Over the last ten years, scaling up has been a major driver of the digital economy. The collection of data has been intensive, the storage of data centralized, and the reuse of data extensive. While this generated growth, it also brought along vulnerabilities in the form of privacy breaches, regulatory challenges, opaque AI systems, and rising trust costs. By 2025, the market will be quietly making a shift from merely accumulating raw data to valuing only verifiable, compliant, and trustworthy data.
This isn't just ideology. It's driven by new regulations, wider institutional adoption, and the reality that most applications don't need full datasets - they need answers that can be confidently proved.
The tightening of regulations and the movement of real-world assets on-chain means trust can’t be taken for granted—it has to be proven. It’s where concepts such as data sovereignty, privacy-preserving computation, and oracle networks converge to build a new layer of operation for Web3.
Most apps today do not need direct access to raw data. For instance, an app for someone who lends money will not need to access the user’s bank account statements. They will only want to know that the income source exceeds a certain threshold. Another app for an insurance company will want sensor data, but only to confirm that a certain incident has occurred.
With the shift from sharing data to sharing proof using cryptography, the game is completely different. Rather than transferring confidential information, solutions issue a proof statement, or an affidavit, that a particular criterion has been fulfilled without disclosing the details. The obtained proof cannot be traced back to the issuing organization.
Decentralized oracles play an essential role in making the above possible. The role of oracles has gone beyond the provision of price feeds. The current state of oracles involves the acquisition of information from outside sources and the provision of tamper-proof assertions for applications and smart contracts to act on.
In practice, two interaction patterns are particularly relevant: data push models, where producers actively distribute the results of agreed facts useful for monitoring, compliance, and real-time settlements; and data pull models, where the consumers need a particular proof on demand, useful in eligibility checks, risk mitigation strategies, and conditional processes. A layered architecture, in which lighter edge systems handle initial processing and a validation layer performs consensus and attestation, is a key strategy to contain costs while maintaining security.
It is at the intersection of Web3 and the real economy that the most insistent demand is emanating for a trust-first data model. Applications such as tokenized real-world assets need dependable, constantly updated off-chain data on valuations and cash flows. Decentralized infrastructures require location, uptime, and usage signals. Privacy-focused finance and digital identities require selective disclosure rather than heavy documentation. Healthcare and research increasingly depend on federated, consent-driven analytics rather than centralized records.
In all of the above fields, the most essential aspect that is constant is that the data needs to be correct, provable, and applicable without ultimately revealing confidential details.
A team of developers does not need to start everything over when they adopt the architecture. That is, they will normally start off in a couple of pilot projects, in which the data flow constraints will limit growth anyway. They then build a set of necessary conditions, assign rules for consent, obtain inputs via edge systems and partners, and employ oracle networks for the purpose of checking and attesting outcomes. On-chain automation will then come into play only when performance, cost, and compliance criteria have been satisfied.
Of course, there are some challenges. Sensing data can be deceived, and hence the need for multi-source verification and reputation. There may be some delay along the lines of privacy-preserving computation, and hence the need for careful design of the predicates. Additionally, poorly designed incentives can cause centralization, and hence the need for clear governance. Currently, the challenges can be measured and managed.
The future of the next version of the Web3 will be measured less by the amount of data it is capable of handling and more by the amount of responsible and legitimate actions it’s able to take on real-world data. Data sovereignty and oracles are now realities rather than ideas.
In today’s environment that’s lacking trust, systems able to show they “know” things without sharing too much information are likely to dictate the future of digital platforms.
