flows cross borders in ways traditional trade treaties never anticipated. The Q-Day that Google projects for twenty twenty-nine, when quantum computing could break current encryption, isn't just a technical problem. It's the culmination of years of decisions about data governance made without consulting anyone outside technical and corporate circles.

Documented alternatives do exist. Not utopias—experiments that actually work. Iceland built island.is, an open-source digital public services platform used by three hundred seventy thousand inhabitants with levels of institutional trust that most countries would consider impossible. The key wasn't the technology. It was the political decision to make visible what is normally hidden: how the system works, who has access to which data, under what conditions, and with what citizen oversight. Transparency by design, not by accident.

The same trend appears in the digital cooperative movements that have emerged in Nigeria, India, and Vietnam. These are responses to the same question that citizens everywhere are starting to ask: why does the data we generate through our daily lives mostly benefit those who built the infrastructure to capture it? Digital cooperatives propose an answer that's simple in theory and complex in practice: that data governance should be communal, not corporate or exclusively state-run.

Tools like Openplanter or open-source intelligence platforms like SitDeck point in that same direction from another angle. They democratize access to information that was once exclusive to well-resourced actors. They partially reverse the informational asymmetry. They don't solve the structural problem, but they demonstrate that opacity isn't inevitable, and that accountability can flow in multiple directions—not just from citizen to State.

The historical problem of opacity in information systems isn't new. Bureaucracies have protected their archives for as long as archives have existed. What changes in the digital context is scale and speed. A State that in the twentieth century took weeks to consolidate information about a citizen can now do it in seconds. That acceleration wasn't accompanied by an equivalent acceleration in citizen oversight mechanisms. That's where the central imbalance lies.

Anti-corruption agencies, transparency bodies, data protection laws: all these instruments exist in many Latin American countries. The problem usually isn't the absence of rules. It's the gap between what the rules say and what the systems actually do in practice. A Mexican citizen can, in theory, request access to the data the government holds on them. In practice, the process is complicated enough to discourage most people. That friction isn't accidental.

The hardest question isn't the diagnosis, which is already fairly well documented, but under what conditions data governance can become genuinely democratic. Not in the sense that everyone votes on every technical decision, but in the sense that systems are intelligible, auditable, and contestable by those affected by them. There's an enormous difference between a system that generates automated decisions and one that generates automated decisions that can be explained. The second condition isn't more expensive. It's a design choice.

How that model scales in countries with weak institutions and limited resources remains an open question. Iceland is a notable case precisely because it's small and relatively homogeneous. What works with three hundred seventy thousand inhabitants under conditions of high institutional trust doesn't automatically transfer to Mexico, Brazil, or Colombia. Digital cooperatives in emerging economies might serve as a bridge, but they're local adaptations, not direct transplants. That's a real limitation of the models we cite as references.

The complexity of the problem doesn't justify resignation in the face of opacity. It justifies exactly the opposite: greater demand for clarity about how the systems that govern us actually work, greater investment in citizen oversight mechanisms, and greater political willingness to accept that governing with data is not the same as governing well. Data is an input. Governance is a practice, and practices can be changed once there's enough clarity about what's failing and why.

Data governance isn't a technical problem that engineers will solve on their own. It's a question about who has the power to define a society's institutional reality, and that question has always been political. Algorithms aren't neutral. Information systems aren't neutral. Nor are the decisions about what data to collect, how to process it, who audits it, and what happens when it's used to make decisions that affect real lives.

Stones don't lie, but historians sometimes do.

Sources:

1. Fenoglietto, Lucas Roberto. "El algoritmo del Estado: gobernar datos para gobernar mejor." Mercojuris. https://mercojuris.com/el-algoritmo-del-estado-gobernar-datos-para-gobernar-mejor-lucas-roberto-fenoglietto/

2. Laurent, Yves. "Q-Day 2029: The Encryption Protecting Your Data Has Already Expired." 2026.

3. Laurent, Yves. "Iceland: The Open Source Government That Works." 2026.

4. Laurent, Yves. "SAT 2026: Automated Tax AI and Fines for Deductions." 2026.

5. Lay, Mariana. "Geopolítica, Gobernanza y Ciberseguridad." Prezi. https://prezi.com/p/gcjynviz_n5q/geopolitica-gobernanza-y-ciberseguridad/